• Review article
  • Open access
  • Published: 10 February 2020

Predicting academic success in higher education: literature review and best practices

  • Eyman Alyahyan 1 &
  • Dilek Düştegör   ORCID: orcid.org/0000-0003-2980-1314 2  

International Journal of Educational Technology in Higher Education volume  17 , Article number:  3 ( 2020 ) Cite this article

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Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success , through which student attributes to focus on , up to which machine learning method is more appropriate to the given problem . This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.

Introduction

Computers have become ubiquitous, especially in the last three decades, and are significantly widespread. This has led to the collection of vast volumes of heterogeneous data, which can be utilized for discovering unknown patterns and trends (Han et al., 2011 ), as well as hidden relationships (Sumathi & Sivanandam, 2006 ), using data mining techniques and tools (Fayyad & Stolorz, 1997 ). The analysis methods of data mining can be roughly categorized as: 1) classical statistics methods (e.g. regression analysis, discriminant analysis, and cluster analysis) (Hand, 1998 ), 2) artificial intelligence (Zawacki-Richter, Marín, Bond, & Gouverneur, 2019 ) (e.g. genetic algorithms, neural computing, and fuzzy logic), and 3) machine learning (e.g. neural networks, symbolic learning, and swarm optimization) (Kononenko & Kukar, 2007 ). The latter consists of a combination of advanced statistical methods and AI heuristics. These techniques can benefit various fields through different objectives, such as extracting patterns, predicting behavior, or describing trends. A standard data mining process starts by integrating raw data – from different data sources – which is cleaned to remove noise, duplicated or inconsistent data. After that, the cleaned data is transformed into a concise format that can be understood by data mining tools, through filtering and aggregation techniques. Then, the analysis step identifies the existing interesting patterns, which can be displayed for a better visualization (Han et al., 2011 ) (Fig.  1 ).

figure 1

standard data mining process (Han et al. 2011 )

Recently data mining has been applied to various fields like healthcare (Kavakiotis et al., 2017 ), business (Massaro, Maritati, & Galiano, 2018 ), and also education (Adekitan, 2018 ). Indeed, the development of educational database management systems created a large number of educational databases, which enabled the application of data mining to extract useful information from this data. This led to the emergence of Education Data Mining (EDM) (Calvet Liñán & Juan Pérez, 2015 ; Dutt, Ismail, & Herawan, 2017 ) as an independent research field. Nowadays, EDM plays a significant role in discovering patterns of knowledge about educational phenomena and the learning process (Anoopkumar & Rahman, 2016 ), including understanding performance (Baker, 2009 ). Especially, data mining has been used for predicting a variety of crucial educational outcomes, like performance (Xing, 2019 ), retention (Parker, Hogan, Eastabrook, Oke, & Wood, 2006 ), success (Martins, Miguéis, Fonseca, & Alves, 2019 ; Richard-Eaglin, 2017 ), satisfaction (Alqurashi, 2019 ), achievement (Willems, Coertjens, Tambuyzer, & Donche, 2018 ), and dropout rate (Pérez, Castellanos, & Correal, 2018 ).

The process of EDM (see Fig.  2 ) is an iterative knowledge discovery process that consists of hypothesis formulation, testing, and refinement (Moscoso-Zea et al., 2016 ; Sarala & Krishnaiah, 2015 ). Despite many publications, including case studies, on educational data mining, it is still difficult for educators – especially if they are a novice to the field of data mining – to effectively apply these techniques to their specific academic problems. Every step described in Fig. 2 necessitates several decisions and set-up of parameters, which directly affect the quality of the obtained result.

figure 2

Knowledge discovery process in educational institutions (Moscoso-Zea, Andres-Sampedro, & Lujan-Mora, 2016 )

This study aims to fill the described gap, by providing a complete guideline, providing easier access to data mining techniques and enabling all the potential of their application to the field of education. In this study, we specifically focus on the problem of predicting the academic success of students in higher education. For this, the state-of-the-art has been compiled into a systematic process, where all related decisions and parameters are comprehensively covered and explained along with arguments.

In the following, first, section 2 clarifies what is academic success and how it has been defined and measured in various studies with a focus on the factors that can be used for predicting academic success. Then, section 3 presents the methodology adopted for the literature review. Section 4 reviews data mining techniques used in predicting students’ academic success, and compares their predictive accuracy based on various case studies. Section 5 concludes the review, with a recapitulation of the whole process. Finally, section 6 concludes this paper and outlines the future work.

Academic success definition

Student success is a crucial component of higher education institutions because it is considered as an essential criterion for assessing the quality of educational institutions (National Commission for Academic Accreditation &amp, 2015 ). There are several definitions of student success in the literature. In (Kuh, Kinzie, Buckley, Bridges, & Hayek, 2006 ), a definition of student success is synthesized from the literature as “Student success is defined as academic achievement, engagement in educationally purposeful activities, satisfaction, acquisition of desired knowledge, skills and competencies, persistence, attainment of educational outcomes, and post-college performance”. While this is a multi-dimensional definition, authors in (York, Gibson, & Rankin, 2015 ) gave an amended definition concentrating on the most important six components, that is to say “Academic achievement, satisfaction, acquisition of skills and competencies, persistence, attainment of learning objectives, and career success” (Fig.  3 ).

figure 3

Defining academic success and its measurements (York et al., 2015 )

Despite reports calling for more detailed views of the term, the bulk of published researchers measure academic success narrowly as academic achievement. Academic achievement itself is mainly based on Grade Point Average (GPA), or Cumulative Grade Point Average (CGPA) (Parker, Summerfeldt, Hogan, & Majeski, 2004 ), which are grade systems used in universities to assign an assessment scale for students’ academic performance (Choi, 2005 ), or grades (Bunce & Hutchinson, 2009 ). The academic success has also been defined related to students’ persistence, also called academic resilience (Finn & Rock, 1997 ), which in turn is also mainly measured through the grades and GPA, measures of evaluations by far the most widely available in institutions.

Review methodology

Early prediction of students’ performance can help decision makers to provide the needed actions at the right moment, and to plan the appropriate training in order to improve the student’s success rate. Several studies have been published in using data mining methods to predict students’ academic success. One can observe several levels targeted:

Degree level: predicting students’ success at the time of obtention of the degree.

Year level: predicting students’ success by the end of the year.

Course level: predicting students’ success in a specific course.

Exam level: predicting students’ success in an exam for a specific course.

In this study, the literature related to the exam level is excluded as the outcome of a single exam does not necessarily imply a negative outcome.

In terms of coverage, section 4 and 5 only covers articles published within the last 5 years. This restriction was necessary to scale down the search space, due to the popularity of EDM. The literature was searched from Science Direct, ProQuest, IEEE Xplore, Springer Link, EBSCO, JSTOR, and Google Scholar databases, using academic success , academic achievement , student success , educational data mining , data mining techniques , data mining process and predicting students’ academic performance as keywords. While we acknowledge that there may be articles not included in this review, seventeen key articles about data mining techniques that were reviewed in sections 4 and 5 .

Influential factors in predicting academic success

One important decision related to the prediction of students’ academic success in higher education is to clearly define what is academic success. After that, one can think about the potential influential factors, which are dictating the data that needs to be collected and mined.

While a broad variety of factors have been investigated in the literature with respect to their impact on the prediction of students’ academic success (Fig.  4 ), we focus here on prior-academic achievement , student demographics , e-learning activity , psychological attributes , and environments , as our investigation revealed that they are the most commonly reported factors (summarized in Table  1 ). As a matter of fact, the top 2 factors, namely, prior-academic achievement , and student demographics , were presented in 69% of the research papers. This observation is aligned with the results of The previous literature review which emphasized that the grades of internal assessment and CGPA are the most common factors used to predict student performance in EDM (Shahiri, Husain, & Rashid, 2015 ). With more than 40%, prior academic achievement is the most important factor. This is basically the historical baggage of students. It is commonly identified as grades (or any other academic performance indicators) that students obtained in the past (pre-university data, and university-data). The pre-university data includes high school results that help understand the consistency in students’ performance (Anuradha & Velmurugan, 2015 ; Asif et al., 2015 ; Asif et al., 2017 ; Garg, 2018 ; Mesarić & Šebalj, 2016 ; Mohamed & Waguih, 2017 ; Singh & Kaur, 2016 ). They also provide insight into their interest in different topics (i.e., courses grade (Asif et al., 2015 ; Asif et al., 2017 ; Oshodi et al., 2018 ; Singh & Kaur, 2016 )). Additionally, this can also include pre-admission data which is the university entrance test results (Ahmad et al., 2015 ; Mesarić & Šebalj, 2016 ; Oshodi et al., 2018 ). The university-data consists of grades already obtained by the students since entering the university, including semesters GPA or CGPA (Ahmad et al., 2015 ; Almarabeh, 2017 ; Hamoud et al., 2018 ; Mueen et al., 2016 ; Singh & Kaur, 2016 ), courses marks (Al-barrak & Al-razgan, 2016 ; Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Asif et al., 2015 ; Asif et al., 2017 ; Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Mueen et al., 2016 ; Singh & Kaur, 2016 ; Sivasakthi, 2017 ) and course assessment grades (e.g. assignment (Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Mueen et al., 2016 ; Yassein et al., 2017 ); quizzes (Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Mohamed & Waguih, 2017 ; Yassein et al., 2017 ); lab-work (Almarabeh, 2017 ; Mueen et al., 2016 ; Yassein et al., 2017 ); and attendance (Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Garg, 2018 ; Mueen et al., 2016 ; Putpuek et al., 2018 ; Yassein et al., 2017 )).

figure 4

a broad variety of factors potentially impacting the prediction of students’ academic success

Students’ demographic is a topic of divergence in the literature. Several studies indicated its impact on students’ success, for example, gender (Ahmad et al., 2015 ; Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Garg, 2018 ; Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Putpuek et al., 2018 ; Sivasakthi, 2017 ), age (Ahmad et al., 2015 ; Hamoud et al., 2018 ; Mueen et al., 2016 ), race/ethnicity (Ahmad et al., 2015 ), socioeconomic status (Ahmad et al., 2015 ; Anuradha & Velmurugan, 2015 ; Garg, 2018 ; Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Mueen et al., 2016 ; Putpuek et al., 2018 ), and father’s and mother’s background (Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Singh & Kaur, 2016 ) have been shown to be important. Yet, few studies also reported just the opposite, for gender in particular (Almarabeh, 2017 ; Garg, 2018 ).

Some attributes related to the student’s environment were found to be impactful information such as program type (Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ), class type (Mueen et al., 2016 ; Sivasakthi, 2017 ) and semester period (Mesarić & Šebalj, 2016 ).

Among the reviewed papers, also many researchers used Student E-learning Activity information, such as a number of login times, number of discussion board entries, number / total time material viewed (Hamoud et al., 2018 ), as influential attributes and their impact, though minor, were reported.

The psychological attributes are determined as the interests and personal behavior of the student; several studies have shown them to be impactful on students’ academic success. To be more precise, student interest (Hamoud et al., 2018 ), the behavior towards study (Hamoud et al., 2018 ; Mueen et al., 2016 ), stress and anxiety (Hamoud et al., 2018 ; Putpuek et al., 2018 ), self-regulation and time of preoccupation (Garg, 2018 ; Hamoud et al., 2018 ), and motivation (Mueen et al., 2016 ), were found to influence success.

Data mining techniques for prediction of students’ academic success

The design of a prediction model using data mining techniques requires the instantiation of many characteristics, like the type of the model to build, or methods and techniques to apply (Witten, Frank, Hall, & Pal, 2016 ). This section defines these attributes, provide some of their instances, and reveal the statistics of their occurrence among the reviewed papers grouped by the target variable in the student success prediction, that is to say, degree level, year level, and course level.

Degree level

Several case studies have been published, seeking prediction of academic success at the degree level. One can observe two main approaches in term of the model to build: classification where CGPA that is targeted is a category as multi class problem such as (a letter grade (Adekitan & Salau, 2019 ; Asif et al., 2015 ; Asif et al., 2017 ) or overall rating (Al-barrak & Al-razgan, 2016 ; Putpuek et al., 2018 )) or binary class problem such as (pass/fail (Hamoud et al., 2018 ; Oshodi et al., 2018 )). As for the other approach, it is the regression where the numerical value of CGPA is predicted (Asif et al., 2017 ). We can also observe a broad variety in terms of the department students belongs to, from architecture (Oshodi et al., 2018 ), to education (Putpuek et al., 2018 ), with a majority in technical fields (Adekitan & Salau, 2019 ; Al-barrak & Al-razgan, 2016 ; Asif et al., 2015 ; Hamoud et al., 2018 ). An interesting finding is related to predictors: studies that included university-data, especially grades from first 2 years of the program, yielded better performance than studies that included only demographics (Putpuek et al., 2018 ), or only pre-university data (Oshodi et al., 2018 ). Details regarding the algorithm used, the sample size, the best accuracy and corresponding method, as well as the software environment that was used are all in Table  2 .

Less case studies have been reported, seeking prediction of academic success at the year level. Yet, the observations regarding these studies are very similar to the one related to degree level (reported in previous section). Similar to previous sub-section, studies that included only social conditions and pre-university data gave the worse accuracy (Singh & Kaur, 2016 ), while including university-data improved results (Anuradha & Velmurugan, 2015 ). Nevertheless, it is interesting to note that even the best accuracy in (Anuradha & Velmurugan, 2015 ) is inferior to the accuracy in (Adekitan & Salau, 2019 ; Asif et al., 2015 ; Asif et al., 2017 ) reported in previous section. This can be explained by the fact that in (Anuradha & Velmurugan, 2015 ), only 1 year of past university-data is included while in (Asif et al., 2015 ; Asif et al., 2017 ), 2 years of past university-data and in (Adekitan & Salau, 2019 ) 3 years of past university-data is covered. Other details for these methods are in Table  3 .

Course level

Finally, some studies can be reported, seeking the prediction of academic success at the course level. As already mentioned in degree level and year level sections, the comparative work gives accuracies of 62% to 89% while predicting success at a course level can give accuracies more than 89%, which can be seen as a more straightforward task than predicting success at degree level or year level. The best accuracy is obtained in course level with 93%. In (Garg, 2018 ), the target course was an advanced programming course while the influential factor was a previous programming course, also a prerequisite course. This demonstrates how important it is to have a field knowledge and use this knowledge to guide the decisions in the process and target important features. All other details for these methods are in Table  4 .

Data mining process model for student success prediction

This section compiles as a set of guidelines the various steps to take while using educational data mining techniques for student success prediction; all decisions needed to be taken at various stages of the process are explained, along with a shortlist of best practices collected from the literature. The proposed framework (Fig.  5 ) has been derived from well-known processes (Ahmad et al., 2015 ; Huang, 2011 ; Pittman, 2008 ). It consists of six main stages: 1) data collection, 2) data initial preparation, 3) statistical analysis, 4) data preprocessing, 5) data mining implementation, and 6) result evaluation. These stages are detailed in the next subsections.

figure 5

Stages of the EDM framework

Data collection

In educational data mining, the needed information can be extracted from multiple sources. As indicated in Table 1 , the most influential factor observed in the literature is Prior Academic Achievement. Related data, that is to say, pre-university or university-data, can easily be retrieved from the university Student Information System (SIS) that are so widely used nowadays. SIS can also provide some student demographics (e.g. age, gender, ethnicity), but socio-economic status might not be available explicitly. In that case, this could either be deduced from existing data, or it might be directly acquired from students through surveys. Similarly, students’ environment related information also can be extracted from the SIS, while psychological data would probably need the student to fill a survey. Finally, students’ e-learning activities can be obtained from e-learning system logs (Table  5 ).

Initial preparation of data

In its original form, the data (also called raw data) is usually not ready for analysis and modeling. Data sets that are mostly obtained from merging tables in the various systems cited in Table 5 might contain missing data, inconsistent data, incorrect data, miscoded data, and duplicate data. This is why the raw data needs to go through an initial preparation (Fig.  6 ), consisting of 1) selection, 2) cleaning, and 3) derivation of new variables. This is a vital step, and usually the most time consuming (CrowdFlower, 2016 ).

figure 6

Initial Preparation of Data

Data selection

The dimension of the data gathered can be significant, especially while using prior academic achievements (e.g. if all past courses are included both from high-school and completed undergraduate years). This can negatively impact the computational complexity. Furthermore, including all the gathered data in the analysis can yield below optimal prediction results, especially in case of data redundancy, or data dependency. Thus, it is crucial to determine which attributes are important, or needs to be included in the analysis. This requires a good understanding of the data mining goals as well as the data itself (Pyle, Editor, & Cerra, 1999 ). Data selection, also called “Dimensionality Reduction” (Liu & Motoda, 1998 ), consists in vertical (attributes/variables) selection and horizontal (instance/records) selection (García, Luengo, & Herrera, 2015 ; Nisbet, Elder, & Miner, 2009 ; Pérez et al., 2015 ) (Table  6 ). Also, it is worth noticing that models obtained from a reduced number of features will be easier to understand (Pyle et al., 1999 ).

Data cleaning

Data sources tend to be inconsistent, contain noises, and usually suffer from missing values (Linoff & Berry, 2011 ). When a value is not stored for a variable, it is considered as missing data. When a value is in an abnormal distance from the other values in the dataset, it is called an outlier. Literature reveals that missing values and outliers are very common in the field of EDM. Thus, it is important to know how to handle them without compromising the quality of the prediction. All things considered, dealing with missing values or outliers cannot be done by a general procedure, and several methods need to be considered within the context of the problem. Nevertheless, we try to here to summarize the main approaches observed in the literature and Table  7 provides a succinct summary of them.

If not treated, missing value becomes a problem for some classifiers. For example, Support Vector Machines (SVMs), Neural Networks (NN), Naive Bayes, and Logistic Regression require full observation (Pelckmans, De Brabanter, Suykens, & De Moor, 2005 ; Salman & Vomlel, 2017 ; Schumacker, 2012 ), however, decision trees and random forests can handle missing data (Aleryani, Wang, De, & Iglesia, 2018 ). There are two strategies to deal with missing values. The first one is a listwise deletion, and it consists in deleting either the record (row deletion, when missing values are few) or the attribute/variable (column deletion, when missing values are too many). The second strategy, imputation, that derives the missing value from the remainder of the data (e.g. median, mean, a constant value for numerical value, or randomly selected value from missing values distribution (McCarthy, McCarthy, Ceccucci, & Halawi, 2019 ; Nisbet et al., 2009 )).

Outliers data are also known as anomalies, can easily be identified by visual means, creating a histogram, stem and leaf plots or box plots and looking for very high or very low values. Once identified, outliers can be removed from the modeling data. Another possibility is to converts the numeric variable to a categorical variable (i.e. bin the data) or leaves the outliers in the data (McCarthy et al., 2019 ).

Derivation of new variables

New variables can be derived from existing variables by combining them (Nisbet et al., 2009 ). When done based on domain knowledge, this can improve the data mining system (Feelders, Daniels, & Holsheimer, 2000 ). For example, GPA is a common variable that can be obtained from SIS system. If taken as it is, a student’s GPA reflects his/her average in a given semester. However, this does not explicitly say anything about this student’s trend over several semesters. For the same GPA, one student could be in a steady state, going through an increasing trend, or experiencing a drastic performance drop. Thus, calculating the difference in GPA between consecutive semesters will add an extra information. While there is no systematic method for deriving new variables, Table  8 recapitulates the instances that we observed in the EDM literature dedicated to success prediction.

Statistical analysis

Preliminary statistical analysis, especially through visualization, allows to better understand the data before moving to more sophisticated data mining tasks and algorithms (McCarthy et al., 2019 ). Table  9 summarizes the statistics commonly derived depending on the data type. Data mining tools contain descriptive statistical capabilities. Dedicated tools like STATISTICA (Jascaniene, Nowak, Kostrzewa-Nowak, & Kolbowicz, 2013 ) and SPSS (L. A. D. of S. University of California and F. Foundation for Open Access Statistics, 2004 ) can also provide tremendous insight.

It is important to note that this step can especially help planning further steps in DM process, including data pre-processing to identify the outliers, determining the patterns of missing data, study the distribution of each variable and identify the relationship between independent variables and the target variable (see Table  10 ). Furthermore, statistical analysis is used in the interpreting stage to explain the results of the DM model (Pyle et al., 1999 ).

Data preprocessing

The last step before the analysis of the data and modeling is preprocessing, which consists of 1) data transformation, 2) how to handle imbalanced data sets, and 3) feature selection (Fig.  7 ).

figure 7

Data Preprocessing

Data transformation

Data transformation is a necessary process to eliminate dissimilarities in the dataset, thus it becomes more appropriate for data mining (Osborne, 2002 ). In EDM for success prediction, we can observe the following operations:

Normalization of numeric attributes: this is a scaling technique used when the data includes varying scales, and the used data mining algorithm cannot provide a clear assumptions of the data distribution (Patro & Sahu, 2015 ). We can cite K-nearest neighbors and artificial neural networks (How to Normalize and Standardize Your Machine Learning Data in Weka, n.d. ) as examples of such algorithms. Normalizing the data may improve the accuracy and the efficiency of the mining algorithms, and provide better results (Shalabi & Al-Kasasbeh, 2006 ). The common normalization techniques are min-max (MM), decimal scaling, Z-score (ZS), median and MAD, double sigmoid (DS), tanh, and bi-weight normalizations (Kabir, Ahmad, & Swamy, 2015 ).

Discretization: The simplest method of discretization binning (García et al., 2015 ), converts a continuous numeric variable into a series of categories by creating a finite number of bins and assigning a specific number of values to each attribute in each bin. Discretization is a necessary step when using DM techniques that allow only for categorical variables (Liu, Hussain, Tan, & Dash, 2002 ; Maimon & Rokach, 2005 ) such as C4.5 (Quinlan, 2014 ), Apriori (Agrawal, 2005 ) and Naïve Bayes (Flores, Gámez, Martínez, & Puerta, 2011 ). Discretization also increases the accuracy of the models by overcoming noisy data, and by identifying outliers’ values. Finally, discrete features are easier to understand, handle, and explain.

Convert to numeric variables: Most DM algorithms offer better results using a numeric variable. Therefore, data needs to be converted into numerical variables, using any of these methods:

Encode labels using a value between [0 and N (class-1)34 ] where N is the number of labels (Why One-Hot Encode Data in Machine Learning, n.d. ).

A dummy variable is a binary variable denoted as (0 or 1) to represent one level of a categorical variable, where (1) reflects the presence of level and (0) reflects the absence of level. One dummy variable will be created for each present level (Mayhew & Simonoff, 2015 ).

Combining levels: this allows reducing the number of levels in categorical variables and improving model performance. This is done by simply combining similar levels into alike groups through domain (Simple Methods to deal with Categorical Variables in Predictive Modeling, n.d. ).

However, note that all these methods do not necessarily lead to improved results. Therefore, it is important to repeat the modeling process by trying different preprocessing scenarios, evaluate the performance of the model, and identify the best results. Table  11 . recapitulates the various EDM application of preprocessing methods.

Imbalanced datasets

It is common in EDM applications that the dataset is imbalanced, meaning that the number of samples from one class is significantly less than the samples from other classes (e.g. number of failing students vs passing students) (El-Sayed, Mahmood, Meguid, & Hefny, 2015 ; Qazi & Raza, 2012 ). This lack of balance may negatively impact the performance of data mining algorithms (Chotmongkol & Jitpimolmard, 1993 ; Khoshgoftaar, Golawala, & Van Hulse, 2007 ; Maheshwari, Jain, & Jadon, 2017 ; Qazi & Raza, 2012 ). Re-sampling (under or over-sampling) is the solution of choice (Chotmongkol & Jitpimolmard, 1993 ; Kaur & Gosain, 2018 ; Maheshwari et al., 2017 ). Under-sampling consists in removing instances from the major class, either randomly or by some techniques to balance the classes. Oversampling consists of increasing the number of instances in the minor class, either by randomly duplicating some samples, or by synthetically generating samples (Chawla, Bowyer, Hall, & Kegelmeyer, 2002 ) (see Table  12 ).

Feature selection

When the data set is prepared and ready for modeling, then the important variables can be chosen and submitted to the modeling algorithm. This step, called feature selection, is an important strategy to be followed to mining the data (Liu & Motoda, 1998 ). Feature selection aims to choose a subset of attributes from the input data with the capability of giving an efficient description for the input data while reducing effects from unrelated variables while preserving sufficient prediction results (Guyon & Elisseeff, 2003 ). Feature selection enables reduced computation time, improved prediction performance while allowing a better understanding of the data (Chandrashekar & Sahin, 2014 ). Feature selection methods are classified into filter and wrapper methods (Kohavi & John, 1997 ). Filter methods work as preprocessing to rank the features, so high-ranking features are identified and applied to the predictor. In wrapper methods, the criterion for selecting the feature is the performance of the forecasting device, meaning that the predictor is wrapped on a search algorithm which will find a subset that gives the highest predictor performance. Moreover, there are embedded methods (Blum & Langley, 1997 ; Guyon & Elisseeff, 2003 ; P. (Institute for the S. of L. and E. Langley, 1994 ) which include variable selection as part of the training process without the need for splitting the data into training and testing sets. However, most data mining tools contains embedded feature selection methods making it easy to try them and chose the best one.

Data mining implementation

Data mining models.

Two types of data mining models are commonly used in EDM applications for success prediction: predictive and descriptive (Kantardzic, 2003 ). Predictive models apply supervised learning functions to provide estimation for expected values of dependent variables according to the features of relevant independent variables (Bramer, 2016 ). Descriptive models are used to produce patterns that describe the fundamental structure, relations, and interconnectedness of the mined data by applying unsupervised learning functions on it (Peng, Kou, Shi, & Chen, 2008 ). Typical examples of predictive models are classification (Umadevi & Marseline, 2017 ) and regression (Bragança, Portela, & Santos, 2018 ), while clustering (Dutt et al., 2017 ) and association (Zhang, Niu, Li, & Zhang, 2018 ), produce descriptive models. As stated in section 4 , classification is the most used method, followed by regression and clustering. The most commonly used classification techniques are Bayesian networks, neural networks, decision trees (Romero & Ventura, 2010 ). Common regression techniques are linear regression and logistic regression analysis (Siguenza-Guzman, Saquicela, Avila-Ordóñez, Vandewalle, & Cattrysse, 2015 ). Clustering uses techniques like neural networks, K-means algorithms, fuzzy clustering and discrimination analysis (Dutt et al., 2017 ). Table  13 shows the recurrence of specific algorithms based on the literature review that we performed.

In the process, first one needs to choose a model, namely predictive or descriptive. Then, the algorithms to build the models are chosen from the 10 techniques considered as the top 10 in DM in terms of performance, always prefer models that are interpretable and understandable such as DT and linear models (Wu et al., 2008 ). Once the algorithms have been chosen, they require to be configured before they are applied. The user must provide suitable values for the parameters in advance in order to obtain good results for the models. There are various strategies to tune parameters for EDM algorithms, used to find the most useful performing parameters. The trial and error approach is one of the simplest and easiest methods for non-expert users (Ruano, Ribes, Sin, Seco, & Ferrer, 2010 ). It consists of performing numerous experiments by modifying the parameters’ values until finding the most beneficial performing parameters.

Data mining tools

Data mining has a stack of open source tools such as machine learning tools which supports the researcher in analyzing the dataset using several algorithms. Such tools are vastly used for predictive analysis, visualization, and statistical modeling. WEKA is the most used tool for predictive modeling (Jayaprakash, 2018 ). This can be explained by its many pre-built tools for data pre-processing, classification, association rules, regression, and visualization, as well as its user-friendliness, and accessibility even to a novice in programming or data mining. But we can also cite RapidMiner and Clementine as stated in Table 4 .

Results evaluation

As several models are usually built, it is important to evaluate them and select the most appropriate. While evaluating the performance of classification algorithms, normally the confusion matrix as shown in Table  14 is used. This table gathers four important metrics related to a given success prediction model:

True Positive (TP): number of successful students classified correctly as “successful”.

False Positive (FP): number of successful students incorrectly classified as “non-successful”.

True Negative (TN): number of did not successful students classified correctly as “non-successful”.

False Negative (FN): number of did not successful students classified incorrectly as “successful”.

Different performance measures are included to evaluate the model of each classifier, almost all measures of performance are based on the confusion matrix and the numbers in it. To produce more accurate results, these measures are evaluated together. In this research, we’ll focus on the measures used in the classification problems. The measures commonly used in the literature are provided in Table  15 .

Early student performance prediction can help universities to provide timely actions, like planning for appropriate training to improve students’ success rate. Exploring educational data can certainly help in achieving the desired educational goals. By applying EDM techniques, it is possible to develop prediction models to improve student success. However, using data mining techniques can be daunting and challenging for non-technical persons. Despite the many dedicated software’s, this is still not a straightforward process, involving many decisions. This study presents a clear set of guidelines to follow for using EDM for success prediction. The study was limited to undergraduate level, however the same principles can be easily adapted to graduate level. It has been prepared for those people who are novice in data mining, machine learning or artificial intelligence.

A variety of factors have been investigated in the literature related to its impact on predicting students ‘academic success which was measured as academic achievement, as our investigation showed that prior-academic achievement, student demographics, e-learning activity, psychological attributes, are the most common factors reported. In terms of prediction techniques, many algorithms have been applied to predict student success under the classification technique.

Moreover, a six stages framework is proposed, and each stage is presented in detail. While technical background is kept to a minimum, as this not the scope of this study, all possible design and implementation decisions are covered, along with best practices compiled from the relevant literature.

It is an important implication of this review that educators and non-proficient users are encouraged to applied EDM techniques for undergraduate students from any discipline (e.g. social sciences). While reported findings are based on the literature (e.g. potential definition of academic success, features to measure it, important factors), any available additional data can easily be included in the analysis, including faculty data (e.g. competence, criteria of recruitment, academic qualifications) may be to discover new determinants.

Availability of data and materials

Not applicable.

Abbreviations

(Probabilistic) neural network

Classification

  • Data mining

Decision tree

Educational data mining

K-nearest neighbors

Logistic regression

Naive Bayes

Neural network

Random forest

Rule induction

Random tree

Tree ensemble

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Alyahyan, E., Düştegör, D. Predicting academic success in higher education: literature review and best practices. Int J Educ Technol High Educ 17 , 3 (2020). https://doi.org/10.1186/s41239-020-0177-7

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Socioeconomic Inequality and Educational Outcomes pp 7–17 Cite as

A Review of the Literature on Socioeconomic Status and Educational Achievement

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Part of the IEA Research for Education book series (IEAR,volume 5)

The foundations of socioeconomic inequities and the educational outcomes of efforts to reduce gaps in socioeconomic status are of great interest to researchers around the world, and narrowing the achievement gap is a common goal for most education systems. This review of the literature focuses on socioeconomic status (SES) and its related constructs, the association between SES and educational achievement, and differences among educational systems, together with changes over time. Commonly-used proxy variables for SES in education research are identified and evaluated, as are the relevant components collected in IEA’s Trends in International Mathematics and Science Study (TIMSS). Although the literature always presents a positive association between family SES and student achievement, the magnitude of this relationship is contingent on varying social contexts and education systems. TIMSS data can be used to assess the magnitude of such relationships across countries and explore them over time. Finally, the literature review focuses on two systematic and fundamental macro-level features: the extent of homogeneity between schools, and the degree of centralization of education standards and norms in a society.

  • Centralization versus decentralization
  • Educational inequality
  • Forms of capital
  • Homogeneity versus heterogeneity
  • International large-scale assessment
  • Student achievement
  • Socioeconomic status
  • Trends in International Mathematics and Science Study (TIMSS)

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Educational inequality occurs in multiple forms. Van de Wefhorst and Mijs ( 2010 ) discussed its existence through the inequality of educational opportunity in terms of the influence of social background on students’ test scores, as well as in learning, as expressed by the performance distribution in test scores. According to the authors, these two characteristics of inequality are conceptually different in that an educational system may have equality in terms of dispersion (or variance) in educational achievement but inequality in terms of opportunities; yet, in general, societies that are equal in terms of dispersion are also more equal in terms of opportunities.

Different education systems take part in each cycle of TIMSS, but 25 education systems took part in the grade eight mathematics student assessment in both 1995 and 2015. For these 25 participating systems, the average mathematics achievement score increased by only five score points between 1995 and 2015 (Mullis et al. 2016 ). Focusing only on more recent trends, for the 32 education systems that participated in the grade eight mathematics student assessment in both 2011 and 2015, there was a gain of nine scale score points between 2011 and 2015, suggesting that many of the education systems with the largest gains are those starting from a low base. As there is limited information on family and home background and its relationship with TIMSS international achievement, this spread in achievement is not sufficient to explain why education systems perform differently. Therefore, our study focuses on the other aspect of educational inequality, namely how SES background is related to educational achievement. In the next two sections of this chapter, we review the concept and measurement of socioeconomic status, and the literature regarding the relationship between family SES and student academic achievement. The rest of this chapter focuses on differences between the various education systems and changes in educational inequality over time.

2.1 Socioeconomic Status and Related Constructs and Measures

The American Psychological Association (APA) defines socioeconomic status as “the social standing or class of an individual or group” (APA 2018 ). SES has been commonly used as a latent construct for measuring family background (Bofah and Hannula 2017 ). However, among empirical studies, there is no consensus on how to best operationalize the concept. In many studies, the measurement of SES does not receive much attention, with very limited discussion over why certain indicators were used rather than others (Bornstein and Bradley 2014 ). Liberatos et al. ( 1988 ) argued that there was no one best measure, because the choice of the SES measure depended on the conceptual relevance, the possible role of social class in the study, the applicability of the measure to the specific populations being studied, the relevance of a measure at the time of study, the reliability and validity of the measure, the number of indicators included, the level of measurement, the simplicity of the measure, and comparability with measures used in other studies.

Historically, SES has been conceptualized and measured in various ways. Taussig ( 1920 ) conceptualized SES as the occupational status of the father. Later, Cuff ( 1934 ) adopted a score card proposed by Sims ( 1927 ) as a measure of SES; this included questions about items possessed by the home, parents’ education, father’s occupation, and other relevant information. Moving on from these early studies, development of instruments for measuring SES has become more complicated, including more advanced methods such as factor analysis or model-based approaches (NCES [National Center for Educational Statistics] 2012 ). By the 1980s, one general agreement had emerged: SES should be a composite variable, typically measuring education, income, and occupation, since these three indicators reflect different aspects of family background (Brese and Mirazchiyski 2013 ).

However, collecting this information is known to be challenging. Besides privacy concerns, there are also concerns about information accuracy (Keeves and Saha 1992 ). For example, the National Assessment of Educational Progress (NAEP) in the United States does not collect family income or parental occupation directly from students, as many of them are unable to accurately report such data (Musu-Gillette 2016 ). Similarly, TIMSS decided not to include questions about parental occupation and income because of doubts about the reliability and utility of similar information collected by previous IEA surveys (Buchmann 2002 ). Therefore, the grade eight student questionnaires for TIMSS include only three proxy components for SES: parental education, books at home, and home possessions (such as ownership of a calculator, computer, study desk, or dictionary), with some evolution in the home possession items over time owing to rapid advancements in technology over the 20 years of TIMSS (more recent items include the internet, or computer tablet, for example).

The abstract nature of the concept of SES leaves some room for researchers to decide what proxy variables to use as SES measures. Yang ( 2003 ), for example, found that the possession of a set of household items may be used as SES indicators. Despite variability and limitations in the measurement of SES, its association with student performance has been demonstrated in numerous studies (Sirin 2005 ).

2.2 Family SES and Student Achievement

Theoretical and empirical work has emphasized that family SES has an impact on children’s educational outcomes, examined mechanisms through which family SES is related to children’s achievement, and identified potential pathways behind this relationship, one of which uses three forms of capital: economic, cultural, and social capital (Bourdieu 1986 ; Coleman 1988 , 1990 ). In other words, differences in the availability of these forms of capital Footnote 1 across households eventually lead to disparities in children’s academic achievement (Buchmann 2002 ).

Bourdieu ( 1986 ) posited that capital can present itself in three fundamental forms and that economic capital is the source of all other forms of capital. The other types of capital are treated as transformed and disguised forms of economic capital. Economic capital can be used in pursuit of other forms of capital; for example, family income can be used to pay for organized after-school activities, to access elite educational opportunities, or to build up valuable social networks (Lareau 2011 ). Children from disadvantaged backgrounds are constrained by the financial resources they and their family possess (Crosnoe and Cooper 2010 ). As such, economic capital determines the extent to which parents can offer financial support to children’s academic pursuits.

In addition to economic capital, cultural capital, namely knowledge of cultural symbols and ability to decode cultural messages, helps parents transmit their advantages to children and to reproduce social class (Bourdieu 1986 ). According to Bourdieu ( 1986 ), an individual’s cultural capital can exist in an embodied state as well as in an objectified state. In the embodied state, cultural capital focuses on “physical capital,” where the body itself is a marker of social class, as particular embodied properties exist as a consequence of specific class practices (Tittenbrun 2016 ). Through this state, inequality in socioeconomic class can find expression in embodied ways, such as physical appearance, body language, diet, pronunciation, and handwriting. In the objectified state, inequality is expressed in forms of cultural goods, such as accessibility to pictures, books, dictionaries, and machines. Therefore, in this view, Bourdieu sees the body and cultural goods as forms of currency that result in the unequal accumulation of material resources and, by extension, represent an important contributor to class inequality (Perks 2012 ).

Children from higher social classes also have advantages in gaining educational credentials due to their families. Cultural capital is considered an important factor for school success. Yang ( 2003 ) suggested possession of cultural resources had the most significant impact on students’ mathematics and science achievement in most countries. If cultural resources are differentiated according to family background, and if some cultural resources have more value than others in the education system, it is reasonable to assume that differential achievement is related to an individual’s social class (Barone 2006 ). For example, a student’s social ability and language style, as well as attitudes toward the school curriculum and teachers, may differ according to social class origins (Barone 2006 ). As such, parental school choice in some countries favors children from those families that already possess dominant cultural advantages (i.e., children attending private schools in the United States), thus confirming the cultural inequalities between classes and status groups of families to produce educational inequalities among their children (Shavit and Blossfeld 1993 ). Lareau ( 1987 , 2011 ) further posited that middle-class parents have a different parenting style, which she termed concerted cultivation, fostering their child’s talent through organized activities, while working-class parents tend to have a natural growth parenting style, letting their children create their own activities with more unstructured time. Consequently, middle-class families prepare their children better for school since their parenting style is more valued and rewarded by the school system.

Finally, the possession of social capital reflects the resources contained in social relations, which can be invested with expected benefits (Bourdieu 1986 ). Differences in educational success can be attributed to different levels of existing social capital, which is produced in networks and connections of families that the school serves (Rogošić and Baranović 2016 ). Coleman ( 1988 ) developed a conceptual framework of social capital in which social structure can create social capital, through family, school, and community. The relationships between the family and the community may be used to explain the higher educational achievements of students based on expected achievements with respect to their socioeconomic status (Mikiewicz et al. 2011 ).

In summary, while the overall association between family SES and students’ academic achievement is well documented in theoretical and empirical work, the magnitude of the relationship between family SES and achievement differs across countries. This may be related to differences in education systems and jurisdictions, and societal changes over time.

2.3 Differences in Education Systems and Changes Over Time

In any society, there are two systematic and fundamental macro-level features that highlight the differences in education systems and how they have changed over time. First, is the extent of homogeneity among education systems. Second, is the degree of centralization of education standards and norms in a society. The association between family background and children’s achievement depends on the education system and the social context (i.e., the level of homogeneity and centralization). Where educational inequality is prominent, students from different backgrounds may demonstrate larger achievement gaps.

2.3.1 Homogeneous Versus Heterogeneous

Previous research has shown that students at lower levels of SES perform better in education systems with lower levels of inequality than their counterparts in countries with more significant SES differences (Ornstein 2010 ). That is, some education systems are more homogeneous than others, with schools being more similar to each other in terms of funding. As an example, Finnish households have a narrow distribution of economic and social status at the population level and their schools show little variation in terms of funding (Mostafa 2011 ).

Furthermore, Mostafa ( 2011 ) found that school homogeneity on a large scale is a source of equality since it diminishes the impact of school characteristics on performance scores. Finland is often seen as an example of a homogeneous education system with high levels of similarity between schools, which in turn reduces the impact of school variables on performance scores (Kell and Kell 2010 ; Mostafa 2011 ). More specifically, Montt ( 2011 ) examined more than 50 school systems, including Finland, in the 2006 cycle of PISA and found that greater homogeneity in teacher quality decreased variability in opportunities to learn within school systems, potentially mitigating educational inequality in achievement.

By contrast, Hong Kong has a relatively high-income disparity compared to other societies (Hong Kong Economy 2010 ). However, the relationship between socioeconomic status and mathematics achievement was found to be the lowest among the education systems participating in the 2012 cycle of PISA (Ho 2010 ; Kalaycıoğlu 2015 ). This suggests that, despite diversity in their SES background, most students from Hong Kong access and benefit from the education system equally. Hong Kong’s high performance in reading, mathematics, and science also suggests the average basic education is of high quality (Ho 2010 ).

However, in many other countries with heterogeneous education systems, educational inequality has manifested itself primarily through the stratification of schools on the basis of socioeconomic composition, resource allocation, or locale. For example, unlike schooling in many other countries, public schooling policies in the United States are highly localized. Local property taxes partially finance public schools, school assignments for students depend on their local residence, and neighborhoods are often divided by racial and socioeconomic background (Echenique et al. 2006 ; Iceland and Wilkes 2006 ). Cheema and Galluzzo ( 2013 ) confirmed the persistence of gender, racial, and socioeconomic gaps in mathematics achievement in the United States using PISA data from its 2003 cycle. Inequalities in children’s academic outcomes in the United States are substantial, as children begin school on unequal terms and differences accumulate as they get older (Lareau 2011 ; Lee and Burkam 2002 ).

In Lithuania, there has also been a growing awareness that an ineffectively organized or poorly functioning system of formal youth education increases the social and economic divide and the social exclusion of certain groups (Gudynas 2003 ). To ensure the accessibility and quality of educational services in Lithuania, special attention has traditionally been paid to a student’s residential location. Gudynas ( 2003 ) suggested that the achievement of pupils in rural schools in Lithuania was lower than that of pupils in urban schools, with the difference being largely explained by the level of parental education in rural areas, which was on average lower than that of urban parents. Similarly, in New Zealand, residential location is considered to be a barrier to educational equality. Kennedy ( 2015 ) observed that students residing in rural residential areas on average tend to have lower SES than those in urban areas, and receive a considerably shorter education than their counterparts living in urban centers, thereby promoting SES disparities in access to education.

In the Russian Federation, Kliucharev and Kofanova ( 2005 ) noted that the inequality between well-off and low-income individuals regarding access to education has been increasing since the turn of the century. According to Kosaretsky et al. ( 2016 ), the greatest inequality in educational access in the Russian Federation was observed in the 1990s, where the rising number of educational inequalities was largely determined by the accelerating socioeconomic stratification of the population, as well as significant budget cuts to education. Although the state articulated policies aiming for universal equality of educational opportunities, they argued that the policies were not implemented with the required financial and organizational support. As a result, in the immediate post-Soviet era, the Russian Federation has observed increasing educational inequality and some loss of achievement compared to the Soviet period.

A final example is Hungary. Horn et al. ( 2006 ) noted that OECD’s PISA studies in the early 2000s highlighted the need for the Hungarian school system to improve both in effectiveness and equality. They contended that achievement gaps among schools make the Hungarian education system one of the most unequal among the participating countries in the PISA 2000 and 2003 cycles. The variation in performance between schools in Hungary is alarmingly large, about twice the OECD average between-school variance (OECD 2004 ). By contrast, the within-school variance is less pronounced, suggesting that students tend to be grouped in schools with others sharing similar characteristics. In other words, students’ achievement gaps seemingly mirror the differences in socioeconomic backgrounds of students across different schools (OECD 2001 , 2004 ). In recent years, persistent education performance gaps with regard to socioeconomic background of students have been observed in Hungary, with 23% of the variation in students’ mathematics performance being explained by differences in their SES background, well above the average of 15% for OECD countries (OECD 2015 ).

2.3.2 Centralized Versus Decentralized

In addition to differences in homogeneity, education systems can be classified as centralized or decentralized. A centralized education system is one that would have centralized education funding (e.g., at the national level) across the education system with little local autonomy, while in decentralized education systems, municipalities oversee school funding for both public and private schools (Böhlmark and Lindahl 2008 ; Oppedisano and Turati 2015 ). Centralization generally leads to the standardization of curriculum, instruction, and central examinations in an education system, and can be helpful in reducing inequalities since it mitigates the influence of a student’s family background (Van de Wefhorst and Mijs 2010 ). By contrast, high levels of decentralization can create greater disparities between schools, especially when the level of funding is determined by the local context (Mostafa 2011 ).

Sweden is an example of a decentralized education system that was centralized until the implementation of wide-reaching reforms in the early 1990s (Hansen et al. 2011 ). The previously centralized Swedish school system has been thoroughly transformed into a highly decentralized and deregulated one, with a growing number of independent schools and parental autonomy in school choice (Björklund et al. 2005 ). Concurrently, examining multi-level effects of SES on reading achievement using data from IEA’s Reading Literacy Study from 1991 and PIRLS data from 1991 to 2001, the SES effect appears to have increased in Sweden over time, with between-school differences being greater in 2001 than in 1991, suggesting school SES has a strong effect (Hansen et al. 2011 ).

Similarly, there has also been growing debate about educational inequality in the Republic of Korea in recent years. By analyzing grade eight TIMSS data from the 1999, 2003, and 2007 cycles of the assessment, Byun and Kim ( 2010 ) found the contribution of SES background on student achievement had increased over time. They suspected the higher educational inequality might be related to various factors, including a widening income gap and recent educational reforms geared toward school choice, as well as increased streaming by academic ability and curriculum differentiation created by a decentralized education system.

Researchers have found evidence to support the view that decentralized education systems in developed countries perform better than centralized systems in terms of reducing students’ achievement inequality (see, e.g., Rodríguez-Pose and Ezcurra 2010 ). Conversely, Causa and Chapuis ( 2009 ) used PISA data for the OECD countries to confirm that decentralized school systems were positively associated with equity in educational achievement. Furthermore, according to PISA 2000 and 2006, in European countries inequality in educational outcomes has apparently declined in decentralized school systems, while it has concomitantly increased in centralized systems (Oppedisano and Turati 2015 ).

Mullis et al. ( 2016 ) argued that efficiency and equality can work together. They found that many countries have improved their TIMSS national averages while also reducing the achievement gap between low- and high-performing students. Similarly, an analysis using TIMSS scores from 1999 and 2007 discovered a prominent inverse relation between the within-country dispersion of scores and the average TIMSS performance by country (Freeman et al. 2010 ; Mullis et al. 2016 ). The pursuit of educational equality does not have to be attained at the expense of equity and efficiency.

In conclusion, the positive association between family background and children’s achievement is universal. However, the magnitude of such associations depend on the social context and education system. In other words, the achievement gap between students from different backgrounds is more pronounced in education systems where overall inequality (e.g., income inequality) is strong. Narrowing the achievement gap is a common goal for most education systems. But it is well understood that stagnant scores for low-SES students and declines in the scores of high-SES students should not be seen as an avenue for enhancing equality. Rather, education systems should strive for equality by improving the performance of all students while focusing on improving the achievement of low-SES students at a faster rate to reduce gaps in achievement (Mullis et al. 2016 ). In recognition of this, our study not only focuses on how inequalities in educational outcomes relate to socioeconomic status over time for select participating education systems in TIMSS but also tracks the performance of low-SES* Footnote 2 students separately. In order to make a comparable trend analysis, we first constructed a consistent measure of family SES* based on a modified version of the TIMSS HER. Chapter 3 describes the data and methods used in the study and Chap. 4 presents the trends in SES* achievement gaps of the 13 education systems that participated in three cycles of TIMSS, including the 1995 and 2015 cycles.

Note that family socioeconomic status is clearly related to Bourdieu’s theory of capital in the empirical world. Conceptually, however, they do not equate with each other.

The SES measure used in this study is a modified version of the TIMSS home educational resources (HER) index and does not represent the full SES construct, as usually defined by parental education, family income, and parental occupation. In this report, we therefore term our measure SES* to denote the conceptual difference (Please refer to Chap. 1 for more details).

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  • Published: 02 October 2018

How does family background affect children’s educational achievement? Evidence from Contemporary China

  • Zhonglu Li 1 &
  • Zeqi Qiu 2  

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Education is a lasting process. Academic performance in primary education plays a crucial role in obtaining further educational opportunities. Thus, it is necessary to examine how family background affects children’s academic achievement at an early stage. Through analysis of data from the Chinese Family Panel Study in 2010(CFPS2010), this paper proposes two pathways through which family influences children’s academic performance. Firstly, parents compete for high-quality educational opportunities for their children and better educational opportunities lead to better academic performance. Secondly, parenting behavior and educational support for their children could cultivate children’s learning habits and affect academic performance. We also find urban students’ academic performance are more heavily affected by their families’ socioeconomic status compared with rural students. These findings bear important implications for how to reduce the class difference in students’ academic performance and promote educational equity in contemporary China.

Introduction

Education is the basic mechanism for enhancing the population quality of a nation, and education during childhood is the foundation for the formation of human labor-force quality. Childhood education not only affects the achievement and happiness at the individual level, but also shapes the labor force quality and capacity of innovation (Heckman 2011 ) to determine the potentiality of the development of a nation. With the spread of enforcement of compulsory education and the expansion of schools across China, the average schooling years of Chinese citizens has been improved significantly. In spite of this, due to the scarcity of educational resources and its unequal distribution, various conditions of education inequality has yet to be addressed and improved (Yang 2006 ). As a response, the national Council executive meeting of 2010 has passed the National Mid-and-long Term Education Development and Reform Plan , targeting “enhance educational equality, develop equal education opportunities that benefits the whole population”, which is listed among the most significant strategic development goals of the nation.

On the one hand, educational (in) equality may be rooted in institutional arrangement, i.e., its role of smoothing or even hampering the effect of family with different social economic status on educational opportunities. On the other hand, educational (in) equality is shaped by the different opportunities and capacities that families have in participation in education. Therefore, the relationship between family background and educational achievement has become a critical indicator in evaluating educational (in) equality. Past studies showed that since the Open and Reform of China, family social economic status has become increasingly important in determining personal education achievement, which has not been dampened with the expansion of schools (Deng and Treiman 1997 ; Zhou et al. 1998 ; Li 2003 , 2010 : Li 2006 ;Liu 2008 ;Wu 2009 ;Wu 2013a ; Li 2016 ).

Existing research has mostly focused on the impact of family background on the eventual education attainment, especially the attainment of higher education, but it is worth noticing that education attainment is a continuous process in which the education achievement of the prior stage affects the later-stage achievement both cumulatively and probabilistically. Without access to high-qualified primary school and middle school education, one barely has much chance to proceed to higher education. The continuous and accumulative nature of education means that the competition for educational opportunities of individuals initiates ever since the primary school and middle school stages. Therefore, without a thorough analysis of the educational processes, it is difficult to fully understand the mechanisms of how family background affects children’s educational opportunities and academic achievement. Moreover, there will be straight-forward policy implications to explore the relationship between academic achievement and family background from the starting point—the phase of compulsory education.

The goal of compulsory education is to ensure the equality at the starting point of one’s education, and its compulsory and equal nature should in theory guarantee that the impacts of family background on the children’s school entering to be the lowest. However, because of the scarcity of high school and college opportunities in current education system of China, academic achievement has remained the primary standard of educational selection. So in fact, the education attainment of individuals is highly related to their academic achievement of each stage. Therefore, the equality of compulsory education should not only be reflected on its equal opportunities of school entering, but also its independence of family background.

In fact, not only that the school quality may affect students’ academic achievement during compulsory education, but also the ways and abilities of the participation of parents in their children’s compulsory education may directly affect students’ academic achievement. Distinctive from past research which focuses on the effect of family background on the final education attainment, this paper concerns through what mechanisms and paths that family background affects the children’s academic achievement during the compulsory education period.

Literature review

Families affect children’s learning behaviors and academic achievement in important ways, as they are the primary and most significant environments that the children are exposed to. Coleman’s report (1966) shows that families may play even more important roles in student’s academic achievement than schools and communities. Since then, the line of empirical research on family background and children’s achievement has found that the family social economic statuses may affect children’s academic achievements more than the impact of schools (Coleman et al. 1966 ; Peaker 1971 ; White 1980 ;Sirin 2005 ; Cheadle 2008 ). The Coleman’s hypothesis has been supported by some research and fieldworks based on some Chinese provinces and cities too. For instance, Fang and Feng ( 2008 ) found that the family’s social economic status affects children’s academic scores significantly using the survey data of the middle school students of Nanjing. Sun et al. ( 2009 ) found a significant positive effect of the parent’s income and educational levels on the academic achievement of primary school students based on a Longitudinal Survey of Families and Children in Gansu province.

Studies have explored the mechanisms of families affecting children’s academic achievement based on the study of Coleman, from the human capital theory, cultural capital theory and social capital theory and so on. The human capital theory claims that education is an important human capital investment, where the “cost-benefit” framework is the primary principles for families to make educational investment decision, and the difference in children’s educational achievement is mainly caused by the difference of family educational investment. Because of the limitation of family resources, parents of poor families usually are not able to invest sufficiently in their children’s education, which affects their children’s academic achievement (Becker 1964 ). Gross ( 1993 ) showed that students’ cognitive skills are positively related to their parents’ socioeconomic status.

The cultural capital theory stresses that family cultural resources and environment determine children’s educational aspirations and performances. Compared to families with insufficient cultural capital, parents with rich cultural capital are more aware of the rules of schools, invest more cultural resources, pay more attention to cultivate the children’s educational aspiration and interest, help children with school curriculum, and enable them to perform in academics outstandingly (Bourdieu and Passeron 1990 ). Sewell and Hauser (1993) showed that parents’ educational expectations have significant effects on junior students’ academic performances. Social capital theory emphasizes the participation of parents in education and children’s learning behaviors and achievement; parents with higher social economic status usually participate in their children’s learning activities more intensively, pay more attention to communication with teachers, manage the children’s school absence and other risky behaviors, and improve the children’ academic performance (Coleman 1988 ). Empirical studies showed that parental educational participation, such as discussing school things with children, checking their homework, and participating in school activities, could improve children’s academic performances (Pong et al. 2005 ).

Due to the heterogeneity of allocation of educational resources across rural and urban areas, districts, and schools, when talking about the relationship between family background and educational achievement of children in China, scholars also regard the school quality as an important factor. The outstanding teaching resources and peers that concentrate in key schools have important impacts on the accessibility of educational opportunities of the next stage for children. Families with higher social economic status can make use of their advantages to gain access to better education opportunities for their children, to enhance their possibilities of obtaining higher education (Li 2006 ; Liu 2008 ; Zhao and Hong 2012 ). Research shows that the parental social economic status can affect their children’s schooling quality significantly. The higher the social economic status of a family, the better schools their children attend (Wen 2006 ; Chen and Fang 2007 ; Li 2008 ; Wu 2013b ).

In spite of the different theoretical perspectives, most research pays attention to the paths and mechanisms of how the social economic status of a family affects the children’s academic achievements. Among these, human capital theory stresses the role of the economic resources of family and educational investment in children’s education, cultural capital and social capital theory pays more attention to the role of parent’s educational level and participation on children’s academic performance, and the perspective of school quality argues that the social economic status of a family affects children’s academic performance and chance of continuing schooling through affecting school qualities.

Actually, the impacts of any type of factors cannot exist independently. All family economic resources, family environment and school qualities are important. The issue is that all of them are exogenous factors which only take effect through students’ behaviors, i.e., through children’s academic achievements.

Analysis framework and research hypothesis

Based on existing studies, this article aims to explore the mechanisms and paths of the impact of family socio-economic status on the children’s academic achievement at the microlevel. Through the organizing of existing literature, combined with the situation of education in China, the following analysis framework (Fig.  1 ) is proposed.

figure 1

Analysis framework

The daily experience shows that the impact of family socio-economic status on children’s academic achievement is not direct, but rather through the following two paths:

First, families with relatively high socio-economic status will strive to secure quality educational opportunities for their children, such as those provided by key schools and markets in the system, which in turn will affect their academic achievements. The key schools, which have excellent teachers and students, not only have a direct impact on their differences in academic achievement, but also affect their learning attitudes and behaviors through teachers and peers, thereby affecting their academic achievement and further educational opportunities.

In addition, the development of the education and private tutor market that are related to primary and secondary education provides alternatives and supplements to formal school education. Families with better economic conditions can purchase additional educational products and services for their children (such as home tutoring and tutoring classes), thus consolidating the influence of family SES on children’s academic achievement.

Second, family socio-economic status affects children’s learning behavior and academic performance by affecting parents’ educational expectations towards children and their educational participation. Parents’ educational expectation and behavioral support for children are, to a certain extent, also affected by their socio-economic status, resources, and ability. There are significant differences in the educational support that families of different resources can provide. Parents’ behavioral support for their children’s education (such as checking homework, discussing school conditions, etc.) can foster the formation of good study habits of children and influence their academic performance (Steinberg et al. 1992 ; Fan and Chen 2001 ; Zhao and Hong 2012 ).

Based on the discussions, this paper proposes four research hypotheses.

Hypothesis 1: Family socioeconomic status has an important impact on the quality of the educational opportunities that children have access to. The higher the family’s socioeconomic status, the higher the qualities of children’s educational opportunities attend.

Hypothesis 1a: Controlling other variables, the higher the family’s socioeconomic status, the higher the quality of children’s school attendance.

Hypothesis 1b: Controlling other variables, the higher the family’s socioeconomic status, the more educational services children receive from the market.

Hypothesis 2: Parents’ participation in their children’s education is affected by their socioeconomic status. The higher the family’s socioeconomic status, the higher the degree of participation in education for their children is.

Hypothesis 3: Parental education participation and the quality of children’s educational opportunities affect children’s learning attitude and behavior.

Hypothesis 3a: Controlling other variables, the better the school quality the children attend, the more active their learning behaviors are.

Hypothesis 3b: Controlling other variables, the more parental education is involved, the more active the children’s learning behaviors are.

Hypothesis 4: Parental participation in children’s education and the quality of children’s educational opportunities affect their academic achievement.

Hypothesis 4a: Controlling other variables, when the level of parental education participation is higher; the children’s academic performance is better.

Hypothesis 4b: Controlling other variables, the better the quality of the school the children attend, the better their academic performance is.

Hypothesis 4c: Controlling other variables, the more educational services children receive in the market, the better their academic performance is.

Data, measurement, and methods

The data in this paper is from the Chinese Family Panel Studies 2010 baseline survey data (CFPS2010). CFPS2010 covered 14,960 households in 25 provinces, municipalities, and autonomous regions in China involving three questionnaire surveys for each household: namely the family questionnaires, adult questionnaires for those aged 16 and above, and the children’s questionnaires for those aged 16 and under. The children’s questionnaire was divided into the part reported by the parents and by the part by children themselves (10–15 years old). The research object of this article is children aged 10–15 years who are having compulsory education and who have filled in self-administered questionnaires. We matched the data obtained from the children’s questionnaire with the data from the family and parent questionnaires while removing samples containing missing variables. Finally, we obtained 2750 cases for analysis in the paper.

Measurement

Family SES is one of the key explanatory variables of this article. The following three indicators were used in the analysis for measurement. The first indicator is the net income of households per capita in 2009; the second is the years of education of the father; the third indicator is the years of education of the mother.

Parental participation in their children’s education is an important mediator of the influences of family SES on the academic achievement of children. In the surveys, four interview questions were engaged to measure the parents’ participation in their children’s education. First, “when the child is learning, will you always cease watching your favorite TV programs?” Second, “have you often discussed what happens in school with the child since the beginning of this school year?” Third, “Do you often ask the child to complete his homework?” Fourth, “Do you often check the child’s homework?”. The measures are ordered from 1 to 5, indicating never, rarely (once per month), occasionally (1–2 times per week), frequent (2–3 times per week), and very often (6–7 times a week). In the multiple regression analysis, we took the average of these measures as the value of parents’ educational participation for analysis.

The quality of the school that children attend has a very important influence on their learning behavior and academic achievement. Four measurements were used to measure the quality of children’s school attendance: first, children’s satisfaction with the school; second, children’s satisfaction with the class advisor; third, children’s satisfaction with the Chinese teacher; and fourth, children’s satisfaction with the Mathematics teacher. The scale of these indicators ranged from 1 to 5. The higher the value means the higher level of the satisfaction. In the multiple regression analysis, we take the average of these four as the value of the school quality. Although the subjective evaluation of children may not fully reflect the quality of the school they attend, it still reflects to a great extent their perception and evaluation of the quality of the school. We look forward to further studies that can make up for the deficiencies in the school’s quality measurement in this paper.

The educational services that children received in the market are measured by the following two indicators: first, whether the children participated in a remedial class in the previous semester, and, second, children’s extracurricular tutoring/tutoring expenditures last year.

The measurement of children’s learning behavior, including their daily learning habits, was surveyed with the following four interview questions. First, “I study very hard”; second, “I concentrate on learning in class”; third, “I only play after completing my homework”; and fourth, “I check it several times to make sure it is correct after finishing my homework.” The measurements of the variable range from 1 to 5, representing very disagree, disagree, neutral, agree, and agree very much respectively.

The measurement of children’s academic achievement involves two types of indicators. First, the parental assessments of language and mathematics scores, which were surveyed with “What do you know about the language/mathematics scores of your child last semester”. It is an ordinal variable ranging from 1 to 4, with 1 poor, 2 medium, 3 good, and 4 excellent. The second category includes the CFPS2010 benchmark scores of children’s words and math ability, with the degree of difficulty adjusted based on the level of children’s education. The scores were standardized according to the province of the child and the grade of enrollment in the analysis.

In studies of the relationship between children’s academic achievement and family background, the ranking of family socio-economic status is usually measured at the national level. It is necessary to pay special attention to the fact that the opportunities of secondary education for children in China are rather regional, and the selection of middle schools from elementary schools, of high schools from middle schools, and of colleges from high schools is implemented based on the regional (county, city, and province) processes gradationally. The access to educational opportunities at a higher level does not depend on the children’s ranking at the national level, but on their relative location within the region. In the same way, their competitors are also not country-level students but the peer group in that specific region.

Therefore, both the influence of family background and the measurement of academic achievement should be relative and regional based. In the multiple regression analysis, we controlled the regional differences in children’s academic achievement and family socioeconomic status by adding provincial dummy variables. In the structural equations, we also standardized measures such as children’s academic achievements, remedial class expenses, and family socioeconomic statuses according to provinces and grades, that is, controlling for the differences in grades and regions in the analysis. For that, the control variables also include gender and ethnicity.

Table  1 reports the sample distribution and descriptive statistics of each of the measured and latent variables. In our sample, urban samples took 38.3%, rural samples 61.7%, boys accounted for 50.6%, and girls 49.4%; 63.7% of children enrolled in primary school and 36.3% enrolled in middle school.

To simultaneously estimate the relationship between observable indicators and latent variables and the relationship within these latent variables themselves, structural equation model is used to estimate the relationship between family background variables and children’s academic achievement. Based on the analysis framework (Fig.  1 ) and research hypotheses of this paper, the structural equation model was set as follows (see Fig.  2 ). For the corresponding relationship between latent variables and measured indicators, please refer to Table  1 .

figure 2

The setting of the structural equation model

First, the socio-economic status of exogenous latent variables has a direct impact on children’s quality of school attendance, education services children receiving on market, parental education participation, and children’s academic behavior, and indirectly affects children’s academic achievement. We set the socio-economic status of the family as the only exogenous variable other than gender, ethnicity, and region. Past research shows that parents’ parenting style, the quality of children’s school, and children’s own educational expectations and learning behaviors are all affected by the socio-economic status of the family extensively.

Second, key schools typically have excellent teachers and students, which not only has a direct impact on children’s academic achievements, but also affects their learning attitudes and behaviors through teachers and peers. We propose that the quality of children’s school and parental education participation can directly affect children’s academic achievement and can also have an indirect effect on children’s academic achievement through the mediator of children’s academic behavior.

Third, there is no direct measure for laten variable children's academic achievement in Fig.  2 . Instead, in the model, it is regarded as a high-level latent variable measured by the children’s benchmark test (Test) and performance ranking (Rank).

Fourth, as it can be arbitrary to assume the correlation between the measurement error terms of the variables which is to be adjusted according to LISREL, it is assumed that the error terms of all endogenous variables are not relevant.

Fifth, the urban-rural differences in the mechanisms of family background affecting children’s academic achievement are examined by comparing the urban sample with the rural sample.

Multiple regression analysis results

Table  2 reports the results from the multiple regression analysis of the children’s words and math benchmark test scores. Model 1, model 2, model 3, model 4, and model 5 respectively control for the urban and rural areas, family socioeconomic status, and parental education participation scores.

In terms of urban-rural differences in children’s academic achievement, model 1 shows that after controlling for variables such as provinces, grades, and ethnicities, urban children’s benchmark scores are 0.755 units higher than those in rural areas. As the mean value of children’s benchmarks is 21.775 and the standard deviation is 7.706, the urban-rural difference in children’s academic achievement accounts for about 0.1 standard deviation. After controlling the household per capita income and years of education of parents, model 2 shows that the difference between children’s benchmark scores in urban and rural areas is statistically insignificant. This shows that the difference between urban and rural areas is largely due to differences in the socio-economic status of the family.

The results of model 2, model 3, model 4, and model 5 consistently show that the family’s socioeconomic status, parental education participation, whether children attend tutorial classes, the quality of children’s school, and the extent of children’s learning efforts all have significant effect on the academic achievement of primary and secondary school students.

The results of the full model (model 5) show that the higher the family’s socioeconomic status, the better children’s academic achievement: for every 1 year of increase in parental education, the child’s benchmark score will increase by 0.118; for every 1% increase in household income, the child’s benchmark test score will increase by 0.26. The higher the parental education participation scores (such as checking homework, discussing school issues, etc.), the better the children’s academic performance achieved. In terms of the impact of educational opportunities on children’s academic achievement, the quality of children’s school attendance, and the educational services provided by the market (whether attended a remedial class) have significant positive effects on academic performance. The more satisfied the child is with the school, the higher the score of the benchmark test. Controlling other variables, the benchmark score of the child who participated in the remedial class is 0.46 higher than children who did not attend the tutoring class.

Table  3 further reports on the influence of family socioeconomic status on parental education participation, whether children attend tutorial classes, the quality of children’s school attendance and children’s learning efforts. Among them, whether the children are on the tutorial class is analyzed with a binary logistic regression approach, and the rest outcomes are analyzed with multiple regression analysis.

Statistics show that urban families and families with higher socioeconomic status place greater emphasis on children’s education participate more in the children’s education, are more likely to purchase education services for their children in the market, and strive for quality educational opportunities. In terms of parents’ participation in education, urban parents’ education participation score is 0.23 higher than that of rural parents. For every 1-year increase in years of education of parents, their educational participation score would increase by 0.050. In terms of educational opportunities, urban children are more likely to participate in extracurricular tutorial classes and attend better-quality schools. The incidence of urban children participating in extracurricular remedial classes was 4.66 (e 1.54 ) times higher than that of rural children, and urban children rated their school 0.049 higher than rural children. The level of education of parents and family per capita income both have significant positive effects on children’s quality of attending school and participation in tutorial classes.

In terms of children’s learning behavior, we found that the higher the family’s socioeconomic status, the lower the enthusiasm children have towards learning. The enthusiasm for learning among urban children is significantly lower than that among rural children. And different from family SES, parental education participation and quality of schooling have significant positive effects on children’s learning behavior. The higher the degree of parental education participation, the more active the children’s learning behaviors are. The better the quality of children’s school is, the higher their enthusiasm for learning. This implies that higher family socioeconomic status cannot directly increase children’s enthusiasm for learning, but must be mediated by parent’s education participation.

Results from the structural equation models

Multiple regression analysis provided preliminary evidence for understanding the influence of family background on children’s academic achievement and various mediator variables. However, multiple regression analysis cannot simultaneously analyze the intrinsic relationship among the independent variables. The assumption that all variables are not biased due to measurement error may not be realistically either. To better deal with measurement errors issues and to further clarify how the family background affects children’s academic achievement, we introduce structural equation analysis.

The goodness of fit of the structural equation model

The evaluation of the goodness of fit of the structural equation model is a prerequisite for explaining the relationship between the measured and the latent variables. In general, χ 2 , χ 2 /df, RMSEA (Residual Error Root Mean Square), GFI (Model Fit Index), and AGFI (Adjusted Model Fit Index) are often used as the main tests of the goodness-of-fit.

χ 2 statistic reflects the differences between the model-estimated covariance matrix E and the sample covariance matrix S. The smaller the χ 2 value is, the better the model fit is. However, the χ 2 value and χ 2 /df value are very easily affected by the sample size. With large sample, a slight difference will make χ 2 and χ 2 /df to yield significant results. GFI and AGFI are traditionally used indicators for evaluating the goodness of fit of structural equations. The closer their values are to 1, the better the model fits. RMSEA not only excludes the influence of sample size, but can also perform statistical tests on the values. Therefore, RMSEA is usually used as the primary indicator for evaluating the merits of the model. The smaller the RMSEA value is, the better the model fits. It is generally accepted that RMSEA less than 0.08 is an acceptable model, less than 0.05 is a better model, and less than 0.01 is considered a perfect model (Markus 2012 ; Kline 2015 ).

Table  4 reports the goodness of fit of implementing the model in the total sample and subsamples. In the hypothetical model (Fig.  2 ), the χ 2 value is 676.5, the degree of freedom is 176, and the χ 2 /df is 3.8, which meets the general evaluation criteria that χ 2 /df is less than 5 in the case of large samples. Besides, the RMSEA is 0.032, with a probability of less than 0.050 being 1, both GFI and AGFI are also closer to 1. According to the results of goodness-of-fit tests with various subsamples, our hypothetical model fits the inherent structure of data quite well.

Table  5 summarizes the relationship between the measured and latent variables. The analysis shows that the factor loading of the measurement index is statistically significant, and the loading of most measurement indexes reaches 0.5. This shows that, overall, the indicators used in the analysis have a high degree of validity, and the latent variables are measured well. It should be noted that in the measurement model, the loading of three measurement indicators is less than 0.5: the loading of children’s mathematics test score is less than 0.5, which indicates that the mathematics test does not reflect the children’s language and math ability well. The loading of parents requiring that their children finishing homework is also less than 0.5, which means that the measurement indicator also does not reflect the parental education participation very well. Although the loading of the log of household per capita income is less than 0.5, but as an exogenous variables, factor loading does not reflect the extent to which the indicator measures the latent variables of family socioeconomic status, but indicate how much the household per capita income can explain the differences in family socioeconomic status. Therefore, it is not a measurement that we focus on. We look forward to further research that can make up for this article’s ambiguity about children’s academic achievement and parental education participation measurement.

Path analysis of family background affecting children’s academic achievement

Figure  3 and Table  6 report the path diagrams and test results of the relationship between the latent variables. Overall, the model specified in this paper explains 1.2% of the difference in quality of schools that children attend, the 33.3% of the difference in children’s access to market education services, 20.3% of the difference in parental education participation, 10.4% of the difference in children’s learning behavior differences, and 34.4% of the difference in children’s academic achievement. The following shows the relationship from the family socioeconomic status to the mediating variables to the children’s academic achievement variables.

figure 3

Path analysis of family social economic status affecting children’s academic achievements

Differences in family socioeconomic status and educational opportunities

The scarcity of quality schooling resources makes the competition to be fierce. From Fig.  3 and Table  6 , it can be seen that the effect coefficient of family socio-economic status on the quality of school children attending is 0.11 standard units, that is, if the family’s socioeconomic status is increased by 1 standard unit, the quality of children’s school would be increased by 0.11 standard units. The research hypothesis 1a in this article (the higher the family socioeconomic status, the higher the quality of the children’s school) is supported by the data. However, family socio-economic status does not explain the quality of children’s schooling to a large extent. The family background only explains the 1.2% difference in the quality of children’s school. This shows that in the compulsory education stage, due to the restriction of the nearest admission principle, the influence of family socio-economic status on children’s quality of attending schools is relatively limited, and the difference in the quality of their schooling may be mainly due to factors other than the family, such as differences between urban and rural areas and regional differences. It should be noted that this may be related to our use of household-based survey data and insufficient measurement of school quality.

Unlike the mechanism for obtaining quality school opportunities, the extracurricular remedial class is an education service provided by the market. Families are free to purchase. The mechanisms affecting their acquisition are mainly the market accessibility and family purchase willingness and ability. The results of the analysis support the hypothesis 1b of this study (the higher the family’s socioeconomic status, the more likely the child receives educational services in the market). From Table 6 , it can be seen that family socio-economic status explained 33.3% of the difference in children’s access to market education services, and its standardized effect coefficient was 0.577.

Family socio-economic status and parental education participation

Although parents in China generally have high educational expectations for their children (Ma 2010 ), parents of different socioeconomic status may provide different behavioral support for their children’s education due to constraints in their own abilities and resources (such as discussing what happens in schools with their children and checking the homework for their children).

Figure  3 and Table  6 show that family socio-economic status explains 20% of the difference in parental support for children’s education, with a standardized coefficient of 0.45. Even though most parents recognize the importance of education, families with different socioeconomic status may create different learning environments (Zhao and Hong 2012 ; Wang and Shi 2014 ). Thus, the hypothesis 2 of this study (the higher the social economic status of the family, the higher the degree of parental participation in the education of the children) is supported by the data.

Family background and children’s learning behavior

The development of children’s learning behaviors and habits cannot be separated from the influence of the imperceptible and enduring influence of parents. The results of the analysis in Table  6 show that family socioeconomic status has a significant negative impact on children’s learning enthusiasm. The higher the family’s socioeconomic status, the lower the enthusiasms for learning the children have. Parental education participation has a significant positive effect on children’s learning behavior. The more parents participate in education, the more active the children’s learning behavior is (hypothesis 3a is supported). Although children’s learning behavior is affected to a certain extent by family background, these variables only explain a small part of children’s learning behavior differences. A reasonable speculation is that children’s learning behavior is more influenced by factors outside the family (schools, communities, peers, etc.).

Differences in educational opportunities and children’s learning behaviors and academic achievements

High-quality schools not only have excellent teachers, but also have a good source of students. The quality of the school children attend not only directly affects children’s academic achievement, but also affects their learning behavior through teachers and peers. From the analysis with results shown in Table  6 , the quality of the children’s school not only has a significant positive effect on their academic achievement (hypothesis 4b that the higher the quality of the child’s school, the better his/her academic performance is supported), but also positively affects their learning behavior (hypothesis 3b the better the quality of the children’s school, the more active is their learning behavior) is supported by the data. The analysis also shows that children’s participation in extracurricular tutoring and tutoring expenses has a significant positive effect on their academic achievement. Research hypothesis 4c (the more education services children receive in the market, the better their academic performance) is supported.

Parental education participation and children’s academic achievement

The results of Table  6 also lend support to Coleman’s argument that parental education participation not only has an indirect effect on children’s academic achievement through affecting children’s learning attitudes and behaviors, but also has a direct impact on children’s academic performance. The higher the degree of parent participation, the better the academic performance of children, and the hypothesis 4a is supported by data. The research of Zhao and Hong ( 2012 ) also showed that parents who have more abundant social network capital can have better communication with teachers and other parents, which indirectly improves children’s academic performance.

The total effect of family background on children’s academic achievement

Table  7 further reports the standardized total effect of various factors on children’s academic achievement so that we can compare their relative importance. It can be seen from Table  7 that family socioeconomic status has the greatest impact on the total effect of children’s academic achievement (the total standardization effect is 0.394), followed by the child’s own learning behavior, followed by parental education participation and children’s school quality, and finally the education services provided by the market (the total standardization effect is 0.103). This shows that even during the stage of compulsory education that appeal to social equity, the family background still has a relatively large impact on children’s academic achievement. At the same time, we can also see that the influence of family socioeconomic status on children’s academic achievement is not simplistic and direct, and there is a large room to improve children’s academic performance through the family and school.

How family background affects vary across urban and rural

Nowadays in China, regional factor (urban or rural) is an important variable affecting education. Not only does the distribution of education resources across urban and rural areas differ tremendously, but urban and rural households also have quite different socioeconomic status, lifestyles, and education patterns. The analysis in Table  2 shows that urban children have significantly better academic performance than rural children. With the structural equation model, we further compare the paths of the effect of family background across urban and rural areas.

Table  8 reports the path coefficients among the various latent variables and the explanatory power of the structural equation model. In general, there are three differences in ways that family background influences the academic achievement of rural students and urban students. First, the influence of family socioeconomic status on urban students’ achievement is greater than that of rural students. The socioeconomic status of the family explained 20.8% of the difference in academic performance for urban students, and 6.4% of the difference in the academic performance of rural students. Footnote 1 Second, the family background has significant urban-rural differences on the purchase of education services, and the family socio-economic status explains 29.5% of difference in the purchase of educational services by urban families, and 11.6% of difference in the purchase of education service by rural students. Third, the rural student’s academic achievement is more explained by their own learning behavior; the path coefficient of the learning effort on the academic performance for rural children’s is 0.16 higher than for urban children.

Conclusions and discussion

Children’s education is related to the quality of the future labor force of a country and thus the country’s competitiveness. Most of the existing studies focus on the influence of family background on college education attainment. Actually, the educational attainment of the higher education is affected by the education attainment during their childhood period. In the literature of the relationship between family background and academic performance in middle school (Fang and Feng 2008 ) and high school (Yang 2005 ), the discussion is also limited in the correlation between family background and academic achievement. There is a lack of discussion on the mechanisms of childhood academic achievement, that is, the path through which the family background can affect education attainment during childhood, which needs further examination in the research of education. Therefore, this article tries to explore the mechanisms producing the differences in children’s academic achievement during the compulsory education period and the influence of family background from the starting point.

Based on the empirical analysis of China Family Panel Studies Baseline Data (CFPS2010), the study found that:

First, the family background has a large impact on children’s academic achievement, which is consistent with the conclusions of existing studies. Contrary to the findings of existing research, this study found that factors such as family background, differences in educational opportunities, and children’s learning behavior explained 34.4% of differences in children’s test scores, within which family SES explained 15.5% of the difference. Footnote 2 This shows that, on the one hand, the family background still has a great influence on children’s academic achievement, even in the period of compulsory education that appeals to social justice. It is in this sense that extensive public policy efforts in promoting education equity at the stage of compulsory education are needed. On the other hand, the influence of family socioeconomic status on children’s academic achievement is not simplistic and direct. There is a large room for schools and families to take action in improving children’s academic performance.

Second, differences in educational opportunities and parental education participation are two important paths for families to affect children’s academic achievement. The existing studies separately demonstrate the impact of educational opportunities and parental involvement. However, these two forces act on the children simultaneously. The analysis using the structural equation model shows that although Chinese parents hold relatively high educational expectations for their children, but family socio-economic status still has a greater impact on children’s educational opportunities, no matter via providing quality schooling opportunities or providing market-based educational resources. At the same time, parents with different socio-economic status are also heterogeneous to a great extent in their behavior support for children.

Third, the analysis of this paper also shows that there are significant urban-rural differences in the path and mechanism of the influence of family background: family socioeconomic status has a greater impact on urban student’s academic performance than for rural students. Besides, compared with urban students, the academic achievement of rural students is more dependent on their own learning behavior. In summary, there are two paths of family background affecting children’s academic achievement: First, families use their social and economic resources to compete and purchase quality educational resources (key schools in the state system and educational services in the market) and thus affect children's academic achievement. Second, parents cultivate children’s interest in learning and learning habits through educational participation and behavioral support for their children, thereby affecting children’s academic achievement.

The empirical analysis of these two paths contributes to the existing literature on family background and education for educators. At the same time, it also provides clear implications to help reduce the class differences in children’s academic achievement during the compulsory education period, and thus raise the overall quality of China’s human capital, and promote education fairness. At the family level, family education is very important for children’s academic performance. Parents with lower socioeconomic status can cultivate good learning behavior of children through their own educational participation (such as through care and supervision of their children’s study, and active communication with teachers). This would improve children’s academic performance and reduce the impact of family socioeconomic status on children’s academic achievement and thus reduce the class differences in schooling progression and even in the labor market. At the school level, under a given allocation of educational resources, schools can improve students’ academic achievement through the following two ways: first, enhancing teachers’ knowledge and teaching skills; and second, through communication with parents, creating a positive educational atmosphere in school and at home, enhancing children’s interest in learning, and cultivating good learning habits of children. At the national level, relevant departments shall strive for the success of every school providing compulsory education, improve school facilities, upgrade the quality of teachers, and achieve a balanced allocation of educational resources, thereby reducing the impact of school factors on children's academic performance.

Given the applicability of the data, there are still issues that need attention by future research. First, with cross-sectional data, this study cannot fully capture the causality of certain paths, such as the impact of participating in extracurricular tutoring classes on children’s academic achievement. Second, the measurements in the quality of school children attend and parents’ education participation need further improvement. Third, further test is needed on the interaction between family and school to better explore the effect of families and schools on individual’s education attainment.

This may be explained by the higher heterogeneity in family background and educational opportunities in urban areas compared to the rural counterpart. But this argument needs further data analysis and tests to confirm.

This can be learnt from the proportions of power explanation of each latent variable by the structural equation model and the simplified model in Table  6 .

Abbreviations

Adjusted Goodness of Fit Index (LISREL), like GFI but adjusts for model complexity (like adjusted multiple r -squared), theoretically ranges from 0 (poor fit) to 1 (perfect fit), considered satisfactory when > .90

The baseline of Chinese Family Panel Study in 2010

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Acknowledgements

We would like to thank the Institute of Social Science Survey at Peking University for collecting data for this study.

This study received funding from the Ministry of Education in China Fund of Humanities and Social Sciences for Youth Scholars Project No.17YJC840024.

Availability of data and materials

The data used in this paper is from “China Family Panel Studies” collected by Institute of Social Science Survey, Peking University. It is publicly available, and we were authorized to use CFPS 2010 for this study.

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Li, Z., Qiu, Z. How does family background affect children’s educational achievement? Evidence from Contemporary China. J. Chin. Sociol. 5 , 13 (2018). https://doi.org/10.1186/s40711-018-0083-8

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educational achievement research paper

COVID-19 and education: An emerging K-shaped recovery

Across the nation this fall, school bells once more summoned students back to classrooms that, in some districts, had stood empty for the previous year and a half. According to Burbio, a data service that aggregates school and community calendars, 100 percent of public-school students are attending schools in districts that offer fully in-person instruction this fall compared with 73 percent last spring. Meanwhile, 91 percent of students taking interim assessments through educational-technology company Curriculum Associates this fall did so in school buildings, up from just 64 percent in the spring and 34 percent the previous fall. This research therefore provides the first opportunity to truly understand the impact of the pandemic on learning for all students (see sidebar, “About the research”). 1 Previous assessments could provide accurate data only for students who took them in person, because changing the assessment environment (for example, taking the test at home) has a meaningful impact on results. Students who take tests at home tend to perform better than those who take tests in a classroom. Previous analyses thus looked only at students who had returned to classrooms and did not include those who were fully remote for instruction and testing.

About the research

To gauge the impact of the pandemic on educational achievement, we analyzed four main data sets: Curriculum Associates’ i-Ready assessment data to understand student academic performance before and during the pandemic, Burbio’s K-12 district report to explore disruptions to learning, Burbio’s Elementary and Secondary School Emergency Relief (ESSER) III Spending Tracker to delve into district spending plans, and our proprietary parent survey to understand the perceptions and experi­ences of parents through the pandemic. We supplemented this with a broader review of academic research, publications of state and district assessment results, and other news stories.

We use the term unfinished learning to denote the difference between where students would have been if the pandemic had not occurred and where students actually are today. This reflects the reality that many students did not have the opportunity to learn as much during the past 20 months as they would have done typically. While some students may have actually slipped backward and “lost” learning, most students continued to progress during the pandemic, but at a slower pace than they would have done otherwise.

Curriculum Associates’ i-Ready assess­ments were taken by 6.9 million students in math and 6.1 million students in reading in the fall of 2021. Our analysis was based upon a sample of 3.0 million students in mathematics and 2.7 million students in reading who took the diagnostic assessments in school buildings, selected to meet historical comparison criteria. The math sample covered 50 states and Washington, DC, and the reading sample covered 49 states and Washington, DC. Florida was overweighted across both sam­ples, accounting for 22 percent of the math sample and 30 percent of the reading sample. We analyzed data for grades one through six to be consistent with the spring analysis.

When calculating unfinished learning, it is not possible to determine the exact amount that students fell behind or recovered in each time period (from fall 2020 to spring 2021 to fall 2021). The subset of students in each assessment window was signifi­cantly different, because more students took in-person assessments in 2021 than in 2020. The fall 2020 numbers relied on 0.2 million in-school students, the spring 2021 numbers relied on 1.3 million in-school students, and the fall 2021 numbers rely upon 2.7 million in-school students.

We cannot isolate the impact of a changing subset versus that of actual changes in learning. However, we can say that the fall 2021 numbers are the most accurate reflection of where students are today—being the most recent data and encompassing the largest and most representative set of students.

To calculate the amount of unfinished learning, we compared fall 2021 results with those of students from matched schools from fall in 2017, 2018, and 2019. We then converted the points difference into months of learning by comparing the historical scores from the fall of one grade level with performance in the fall of the next grade level, treating this fall-to-fall variation in historical scores as one “year” of learning. Our analysis assumed each year consisted of ten months of school and a two-month summer vacation. Actual school schedules vary significantly, and i-Ready’s typical growth expectations are based upon 30 weeks of actual instruction from the fall to the spring rather than a comparison of fall-to-fall academic growth.

We further analyzed the differential impact of the pandemic on different student groups. In the Curriculum Associates i-Ready assessment data, majority-Black schools are those in which more than 75 percent of the student population identifies as Black; majority-White schools are those in which more than 75 percent of the student population identifies as White. Across all our analyses, high income is defined as family income of $75,000 or more, while low income is defined as family income of less than $25,000.

Burbio is a data service that aggregates school and community calendars. Burbio’s K-12 district report includes two different periods: spring data (as of May 28, 2021) and fall data (as of November 25, 2021). Burbio audits nearly 5,000 school districts, representing more than 75 per­cent of US student enrollment. 1 For more on Burbio’s methodology, please see “Burbio’s Methodology,” burbio.com.

Burbio’s Elementary and Secondary School Emergency Relief (ESSER) III Spending Tracker tabulates planned spending for more than 1,400 districts in 44 states. We analyzed committed spending for districts that broke down spending into different categories. These districts have committed the funds to particular purposes, but the funds may not have been disbursed and programs may not have commenced. Our analysis does not account for districts that have earmarked funds for categories but not indicated specific dollar amounts.

Finally, we augmented these data sets with proprietary research on the perceptions of parents regarding their children’s learning experiences and needs. McKinsey conducted two surveys: a survey of 16,370 parents between June 1 and June 21, 2021; and a survey of 14,498 parents that took place from October 29 to November 14, 2021. Respondents were parents of children in kindergarten through 12th grade across all 50 states and Washington, DC. We then compared the experiences of students by location, race and ethnicity, and income levels.

Our analysis finds that students remain behind in both math and reading. What’s more, gains made since the spring are uneven. While some students are making up lost ground, others are stagnating. For example, students in majority-Black schools remain five months behind their historical levels in both mathematics and reading, while students in majority-White schools are now just two months behind their historical levels, widening prepandemic achievement gaps. 2 Based on Curriculum Associates i-Ready fall 2021 assessment data. See Understanding student learning: Insights from fall 2021 , Curriculum Associates, November 2021, curriculumassociates.com. This means that, in math, students in majority-Black schools are now 12 months behind their peers in majority-White schools, having started the pandemic nine months behind. Similarly, concerns around student mental health have lessened somewhat since the spring, but they remain higher than before the pandemic.

Furthermore, we aren’t even out of the woods yet. Disruptions to learning are not over, and student attendance rates lag significantly behind prepan­demic levels. While actual closures of whole schools or districts have affected just 9 percent of students, quarantines and other disruptions have affected 17 percent of in-person students. On top of school closures, absenteeism rates have risen, with 2.7 times as many students on a path to be chronically absent from school this year compared with before the pandemic. While absenteeism rates for high-income students are leveling off, rates for low-income students have continued to worsen since the spring, despite the return to in-person school. If historical correlations between chronic absenteeism and high school graduation hold, this could translate into an additional 1.7 million to 3.3 million eighth–12th graders dropping out of school because of the pandemic. 3 The federal definition of chronic absenteeism is a student who misses more than 15 days of school each year. The Utah Education Policy Center’s research brief on chronic absenteeism calculates the overall correlation between one year of chronic absence from eighth to 12th grade and dropping out of school is 0.134. For more, see “Research brief: Chronic absenteeism,” Utah Education Policy Center, July 2012, uepc.utah.edu. Our analysis then examined the differential in chronic absenteeism between fully virtual and fully in-person students to account for virtual students reengaging when in-person education is offered. For students who had stopped attending school, we assumed 50 to 75 percent would not return to learning. This estimation is based partly on the UChicago Consortium on School Research’s on-track indicator as a predictor of high school graduation, which estimates up to 75 percent of high school students who are “off track”—either failing or behind in credits—do not graduate in five years. For more, see Elaine Allensworth and John Q. Easton, “The on-track indicator as a predictor of high school graduation,” UChicago Consortium on School Research, June 2005, consortium.uchicago.edu.

Varying access to support programs could be another reason why some students seem to be bouncing back more quickly than others. More than $200 billion has been allocated by the federal government to K-12 schooling, but just a small portion of that has reached students thus far, and parents report uneven access to benefits. High-income parents are 21 percentage points more likely to report their child has participated in a program to support either academic or mental-health recovery, including tutoring, summer school, after-school programs, and counseling and mentoring. 4 Forty percent of low-income parents and 61 percent of high-income parents indicated that their child participated in at least one of these programs. If current trends persist, students from high-income families could recover unfinished learning by the end of this school year. Historically disadvantaged students, meanwhile, could remain up to a grade level behind their peers. 5 This assumes that from fall 2021 to spring 2022 students catch up twice as many months of unfinished learning as they did between spring 2021 and fall 2021. It also factors in prepandemic achievement gaps between high-income and low-income schools.

As districts plan for further recovery programs, an understanding of which students most need support can help inform decisions and begin to close both preexisting and new opportunity and achievement gaps.

How students are doing today

A few months into this school year, students appear to be performing better academically and feeling better than they were last spring. However, indicators of both academic performance and broader well-being are still well below prepandemic levels.

The amount of unfinished learning varies significantly across schools

Our sample of the Curriculum Associates i-Ready assessment data, which covers nearly three million students across 50 states, 6 Over six million students took the reading and math Curriculum Associates diagnostic assessments in the fall of 2021, but only about three million students met historical comparison sample inclusion criteria and took the assessment in school. suggests students are four months behind in mathematics and three months behind in reading compared with students in matched schools in previous years. This level of unfinished learning is about a month less (that is, better) compared with the spring, suggesting students may be making up some of the academic ground they lost during the pandemic.

However, a deeper look at the data reveals the amount of unfinished learning is far from equitable. Students in majority-Black schools are five months behind where they would otherwise have been, both in math and reading. Students in majority-White schools are now just two months behind historical levels (Exhibit 1).

Inequalities were already baked into the historical levels: even before the pandemic, students in majority-Black schools were nine months behind students in majority-White schools in mathematics in the Curriculum Associates data set. Students in majority-Black schools are now a full 12 months behind those in majority-White schools, widening the preexisting achievement gap between Black and White students by about a third (Exhibit 2).

Within classrooms, teachers face a wider range of student needs than ever

The differences identified in our analysis are likely even greater at the student level. In the United States, the variation among students within a school is typically three times greater than the variation among schools. 7 Programme for International Student Assessment (PISA) 2012 results: Excellence through equity: Giving every student the chance to succeed (volume II) , OECD, 2013, oecd.org; US between-school versus within-school data. This fall, each classroom likely included students with a broad range of experiences during the past year and a half. Where students stand compared to grade-level expectations reveals this disparity. 8 Understanding student learning: Insights from fall 2021 , Curriculum Associates, November 2021, curriculumassociates.com. In math, for example, the share of students at grade level or above has decreased by six percentage points since the pandemic, while the share of students who are two or more grade levels below has increased by nine percentage points. 9 Averages quoted for grades one through six to be consistent with the previous “months of learning” analysis from spring 2021. The exhibit provides data from kindergarten through eighth grade. However, readers should bear in mind that i-Ready is used only for a subset of students in some middle schools. This pattern means that in a math classroom of 30 fourth-grade students, for example, three additional students are now two or more grade levels below (Exhibit 3). This makes an already difficult job tougher, as teachers need to tailor their instruc­tion to an even broader set of student needs.

This is most concerning for early-elementary students in reading and mid- to late-elementary students in math. Students who do not learn to read proficiently by third grade struggle to “read to learn” thereafter and are four times less likely to graduate high school. 10 Double jeopardy: How third grade reading skills and poverty influence high school graduation , Annie E Casey Foundation, January 2012, aecf.org. Similarly, students who do not master middle-grade math concepts such as fractions and whole-number division will likely struggle in more conceptual high school mathematics and are less likely to graduate high school on time. 11 Robert Siegler et al., “Early predictors of high school mathematics achievement,” Psychological Science , 2012, Volume 23, Number 7, pp. 691–7; Robert Balfanz, Liza Herzog, and Douglas J. Mac Iver, “Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: Early identification and effective interventions,” Educational Psychologist , 2007, Volume 42, Number 4, pp. 223–35.

High school students have also fallen behind

We drew on state assessments to understand the pandemic’s impact on older students. A subset of states administered assessments in spring 2021, but many had low participation rates, making the results difficult to compare. Where data are available, the story is sobering. For the 13 states 12 Arizona, Iowa, Louisiana, Massachusetts, Missouri, Mississippi, North Carolina, North Dakota, Oklahoma, South Dakota, Tennessee, West Virginia, and Wyoming. with participation rates higher than 90 percent, the proportion of students meeting proficiency standards dropped by an average of five percentage points in math and three percentage points in English language arts. These declines are similar in magnitude to the reduction in students achieving grade-level proficiency in the Curriculum Associates data set, 13 Per Curriculum Associates data, 17 percent of students in fall 2021 were on or above grade level in mathematics compared with 23 percent of the historically matched population (a decrease of six percentage points). For reading, 28 percent were on or above grade level in fall 2021 as opposed to 31 percent historically (a decrease of three percentage points). These percentage-point declines are similar in magnitude to the percentage-point declines in high school assessment proficiency rates. suggesting the pandemic may be having an equal impact on high school learning. Furthermore, these results likely under­estimate unfinished learning in high school, as the states and students experiencing the longest disruption are not reflected in the assessments.

The pandemic’s impact goes beyond academics

Parents remain concerned about their children’s academic performance, school attendance, and mental health. This level of concern has dropped since the spring but remains higher than before the pandemic. In our survey, for example, 21 percent of parents reported being very or extremely concerned about their child’s mental health before the pandemic. This share spiked to 35 percent in June 2021 and dropped back to 28 percent this November. Parents of Black and Hispanic students have higher levels of concern across all areas (Exhibit 4).

Across the board, the picture a few months into the fall semester looks marginally better, on average, than it did in the spring, but the top-line numbers hide a lot of variability. Upon delving deeper, many students—especially those from historically disadvantaged backgrounds—still need help.

Ongoing disruptions to learning

Before states and districts can fully help students recover from the pandemic, disruptions to learning must end. Burbio reports 1,282 school closures since the start of the 2021–22 school year. 14 Burbio data as of November 25, 2021; includes closures due to school mental-health days. That means 9 percent of public-school students have been affected by a school closure, with the average closure lasting two days. 15 Average closure duration is calculated by the number of closure days weighted by the number of students disrupted. For example, shorter closures that affected more students are given a greater weight than longer closures that affected fewer students. However, in ten states, more than 15 percent of students have endured closures (Exhibit 5).

Our parent survey paints an even less rosy picture (Exhibit 6). Of all students who chose to attend fully in-person learning this fall, 16 Excluding students whose parents said they had chosen to put their child partially or fully into virtual, hybrid, or home-based learning in the past two weeks. Of our total sample, 6 percent of parents chose to homeschool their child, 5 percent selected fully virtual learning, and 19 percent opted for some form of hybrid model. just 83 percent attended ten full days during the two weeks the survey was in the field. 17 Numerator reflects students who opted for and attended in-person learning for all days in the past two weeks that weren’t holiday days, minimum days, or absences due to sickness or other non-COVID-19 disruption reason. Parents of Black and Hispanic students were slightly more likely to report disruptions to their children’s in-person learning experience.

It is not just the duration of disruption that matters; the alternative to in-person learning is also critical. In the case of school closures, Burbio data suggest students received some form of virtual instruction 54 percent of the time when disruptions occurred. In some states, that number was much lower: in Arkansas, Iowa, Maryland, Missouri, Tennessee, and Texas, students received instruction during less than 10 percent of school closures.

Our parent survey also indicates a wide variety of options when in-person school is disrupted. Of all disrupted days (excluding holidays), alternatives provided by districts included synchronous virtual (54 percent), asynchronous virtual (30 percent), and no instruction at all (15 percent). 18 Synchronous virtual includes videoconferencing and direct-teacher instruction, and asynchronous virtual encompasses activities such as independent work packets. Parent satisfaction with the quality of learning options provided also varied significantly. While 75 percent of parents had high or very high confidence in the quality of fully in-person education, that total dropped to 64 percent for parents whose children experienced disruptions to learning.

Reducing the number of disruptions requires an understanding of what is causing them. The Burbio data suggest that confirmed or suspected COVID-19 cases in staff, students, or the local community account for only 12 percent of closure days. 19 Excluding all closures for which no reason was recorded (n = 788 of total 1,282 closures). These percentages have been weighted by student days disrupted to account for closure duration and number of students affected. Pandemic-related stressors are causing most of the disruptions: 50 percent of closure days are the result of primarily single-day breaks that school districts have taken to support student and staff mental health. Staff shortages also make up a significant portion (13 percent) of closure days. For individual students, our parent survey suggests campus closures account for only 45 percent of total missed days, with quarantines making up 12 percent and sickness an additional 6 percent. 20 For parents whose students had signed up for in-person learning (did not choose virtual or hybrid) but missed one or more days of regular scheduled instruction of the past two weeks. Parents of Black and Hispanic students were most likely to report that campus closures were the cause of their students’ interruptions to in-person learning.

Even if school is open, students don’t always attend. Accounts of rising absenteeism have emerged in recent months. Nearly half of Cleveland’s students are on track to be chronically absent this school year. 21 Patrick O’Donnell, “Schools are open, but Cleveland kids keep cutting class: Chronic absenteeism is more than double pre-pandemic levels,” The 74 , October 26, 2021, the74million.org. An analysis of 30 districts in California, representing more than 330,000 students, shows rates of chronic absenteeism have more than doubled since the pandemic started. 22 Data from School Innovations & Achievement suggest that chronic absenteeism from a subset of districts in California rose from 11 percent in 2019 to 18 percent in 2020 to 27 percent in 2021, with the largest increases in the primary grades. See Statewide chronic absenteeism analysis , School Innovations & Achievement, 2021, sia-us.com.

Our parent survey suggests these news stories are not isolated anecdotes. According to respondents, just over half of students attended school every day this fall. Many students are missing multiple days of school. Before the pandemic, about 8 percent of parents reported that their children were chronically absent from school (missing 15 days or more of school during the year 23 There are multiple definitions of chronic absenteeism. In this article, we use the federal definition, which counts a student who has missed 15 days of school for any reason during one school year as chronically absent. Some states, including California, instead categorize students as chronically absent if they miss 10 percent or more of instructional days. ). In the spring, chronic absenteeism more than doubled, to 18 percent. This fall, those numbers increased again, with 22 percent of parents now reporting their child is on track to be chronically absent this school year. 24 Twenty-two percent of parents reported their student had missed four or more days of the 2021–22 school year so far. If these students were to continue missing school at the same rate for the remainder of the school year, they would be on track to miss 15 or more days across the full school year. Put another way, parent reports of chronic absenteeism have increased by a factor of 2.7 since before the pandemic. Low-income students, who often lack access to resources to make up for lost instruction in the classroom and who are more likely to experience ongoing attendance barriers, 25 “Attendance in the early grades: Why it matters for reading,” Attendance Works, February 2014, attendanceworks.org. are 1.6 times more likely to be missing multiple days of school than their high-income peers (Exhibit 7).

Previous research has revealed that parents tend to underestimate their children’s absent days by a factor of two 26 Avi Feller and Todd Rogers, “Reducing student absences at scale by targeting parents’ misbeliefs,” Nature Human Behaviour , April 2018, Volume 2, pp. 335–42, nature.com. The McKinsey Parent Survey, June 2021 (n = 16,370), corroborates Feller and Rogers: the historical US-wide chronic absenteeism rate of 16 percent for grades eight through 12 (per the Department of Education) was two times that of the rates at which parents indicated their student had missed more than 15 days of school per year (“Prior to the pandemic, did your child attend school consistently?”). —suggesting nearly one-third of students across the country may be on track to be chronically absent this school year. Based on historical links between chronic absenteeism and dropout rates, as many as 1.7 million to 3.3 million eighth–12th graders could drop out of school due to the pandemic without coordinated efforts to reengage them in learning. 27 The federal definition of chronic absenteeism is a student who misses more than 15 days of school each year. The Utah Education Policy Center’s research brief on chronic absenteeism calculates the overall correlation between one year of chronic absence from eighth to 12th grade and dropping out of school is 0.134. For more, see “Research brief: Chronic absenteeism,” Utah Education Policy Center, July 2012, uepc.utah.edu. Our analysis then examined the differential in chronic absenteeism between fully virtual and fully in-person students to account for virtual students reengaging when in-person education is offered. For students who had stopped attending school, we assumed 50 to 75 percent would not return to learning. This estimation is based partly on the UChicago Consortium on School Research’s on-track indicator as a predictor of high school graduation, which estimates up to 75 percent of high school students who are “off track”—either failing or behind in credits—do not graduate in five years. For more, see Elaine Allensworth and John Q. Easton, “The on-track indicator as a predictor of high school graduation,” UChicago Consortium on School Research, June 2005, consortium.uchicago.edu.

Efforts to support student recovery

Many school systems around the country are balancing their efforts to continue limiting disruptions while supporting student recovery. The federal government has committed more than $200 billion to K-12 education  during the next three years through the Elementary and Secondary School Emergency Relief (ESSER) and Governor’s Emergency Education Relief (GEER) funds, with most of the support going directly to school districts. 28 The Coronavirus Aid, Relief, and Economic Security (CARES) Act of 2020 allocated $13 billion to ESSER and $3 billion to GEER funds; the Coronavirus Response and Relief Supplemental Appropriations Act (CRRSAA) of 2021 provided $54 billion to ESSER II, $4 billion to GEER II, and Emergency Assistance to Non-Public Schools (EANS); and the American Rescue Plan (ARP) Act of 2021 provided $123 billion to ESSER III, $3 billion to GEER (EANS II), and $10 billion to other education programs. For more, see “CCSSO fact sheet: COVID-19 relief funding for K-12 education,” Council of Chief State School Officers, 2021, learning.ccsso.org. It is still too soon to determine whether this funding is being spent on programs that will help students who need it most, but available data provide an overview of existing district commitments and initial parent experience.

Burbio has collected information from 1,420 districts in 44 states on their plans for ESSER III funding (the largest and most recent tranche). Across those districts, 67 percent of funds have been committed thus far. 29 This means districts have decided (committed) how to use the funds, but it does not necessarily mean the funds have been disbursed or that programs have commenced. The denominator excludes districts that have earmarked funds for categories but not indicated specific dollar amounts. Of that amount, 28 percent is focused on academic recovery, with a further 6 percent on mental-health recovery. Within academic recovery, summer school and after-school programs account for 34 percent of funding, while tutoring makes up just 7 percent. 30 Some tutoring programs may not have been coded by districts specifically as “tutoring” but are using existing staff and learning interventions. While tutoring reflects only 7 percent of all committed funds related to academic recovery activities, 34 percent of the 1,420 districts for which Burbio has gathered data have noted at least some funding will go toward tutoring, although many have not yet disclosed a dollar amount. An analysis of committed funds may underestimate the portion of districts planning different acceleration efforts. The Center on Reinventing Public Education (CRPE) reports that 71 percent of the top 100 districts are planning to extend learning, while 62 percent intend to roll out some form of tutoring, and 45 percent want to expand small-group instruction. 31 Bree Dusseault, “By the numbers — how 100 school systems are (and aren’t) recovering from COVID: Tutoring, extra class time & other learning acceleration strategies,” The 74 , November 21, 2021, the74million.org.

Meanwhile, just over half of parents report their students have participated in some form of academic or mental-health recovery program. The largest number of students attended tutoring, homework help, or test-preparation services, followed by academic after-school programs and mental-health and counseling support. Despite the large amount of funds directed to summer school, a smaller portion of students participated in these programs. High-income students are nearly twice as likely to have participated in several of these recovery programs (Exhibit 8).

Students most commonly access these programs through free-of-charge offerings at their schools, with a mix of community organizations and programs paid for by parents making up the balance. Schools provided more than 60 percent of in-person academic summer school and more than 50 percent of in-person tutoring, after-school, and mentoring programs free of charge. Parents were more likely to pay for mental-health or counseling services, with only about 30 percent receiving these offerings from schools.

Our survey suggests unmet demand for some services (Exhibit 9). Across income and ethnicity groups, parents are most interested in in-person tutoring and in-person after-school programs; summer school was at the bottom of the list. In every category, in-person programs are more sought after than virtual ones. Low-income parents are much more likely than high-income parents to say they are not interested in any programs: 33 percent versus 20 percent.

A comparison of the two data sets highlights a possible mismatch between what parents say they want for their children and where districts are currently investing funds. While around 21 percent of ESSER III funds committed for academic recovery are directed toward summer school, 32 Per Burbio, some districts have grouped “summer learning and supplemental after-school programs” in their committed spending. Our analysis assumes that within this category, 50 percent of committed spending is for academic summer school, and 50 percent is for academic after-school programs. only 17 percent of parents are interested in this option. Meanwhile, 7 percent of funds committed for academic recovery are directed to tutoring, yet 29 percent of parents are interested in this option.

In addition, the students who need services most may not be receiving them. High-income students have less unfinished learning, on average, than low-income students. Yet high-income parents are both more concerned about their children’s academic performance and more likely to have signed up their children for programs to help them recover. They are also more satisfied with academic and mental-health recovery programs provided by their children’s school. A more targeted approach may be required to ensure that low-income students and other vulnerable populations are able to access high-quality support and recovery programs.

After an incredibly challenging period, initial research suggests some students are beginning to settle back into their prepandemic school routines. However, prepandemic education was failing many students, and these inequalities have been exacerbated by the pandemic, causing some segments—primarily low-income students and students of color—to fall even further behind their peers. Moreover, disruptions to learning continue, and programs to support students are not always reaching the ones who need it most. If this trend continues, the pandemic could leave students with increasingly unequal access to education and opportunity.

Decades of education research have reinforced the relationship between poverty and depressed learning outcomes, and the income achievement gap has grown over the past three decades as income inequality has risen. 33 Sean F. Reardon et al., Is separate still unequal? New evidence on school segregation and racial academic achievement gaps , Stanford Center for Education Policy Analysis working paper, number 19-06, September 2021, cepa.stanford.edu. An inclusive economic recovery will be important to avoid further exacer­bating widening gaps in learning outcomes. School systems are increasingly swimming upstream against these strong educational and economic currents. To not only prevent widening gaps in opportunity and achievement but also close them, systems can invest now to ensure all students have the chance to recover from the pandemic’s many setbacks and reach their full potential.

Emma Dorn is a senior expert in McKinsey’s Silicon Valley office; Bryan Hancock and Jimmy Sarakatsannis are partners in the Washington, DC, office; and Ellen Viruleg is a senior adviser based in Providence, Rhode Island.

The authors wish to thank Annie Chen, Chauncey Holder, and Kunal Kamath for their contributions to this article.

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Single-Parent Households and Children’s Educational Achievement: A State-Level Analysis

Paul r. amato.

a Department of Sociology, Pennsylvania State University

Sarah Patterson

b Department of Sociology, Pennsylvania State University, 211 Oswald Tower, University Park, PA 16802

Brett Beattie

c Department of Sociology, Pennsylvania State University, 211 Oswald Tower, University Park, PA 16802

Although many studies have examined associations between family structure and children’s educational achievement at the individual level, few studies have considered how the increase in single-parent households may have affected children’s educational achievement at the population level. We examined changes in the percentage of children living with single parents between 1990 and 2011 and state mathematics and reading scores on the National Assessment of Educational Progress. Regression models with state and year fixed effects revealed that changes in the percentage of children living with single parents were not associated with test scores. Increases in maternal education, however, were associated with improvements in children’s test scores during this period. These results do not support the notion that increases in single parenthood have had serious consequences for U.S. children’s school achievement.

1. Introduction

Two well-known facts provide a rationale for the current study. First, the percentage of children living with single parents increased substantially in the United States during the second half of the 20 th century. Only 9 percent of children lived with single parents in the 1960s—a figure that increased to 28 percent in 2012 ( Child Trends, 2013 ). Given current trends, about of half of all children will spend some time living with single parents before reaching adulthood ( McLanahan and Percheski, 2008 ). Second, research shows that children in single-parent households score below children in two-parent households, on average, on measures of educational achievement ( Amato, 2005 ; Brown, 2010 ; McLanahan and Sandefur, 1994 ). The combination of these two observations suggests that the rise in single parenthood has lowered (or slowed improvements in) the educational achievement of children in the United States.

Some observers have claimed that the rise of single-parent families (as reflected in high rates of divorce and nonmarital childbearing) is the primary cause of school failure and related problems of delinquency, drug use, teenage pregnancies, poverty, and welfare dependency in American society ( Blankenhorn, 1995 ; Fagan, 1999 ; Pearlstein, 2011 ; Popenoe, 2009 ; Whitehead, 1997 ). Consider the following statements:

Very high rates of family fragmentation in the United States are subtracting from what very large numbers of students are learning in school and holding them back in other ways. This in turn is damaging the country economically by making us less hospitable to innovation while also making millions of Americans less competitive in an increasingly demanding worldwide marketplace ( Perstein, 2011 , p. xiii)
Fatherlessness is the most harmful demographic trend of this generation. It is the leading cause of declining child well-being in our society. It is also the engine driving our most urgent social problems, from crime to adolescent pregnancy to child sexual abuse to domestic violence against women ( Blankenhorn, 1995 , p. 1).
Marital and family stability is undeniably linked to economic prosperity for American families… The effects of marital breakdown on national prosperity and the well-being of individual children are like the action of termites on the beams in a home’s foundation: They are weakening, quietly but seriously, the structural underpinnings of society ( Fagan, 1999 ).

How strong is the evidence to support these claims? Although dozens (perhaps hundreds) of studies have examined associations between family structure and children’s educational achievement at the individual level, few studies have considered whether single parenthood is linked to declines in children’s educational achievement (or other forms of well-being) at the aggregate level. The purpose of the current study is to assess whether changes in the percentage of children living with single parents were related to changes in children’s scores on the National Assessment of Educational Progress (NAEP) between 1990 and 2011. To address this question, we conducted a state-level analysis of NAEP data using statistical models with state and year fixed-effects.

2. Background

A small number of studies have suggested that single parenthood has problematic consequences for children’s school performance at the aggregate or societal level. Using multilevel modeling, Pong (1997 , 1998) found that U.S. students performed more poorly on math and reading achievement tests in schools with high proportions of children from single-parent families, even after controlling for school socioeconomic status and other school characteristics. Bankston and Caldas (1998) obtained comparable results with aggregate data on general academic achievement from students in Louisiana. In a cross-national study, Pong, Dronkers, and Hampden-Thompson (2003) found that single-parent family status was negatively associated with math and science achievement scores in nine out of 11 countries. Moreover, the gap in achievement between children with one rather than two parents was smaller in countries with more supportive social policies, such as family and child allowances and parental leave. These four studies are useful in showing that single parenthood and academic performance are associated within schools and countries. None of these studies, however, used longitudinal data to see if increases in single parenthood are accompanied by declines in the aggregate level of student performance.

Several studies have shown that the rise in the percentage of children living with single parents since the 1960s was related to an increase in child poverty in the U.S., although the strength of this association varies with the particular years studied ( Eggebeen and Lichter, 1991 ; Iceland, 2003 ; Martin, 2006 ; Thomas and Sawhill, 2005 ). Given that single parents (usually mothers) are more likely than married mothers to be poor, this result is not surprising. Nevertheless, the rise in child poverty associated with single parenthood since the 1960s may have had negative consequences for children’s educational outcomes.

Several good reasons exist for assuming that the number of parents in a household affects children’s academic achievement (for reviews, see Amato, 2010 ; Brown 2010 ; McLanahan and Percheski 2008 ). First, children in single-parent households have a lower standard of living than do children in two-parent households. Family income, in turn, is a good predictor of children’s school grades and test scores. Second, parents are important sources of social capital and provide many resources to children, including emotional support, encouragement, everyday assistance, and help with homework. Parents’ provision of social capital, in turn, is positively associated with children’s school success. Children who live with single parents, however, have less access to these social resources, in general, than do children with two parents in the household. Finally, most children with single parents have experienced the disruption of their parents’ unions, and many of these children endure additional parental transitions before reaching adulthood. The cumulative amount of household instability or “turbulence” in children’s lives is associated with a variety of problematic outcomes, including school performance and educational attainment.

Selection provides an alternative explanation. Growing up in poverty increases the risk of becoming a single parent as well as the risk of academic failure for one’s children. In addition, some parents have personal traits that predict poor academic outcomes for children, such as low cognitive ability, personality disorders, alcohol or substance use problems, and poor social and parenting skills. These traits also increase the risk of relationship disruptions and the formation of single-parent households. Because these traits can be causes of single parenthood as well as problematic child outcomes, the association between family structure and children’s academic achievement is likely to be at least partly spurious.

Researchers have adopted a variety of strategies to assess whether the links between family structure and child outcomes are causal or spurious, including the use of fixed effects models to control for unmeasured time-invariant variables. Results from studies using fixed effects models are mixed, however, with some suggesting that associations between family structure and child outcomes are spurious ( Aughinbaugh, Pierret, and Rothstein, 2005 ; Bjorklund and Sundstrom, 2006 ; Bjorklund, Ginther, and Sundstrom, 2007 ), and others supporting a causal interpretation ( Amato and Anthony, 2014 ; Cherlin, Chase-Lansdale, and McRae, 1998 ; Ermisch and Francesconi, 2000; Gennetian, 2005 ). After reviewing studies that used fixed effects models and other methods to adjust for unobserved heterogeneity, McLanahan, Tach, and Schneider (2013) concluded that father absence probably increases children’s antisocial behavior but may not affect children’s cognitive outcomes and academic performance. This debate in the literature is unlikely to be resolved in the near future, and most reviewers have concluded that a combination of selection and causal factors are responsible for the links between family structure and children’s well-being (e.g., Amato 2010 ; McLanahan and Percheski, 2008 ; McLanahan et al., 2013 ).

2. The Current Study

The current study examines associations between the percentage of children living in single-parent households and children’s test scores on the NAEP. Because mathematical and reading skills are central to children’s school success, we focus on trends in math and reading scores in grades 4 and 8. State level data on these outcomes have been available since the early 1990s.

Analyzing data at the state level makes it possible to determine if the increase in single parenthood in the United States since 1990 was associated with declines in children’s test scores in the general population. This goal differs from most previous studies in this literature, which have examined links between family structure and school achievement among individual children. The current study attempts to answer the question, “Has the increase in single parenthood lowered the educational achievement of children in the U.S.?” rather than, “Do children living with single parents have lower levels of educational achievements than children living with two parents?”

A disadvantage of using population-level data involves the well-known ecological fallacy, or the possibility that associations observed at the aggregate level do not hold at the individual level. An offsetting advantage, however, is that population-level data are less susceptible to selection effects than are individual-level data. If associations observed at the individual level are due mainly to the self-selection of troubled adults into single parenthood, then children’s aggregate-level test scores will not necessarily decline. Because the children of troubled parents will be disadvantaged irrespective of what type of families they reside in, increases in the proportion of single-parent households would have few consequences for children’s aggregate test scores. Of course, traits such as low cognitive ability, personalities problems, and mental health disorders change slowly at the population level. For this reason, the increase in single parenthood since the 1960s almost certainly was due to structural factors rather to than genes or personalities. This being the case, a decline in children’s mean test scores concomitant with an increase in single parenthood would suggest a causal interpretation, assuming that relevant third variables are controlled.

4.1. Demographic Variables

Data on children’s living arrangements between 1990–2011 came from the American Community Survey, the U.S. Decennial Census, and the Current Population Survey (CPS), depending on the year. These data were downloaded through the Integrated Public Use Microdata Series USA ( Ruggles et al., 2010 ). We relied on constructed variables to describe children’s living arrangements. MOMLOC and POPLOC indicated whether a child’s mother and father resided in the same household, respectively. We classified children into single-parent households if they lived with a mother or a father (either biological or adoptive) but not both. We also included a variable to reflect the percentage of children living with neither parent. Children in all households with two parents (two biological parents, two adoptive parents, one biological parent and a step-parent) served as a combined reference category. We did not distinguish between cases in which children lived with married and unmarried parents in the main analysis, given our focus on the number of parents in the household rather than parents’ marital status. (We considered parents’ marital status in supplementary analyses, however, as described later.) Children were between the ages of 8 and 11 for the analysis of 4 th grade achievement and between the ages of 12 and 15 for the analysis of 8 th grade achievement.

Although children living with stepparents are of interest, it is not possible to identify all stepparents from the available information ( Minnesota Population Center, 2011 ). As a result, an unknown number of children classified as living with two parents are living with a parent and a stepparent. This ambiguity precluded the use of stepfamilies as a separate analytic category in the main analysis, although we attempted to deal with this issue in supplementary analyses, as noted below. In addition, it is difficult to identify children living with same-sex parents. Although this is a limitation, the percentage of children living with same-sex parents is relatively small and should not distort the results appreciably.

As discussed below, our fixed effects models controlled for all stable state-level variables and period effects that influenced all states similarly. It was necessary, however, to control for variables that might be causes of single parenthood as well as children’s academic achievement to reduce the possibility of observing spurious associations. To control for race-ethnicity, we included the percentage of children in each age group who were Black or Hispanic. To control for parental education, we included three variables: the percentage of mothers (of children in each age group) with high school degrees, with some college or postsecondary education, and with college degrees. The percentage of mothers without high school degrees was omitted because it was perfectly correlated with the sum of the other three variables. Finally, we included the percentage of children living below the federal poverty line. We used these variables as controls because children’s race/ethnicity, parents’ education, and poverty status are related to the likelihood of living with a single parent ( Kreider and Elliot, 2009 ) as well as children’s academic test scores ( Cheadle, 2008 ). All of the variables described in this section were weighted and aggregated to the state level in each year (1990–2011).

4.2. Student Achievement Variables

The NAEP surveys were congressionally mandated to track the academic skills of students over time, with the first national assessment occurring in 1969. The implementation of the No Child Left Behind Act of 2001 increased the importance of the NAEP by requiring states to participate in biennial mathematics and reading assessments ( National Center for Educational Statistics, 2009 ).

The NAEP uses a multi-stage sampling method to select students for assessment. All eligible schools within a state are grouped according to location, racial/ethnic composition, and student achievement to ensure an accurate representation of the student population. Within these groups, selected schools are assigned weights based on the school population relative to the state’s student population for that grade, with larger schools receiving larger weights. Within sampled schools, students in the target grades were randomly selected and then randomly assigned to a single subject area in which they were tested ( Allen et al. 2001 ). Approximately 3000 students per subject and per grade are assessed in each state, though this varies somewhat by state size.

The National Assessment Governing Board oversees the construction of questions with input from teachers, researchers, measurement experts, policymakers and the general public.

A great deal of attention is paid to ensure that tests are comparable between states and across time. To ensure comparability across states, for example, the same set of test booklets is used across the nation. Assessments are generally consistent from year to year and any changes are carefully documented and investigated. In 2004, for example, accommodations were allowed for children with disabilities and limited English language abilities. Although national estimates include both public and private schools, the state level data (used in the current analysis) includes test scores from public schools only. (Enrollment in private schools declined slightly since the mid 1990s, from 11.7% to 10% of all children [National Center for Education Statistics, 2013]. This trend should not have major implications for our analysis.)

Although data are collected on a range of subjects, we focused on mathematics and reading scores because these were available for the largest number of years. The NAEP mathematics survey was administered in 1992, 1996, 2000, 2003, 2005, 2007, 2009 and 2011 for grades 4 and 8, with grade 8 also being tested in 1990. The mathematics test is designed to measure ability in five core areas: number properties and operations, measurement, geometry, data analysis and statistics, and algebra. These core areas are measured through a combination of multiple choice and constructed response questions. The NAEP reading survey was administered in 1998, 2002, 2003, 2005, 2007, 2009 and 2011 for grades 4 and 8, with grade 4 also being tested in 1992 and 1994. This test assesses comprehension of both literary and informational texts using a combination of multiple choice and constructed response questions.

State NAEP scores are available in two forms. The first is a mean score based on Item Response Theory (IRT), with individual scores ranging from 0 to 500. Depending on the year, standard deviations for the tests ranged from 29–32 for 4 th grade mathematics, from 35–42 for 4 th grade reading, from 36–38 for 8 th grade mathematics, and from 34–36 for 8 th grade reading. The second score is based on achievement levels and involves the percentage of students in four groups: below basic proficiency, basic proficiency, proficient, and advanced proficiency. Our first analysis was based on the mean (overall) state scores on each test for each year. Because the percentage of children living with single parents might disproportionately affect children at the bottom or top of the test distributions, we supplemented the analysis of mean scores with state data on the percentage of children scoring at below basic and advanced proficiency levels. Across all states and years, the percentage of students who scored at the below basic level was 26% for 4 th grade mathematics, 36% for 4 th grade reading, 34% for 8 th grade mathematics, and 26% for 8 th grade reading. The percentage of students who scored at advanced levels was 4% for 4 th grade mathematics, 7% for 4 th grade reading, 5% for 8 th grade mathematics, and 2% for 8 th grade reading.

4.3. Plan of Analysis

We used pooled time series regression analysis with fixed state and year effects to estimate the statistical models. The data file included one observation for each state in each year beginning in 1990 and concluding in 2011. Because state NAEP scores were not available in every year, the total number of observations was 382 for 4 th grade math, 421 for 4 th grade reading, 404 for 8 th grade math, and 322 for 8 th grade reading. We included a series of dummy variables (one for each state minus one) to capture state fixed effects and a second series of dummy variables (one for each year minus one) to capture year fixed effects.

The formula for the first model (without control variables) is

where Y it is the mean NAEP score in state i in year t ; α is a constant, β is a regression coefficient, State i refers to state fixed effects, Year t refers to year fixed effects, and ε it is the error term. State fixed effects control for all unmeasured state variables that are time invariant. Examples include region of the country, state government policies that did not change during the period of observation, and relatively stable cultural factors such as religiosity and political party support (consistently red or blue states). Year fixed effects control for all period effects (such as elections, changes in federal policies, and economic recessions) that affect all states similarly. Because the statistical models included only within-state change, the regression coefficients can be interpreted as the estimated effect of changes in the independent variables on changes in the dependent variables. (See Allison, 2009 , for a discussion of fixed effects models. For a recent study that used state and year fixed effects, see Amato and Beattie, 2011 .)

The regression analyses involve four models. The first model shows the association between changes in single parenthood and changes in state NAEP scores controlling for state and year fixed effects. The second model included controls for the percentage of Black and Hispanic children (in the appropriate age range) in each state. Controlling for these variables is necessary because race-ethnicity might affect the likelihood of becoming a single parent, but becoming a single parent cannot affect one’s race-ethnicity. The third model added controls for mothers’ education. Although maternal education can affect the likelihood of becoming a single parent, it also is possible that having a child outside of marriage affects a mothers’ likelihood of completing high school or attending college. For this reason, adding maternal education yields a slightly conservative estimate of the effects of single parenthood. (In preliminary models we also used paternal education and a variable reflecting the average education of mothers and fathers. Because maternal and paternal variables are positively correlated, these models yielded results that did not differ substantively from those presented in the main analysis.) In the fourth and final model, we included the percentage of children in poverty. Because poverty can be a cause as well as a consequence of single parenthood, this model was the most conservative with respect to estimating family structure effects. Specifically, the final model indicates whether single parenthood has an estimated effect that is independent of poverty.

5.1. Descriptive Trends

Figure 1 shows the state means for the NAEP tests. In this figure and the one that follows, each state is weighted equally, although weighting by population size produces similar trends.

An external file that holds a picture, illustration, etc.
Object name is nihms696746f1.jpg

Mean State Scores on Math and Reading Tests (National Assessment of Educational Progress) by Year.

Mean 4 th grade math scores increased from 218 in 1992 to 240 in 2011. This 22-point improvement represents a gain of about two-thirds of a standard deviation. Although mean math scores improved in all states, some states improved more than others, with increases ranging from +12 to +30 points. Scores on the 4 th grade reading test increased modestly, rising from 215 to 220 overall, or about one-eighth of a standard deviation. Not all states showed improvements, with changes for individual states ranging from −5 to +16 points.

Figure 1 also shows that mean 8 th grade math scores increased from 263 in 1990 to 285 in 2011. This 22-point improvement represents a gain of about two-thirds of a standard deviation. States varied a great deal, however, with the amount of change ranging from −5 to +57 points. Between 1998 and 2011, mean 8 th grade reading scores increased from 261 to 266—a change of about one-seventh of a standard deviation. The amount of change across states ranged from −17 to +35 points. (The overall trends for 4 th and 8 th graders were similar, although this similarity would be less striking if different starting and ending years had been selected.)

Despite a good deal of variability across states, bivariate fixed effects regression analyses (not shown) revealed that the year of observation was positively and significantly associated with the state means of all four tests (all p < .01). Although not shown in the figure, the percentage of children scoring at below basic and advanced proficiency levels mirrored the trends for the means. That is, between the early 1990s and 2011, the percentage of children at both grade levels scoring at the below basic level declined and the percentage of children scoring at the advanced level increased, although these trends were stronger for mathematics than for reading. All of these time trends were statistically significant ( p < .01).

The demographic variables also changed over time. Figure 2 shows that the mean percentage of 4th grade children living with single parents increased from 23% in 1990 to 33% in 2011. This 10 percentage point change represents an increase of about half of a percentage point per year. Variation existed across states, however, with increases ranging from 3% to 19%. A regression analysis (not shown) revealed that the overall increase was statistically significant ( p < .001). The percentage of children living with neither parent also rose slightly during this period, from about 3% in the early 1990s to 4% in the late 2000’s. Although small, this increase was statistically significant ( p < .001). The figure also shows that the percentage of Black children changed relatively little during this time (a decline of one half of a percent). In contrast, the percentage of Hispanic children increased substantially from 7 percent in the early 1990s to 15 percent in 2011 ( p < .001). Maternal education also increased. The percentage of mothers with college degrees, for example, increased from 18 percent to 32 percent ( p < .001). Finally, the percentage of children living in poverty fluctuated a good deal during this period, although it rose after the Great Recession in 2007. The trends for 8 th grade children were nearly identical to those of 4 th grade children and never differed by more than a percentage point across years. For this reason, only the 4 th grade trends are shown in a figure.

An external file that holds a picture, illustration, etc.
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Mean Percentage of 4 th Grade Children Living with Single Parents, No Parents, and Other Demographic Variables by Year.

Taken together, Figures 1 and ​ and2 2 demonstrate that children’s performance on the NAEP exams improved during the same period that the percentage of children living with single parents increased. Although a more detailed analysis is necessary to reach firm conclusions, these contrary trends suggest that the increase in single parenthood did not have catastrophic effects on children’s school achievement. Nevertheless, as noted earlier, not all states showed improvements in NAEP scores. And even in the case of 4 th grade math (in which all states showed improvements), the two variables could be negatively associated in a fixed effects framework if test scores increased more slowly in states that had the largest increases in single parenthood.

Preliminary analysis revealed a high correlation of .71 ( p < .001) between the percentage of children living with single parents and the percentage of Black children across all states and years. Between-state correlations for single years were similar in magnitude. Given that single parenthood is more common among African Americans than among whites, this finding is not surprising. Although correlations of this magnitude can produce multicollinearity problems, state fixed effects models rely entirely on within-state variation and exclude between-state variation. With only within-state variation modeled, the correlation between the two variables was only .37 ( p < .001). Although statistically significant, the moderate within-state correlation was unlikely to cause estimation problems. All other correlations between independent variables were low or moderate in magnitude (below .40).

5.2. Regression Analysis

We estimated fixed effects and random effects models for the same outcomes in preliminary analyses. In each case, Hausman tests ( Worrall, 2008 ) indicated that fixed effects models provided a better fit to the data than did random effects models (all p < .05). In other words, using random effects models with these data (and failing to take unmeasured differences between states into account) would have provided misleading results.

Preliminary analyses without fixed effects revealed that the percentage of children living with single parents was negatively associated with mean NAEP scores. In other words, scores tended to be lower in states with relatively large proportions of single-parent households. States with large proportions of single-parent households differ from other states in many other ways, however. When state fixed effects (which control for all stable state characteristics) were added to the models, the percentage of children living with single parents and mean NAEP scores were positively associated. This is because the percentage of children living with single parents increased at the same time that children’s test scores also increased (see Figures 1 and ​ and2)—another 2 )—another potentially misleading association. To control for secular trends in both variables, it was necessary to add dummy variables for years in the statistical models to capture year fixed effects.

The state and year fixed effects accounted for most of the variance in test scores. For example, the fixed effects accounted for 95% of the variance in 4 th grade mathematics and 88% of the variance in 8 th grade mathematics. Nevertheless, after accounting for the state and year fixed effects, the independent variables (family structure, race-ethnicity, maternal education, and poverty) accounted for significant increments in variance for all of the mathematics outcomes (mean scores, % below basic, and % advanced) at both grade levels. For example, with the variance due to the fixed effects removed from the data, the independent variables accounted for 9% of the remaining variance in 4 th grade mathematics scores and 14% of the remaining variance in 8 th grade mathematics scores. The independent variables, however, did not account for significant increments in variance for the reading outcomes at either grade level. For this reason, we focus on the results for mathematics in the remainder of this paper.

Table 1 shows the regression results for state mean NAEP scores for the 4 th grade (top panel) and 8 th grade (bottom panel). For ease of presentation, the dummy variables for states and years are not shown. The constants also are excluded from the table because they reflect the excluded states and years and do not have a substantive interpretation. Model 1 reveals that the association between the percentage of children with single parents and 4 th grade math scores was negative but close to zero (−.051) and not statistically significant. The corresponding coefficient for the percentage of children living with no parents (−.272), in contrast, was negative and statistically significant. The regression coefficient indicated that a 1-point increase in the percentage of children living with neither parent was associated with a decline in test scores equivalent one-fourth of a point—a small effect. In Model 2, which included children’s race-ethnicity, the percentage of Black children was negatively and significantly associated with test scores, whereas the coefficient for the percentage of Hispanic children was not statistically significant. With race-ethnicity controlled, the coefficient for the percentage of children living with neither parent declined and no longer was significant. In Model 3, the variables reflecting maternal education (high school graduate, some college, and college graduate) were positive and statistically significant. These results indicate that increases in maternal educational attainment were linked with improvements in children’s test scores. Finally in Model 4, the percentage of children living at or below the poverty line was not significant, and adding this variable to the equation did not change the coefficients for race and maternal education appreciably.

Fixed Effects Regression of Mean Math Scores (National Assessment of Educational Progress) on the Percentage of Children Living with Single Parents or No Parents: State Data, 1990–2011.

Table values are unstandardized regression coefficients with robust standard errors in parentheses. Results are based on 12 separate regression analyses with dummy variables for states and years. Total observations (states x years) = 382 for 4 th grade and 404 for 8 th grade.

With respect to the 8 th grade, the regression coefficients for the percentage of children living with single parents and no parents failed to attain statistical significance in any model. In Model 2, the coefficient for Black children was negative and significant, and in Model 3, the variables reflecting maternal education all achieved significance. The coefficient for poverty was positive and statistically significant in Model 4 (a counter-intuitive finding). This result indicates that states with high levels of child poverty tended to have higher mathematics test scores, controlling for family structure, race-ethnicity, and maternal education. Overall, the results from Table 1 do not support the notion that the increase in the percentage of children living with single parents reduced children’s test scores.

The next set of analyses, shown in Table 2 , focused on the percentage of children scoring at a below basic level of proficiency on the NAEP mathematics test. The percentage of children living with single parents was not a significant predictor of this outcome in any model at either grade level. The coefficient for no parents, however, was positive and significant in two of the four models for 8 th grade students. The regression coefficient for the percentage of Black children was positive and significant for 4 th grade students in all models, although the corresponding coefficient for 8 th grade students was significant only in Model 2. For students at both grade levels, maternal education was negatively and significantly related to the outcome. Finally, increases in child poverty were not associated with math scores at either grade level.

Fixed Effects Regression of the Percentage of Children Scoring at a Below Basic Level of Math Proficiency (National Assessment of Educational Progress) on the Percentage of Children Living with Single Parents or No Parents: State Data, 1990–2011.

Table values are unstandardized regression coefficients with robust standard errors in parentheses. Results are based on 12 separate regression analyses with dummy variables for states and years. Total observations (states x years) = 382 for 4 th grade math and 404 for 8 th grade.

The final analyses, involving the percentage of children scoring at an advanced level of proficiency, are summarized in Table 3 . In contrast to previous results, the percentage of children living with single parents was negatively and significantly associated with 4 th grade math achievement, but only with control variables in the models (models 2, 3, and 4). A comparable result appeared for 8 th grade math in Models 1 through 3. Although the results are not entirely consistent across grade levels, they suggest that increases in the percentage of children living with single parents were related to declines in the percentage of children scoring at the top of the mathematics distribution. In addition, the coefficient for not living with either parent in the 8 th grade was negative and significant in Models 3 and 4.

Fixed Effects Regression of the Percentage of Children Scoring at an Advanced Level of Math Proficiency (National Assessment of Educational Progress) on the Percentage of Children Living with Single Parents or No Parents: State Data, 1990–2011

Table values are unstandardized regression coefficients with robust standard errors in parentheses. Results are based on 12 separate regression analyses with dummy variables for states and years. Total observations (years x states) = 382 for 4 th grade math and 404 for 8 th grade math.

With respect to the control variables, the percentage of Black children was positively related to 4 th grade outcomes (but not 8 th grade outcomes)—a result that appears to contradict the earlier results for this variable. Combined with the results from Table 2 , this outcome suggests that increases in the percentage of Black children within states resulted in more children scoring in both tails of the distribution of 4 th grade math scores. Maternal education (although not necessarily having a college degree in this case) was positively related to advanced scores on the 4 th grade test but not to advanced scores on the 8 th grade test. Poverty was not related to advanced test scores in any model.

5.3. Alternative Specifications

Alternative specifications of the models yielded few new findings. First, weighting the data by state populations (so that larger states contributed more to the analysis than smaller states) yielded results nearly identical to those reported in Tables 1 – 3 . Second, following Friedberg (1998) , we included a series of state x time interaction terms in the models to control for state-level trends that may have been distinct from the national trend. None of the coefficients for single-parent households were significant in these models, although most of the coefficients for maternal education continued to be significant. Third, we conducted analyses in which the percentage of children living in single-parent households was lagged by 1 through 5 years. This procedure did not produce findings that differed substantively from those reported in Tables 1 – 3 . Fourth, Washington D.C. consistently had low mean scores on the achievement tests as well as the highest proportions of children living with single parents. Because this was an influential case, we ran the analyses with Washington D.C. excluded, but the results were nearly identical. Fifth, Bertrand, Duflo, and Mullainathan (2004) suggested using bootstrapped standard errors to deal with potential problems with serial correlation in fixed effects models. Following this suggestion, we conducted new analysis with bootstrapped errors, and although the standard errors increased modestly, the results were essentially the same as those reported earlier.

Fifth, although our data set undercounts stepparents, we created a dummy variable for stepparent households that we were able to identify. This step effectively removed identified stepfamilies from the omitted comparison group of two-parent households. With the new stepparent variable in the model, the results mirrored those in Table 1 – 3 , and the pattern of largely non-significant results for the single-parent household variable was replicated. The stepparent variable yielded one significant finding: a positive association between the percentage of 4 th grade children living with a stepparent and the percentage of children scoring at a below basic level of proficiency ( b = .33, p < .05). Finally, we conducted additional analyses with a dummy variable for two-parent households in which parents were unmarried. With this variable in the model, the omitted comparison group consisted of two-parent married couple households. The new variable was never significant, however, suggesting that the increase in unmarried parent households was not associated with children’s test scores. Moreover, the results for single parenthood variable did not change with this new variable in the model.

6. Discussion

The percentage of children living with single parents in the United States has increased steadily in recent decades. Moreover, research consistently demonstrates that children living with single parents score lower on measures of academic ability and achievement than do children with two continuously married parents ( Amato 2005 ; Brown, 2010 ; McLanahan and Sandefur, 1994 ). Given these findings, many observers have assumed that the increase in single parenthood has placed downward pressure on children’s school performance and educational achievement. Few studies, however, have attempted to establish whether the increase in single parenthood was linked with declines in children’s test scores (or with other aspects of children’s well-being) at the population level. The purpose of the present study was to assess whether the increase in single parenthood between 1990 and 2011 had consequences for state scores on the National Assessment of Educational Progress.

Did the rise in the percentage of single-parent households lower the academic performance of American children? Since 1990 the percentage of children living in single-parent households increased, as did children’s mean NAEP scores. Nevertheless, these two variables could be negatively associated if test scores rose more slowly in states with the largest increases in single parenthood. A statistical model with state and year fixed effects is necessary to determine whether this is the case. The current analysis indicates, however, that the percentage of children living with single parents was not associated with children’s mathematics scores. We did not focus on children’s reading scores, because they increased only modest during the last few decades and were not related significantly to our independent variables. Nevertheless, the results for reading scores also support the conclusion that increases in single parenthood did not lower children’s aggregate-level school performance. These findings contradict the claim that single parenthood has produced widespread school failure, a pervasive decline in the academic ability of American children, and a general weakening of American economic competitiveness ( Blankenhorn, 1995 ; Fagan, 1999 ; Pearlstein, 2011 ; Popenoe, 2009 ; Whitehead, 1997 ).

Despite the null results for mean state test scores, the present study suggests that the increase in single-parent households since 1990 reduced the percentage of children who scored at the top of the test score distributions (advanced NAEP levels). How big are these estimated effects? The regression coefficient for single-parent households in Model 3 of Table 1 (which included controls for race-ethnicity and maternal education but not poverty) was −.045 for 4 th grade math. Between 1990 and 2011, the percentage of children in this age group living with single parents increased by 9.48 points. This means that the rise in single parenthood was associated with a .43 decline in the percentage of 4 th grade children scoring at an advanced level (9.48 × −.045). For 8 th grade students, the corresponding coefficient in Model 3 of Table 3 was −.05, and our estimate of the total effect of the rise in single parenthood amounts to .48 of a percentage point (9.51 × −.05). A decline of approximately one-half of a percentage point is not trivial, especially when the percentage of children scoring at advanced levels ranged from only 2% to 8% during this period. Moreover, single parenthood increased a good deal between 1960 and 1990, and NAEP scores are not available for this period. If we were to extrapolate the current results to the 1960s, the cumulative effects of single parenthood on children’s advanced math scores would be larger. Nevertheless, these modest declines are not consistent with the strong claims made by many observers about single parenthood and the declining academic achievement of American children.

Given strong claims about the negative effects of single parenthood on American children, the current findings are surprisingly mild. These findings are consistent with the assumption that most of the associations between single parenthood and children’s academic performance (previously observed at the individual level) are due to selection. This interpretation is consistent with McLanahan, Tach, and Schneider (2013) , who concluded that the evidence for a causal effect of family structure on children’s educational achievement is weak. We suspect that not all of the association is spurious, however. In a recent study based on fixed effects regression models, Amato (2014) found that the average estimated effects of divorce on children’s standardized math and reading scores were statistically significant but weak: slightly below one tenth of a standard deviation for primary school students and slightly above one tenth of a standard deviation for high school students. With effect sizes in this range, even large increases in the percentage of children living with single parents would result in exceedingly small aggregate-level changes. This conclusion is consistent with Amato (2005) , who argued that changes in family structure have had only modest effects on child outcomes at the societal level. For example, he estimated that the percentage of children who have repeated a grade would be only 3 percentage points lower (21% rather than 24%) if children’s family structures had not changed between 1960 and 1995.

Although the increase in single-parent households does not appear to have affected children’s test scores appreciably, it is possible that family structure has had stronger effects on behavior than on cognitive ability, as suggested by McLanahan, Tach, and Schneider (2013) . If this is true, then the increase in single-parent households may have negatively affected children’s school grades and the probability of high school graduation, despite having few measureable consequences for test scores. It is also possible that the increase in single parenthood affected trends in other child outcomes, such as delinquency, behavior disorders, accidents, or mental health problems. Additional research that focuses on other aggregate-level outcomes would be a useful supplement to existing individual-level studies.

Although not the main focus of the current study, the percentage of children living without either parent was associated with some outcomes, such as the percentage of 8 th grade students scoring at a below basic level of proficiency. Comparatively little attention has been given to children living with neither parent, presumably because their numbers are relatively small and have not increased much in recent decades. Despite this lack of attention, however, children living without parents have worse educational outcomes, on average, than do children living with one or two parents, not only in the United States but also in other countries ( Scott, DeRose, Lippman, and Cook, 2013 ). How big is this effect in the current study? Our data indicate that the percentage of children living without parents increased by only about 1 percentage point between 1990 and 2011, and the regression coefficient from Table 2 (8 th grade, Model 2) was .321. Consequently, the increase in children living without parents may have been responsible for an increase of one-third of a percentage point in below basic achievement. Given the minimal changes in this household type, its effects at the aggregate level are necessarily modest.

Other findings from the current study are worthy of comment. The percentage of Black children was negatively associated with mean mathematics scores in the 4 th and 8 th grades ( Table 1 ). These findings are consistent with earlier research (e.g., Cheadle 2008 ) and speak to the continuing educational disadvantages associated with race in the United States. The current study also yielded an unexpected finding: a positive association between the percentage of Black children and the percentage of 4 th children scoring at an advanced level of proficiency. This suggests that an increase in the size of the Black population increases the variance in outcomes, with more scores in the bottom and the top of the distribution. Although this is an intriguing possibility, more research is needed to explore this finding. In contrast to the findings for Black children, the percentage of Hispanic children in the population was not related to any educational outcomes. In other words, the relatively large increase in the size of the Hispanic population in the United States during the last two decades does not appear to have had any negative consequences for children’s educational performance on the NAEP—a finding that may be relevant to current debates about immigration.

Increases in the educational attainment of mothers were associated positively with most of the educational outcomes. Tables 1 – 3 included coefficients for three levels of maternal education (high school graduate, some college, and college graduate), with the percentage of mothers without high school degrees serving as the omitted category. For most outcomes and grade levels, all of the education coefficients were significant, although the coefficients for the Mom High School and Mom Some College categories were sometimes larger than the coefficients for the Mom College Grad category. Although these coefficients appear to be in the “wrong” order, their confidence intervals overlapped considerably. For example, for 4 th grade mathematics scores, the coefficients (and 95% confidence intervals) were .208 (.064–.352) for high school, .217 (.074–.359) for some college, and .178 (.022–.333) for college graduate. Even though the pattern of coefficients was not always in the expected rank order, the overriding conclusion is that increases in maternal educational were related to improvements in mathematics achievement among American children. This positive change occurred despite the growth of single-parents households during the same period. This conclusion is consistent with Western, Bloome, and Percheski (2008) , who found that income inequality in the U.S. was exacerbated by increases in single-parent families but cushioned by corresponding increases in women’s educational attainment and employment. Many observers have focused on the potentially troubling consequences of the increase in single-parent households and failed to note the substantial and beneficial increase in maternal education in recent decades. A balanced assessment of children’s changing well-being in the U.S. should take both trends into account.

The current study is limited in several respects. Reading and math scores were not available in every year due to the structure of the NAEP. Moreover, we were unable to examine student achievement prior to 1990, a period in which single parenthood increased a great deal. Furthermore, the focus on reading and math achievement excluded other potentially informative educational outcomes, such as the percentage of children each year who are held back or drop out of school. And we could not distinguish clearly between children living with two biological parents and children living with one biological parent and a stepparent.

Moreover, variables not included in our model may have affected the percentage of children living with single parents as well as children’s test scores during this period. One possibility involves welfare reform. Our model (with year fixed effects) captured aspects of the national welfare reform legislation of 1996 that affected all states similarly. Nevertheless, many states were experimenting with welfare reform prior to the passage of the federal legislation, and states were given discretion on how to implement some aspects of the legislation. Including all of these state variations in the statistical model was beyond the limits of the current study. Of course, because our results for single parenthood were not statistically significant, there is no risk that omitted variables produced a spurious association in the current study. It is possible, of course, that one or more omitted variables masked a negative association between single parenthood and children’s test scores. For a suppression effect to occur, however, the omitted variable would need to have a positive effect on single parenthood as well as a positive effect on children’s test scores (or a negative effect on each variable). It is difficult to think of omitted variables that might be related to our independent and dependent variable in this manner.

Despite these limitations, the current study is one of the few to consider how changes in family structure affected child outcomes at the societal rather than the individual level. Additional studies that examine a broader range of child outcomes, as well as a longer time periods, would make useful contributions to our understanding of a topic of great public concern.

  • We used the CPS, the decennial U.S. Census, and the American Community Survey for data on single-parent households, and the National Assessment of Educational Progress for data on children’s math and reading scores.
  • We used regression models with state and year fixed effects to estimate the effects of changes in the percentage of children living with single parents on changes in children’s 4 th and 8 th grade test scores between 1990 and 2010.
  • Single parenthood was not associated with mean mathematics and reading scores, although it was weakly but negatively associated with the percentage of children who scored at an “advanced” level of proficiency in mathematics.
  • Increases in maternal education were generally related to improvements in children’s math scores.

Acknowledgments

We thank David Johnson and Wayne Osgood for expert advice on the statistical analysis. This research was supported by funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development to the Population Research Institute at The Pennsylvania State University for Population Research Infrastructure (R24 HD041025) and Family Demography Training (T-32HD007514).

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  • Systematic review
  • Open access
  • Published: 19 February 2024

‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice

  • Annette Boaz   ORCID: orcid.org/0000-0003-0557-1294 1 ,
  • Juan Baeza 2 ,
  • Alec Fraser   ORCID: orcid.org/0000-0003-1121-1551 2 &
  • Erik Persson 3  

Implementation Science volume  19 , Article number:  15 ( 2024 ) Cite this article

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The gap between research findings and clinical practice is well documented and a range of strategies have been developed to support the implementation of research into clinical practice. The objective of this study was to update and extend two previous reviews of systematic reviews of strategies designed to implement research evidence into clinical practice.

We developed a comprehensive systematic literature search strategy based on the terms used in the previous reviews to identify studies that looked explicitly at interventions designed to turn research evidence into practice. The search was performed in June 2022 in four electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched from January 2010 up to June 2022 and applied no language restrictions. Two independent reviewers appraised the quality of included studies using a quality assessment checklist. To reduce the risk of bias, papers were excluded following discussion between all members of the team. Data were synthesised using descriptive and narrative techniques to identify themes and patterns linked to intervention strategies, targeted behaviours, study settings and study outcomes.

We identified 32 reviews conducted between 2010 and 2022. The reviews are mainly of multi-faceted interventions ( n  = 20) although there are reviews focusing on single strategies (ICT, educational, reminders, local opinion leaders, audit and feedback, social media and toolkits). The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Furthermore, a lot of nuance lies behind these headline findings, and this is increasingly commented upon in the reviews themselves.

Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been identified. We need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of research perspectives (including social science) in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed.

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Contribution to the literature

Considerable time and money is invested in implementing and evaluating strategies to increase the implementation of research into clinical practice.

The growing body of evidence is not providing the anticipated clear lessons to support improved implementation.

Instead what is needed is better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice.

This would involve a more central role in implementation science for a wider range of perspectives, especially from the social, economic, political and behavioural sciences and for greater use of different types of synthesis, such as realist synthesis.

Introduction

The gap between research findings and clinical practice is well documented and a range of interventions has been developed to increase the implementation of research into clinical practice [ 1 , 2 ]. In recent years researchers have worked to improve the consistency in the ways in which these interventions (often called strategies) are described to support their evaluation. One notable development has been the emergence of Implementation Science as a field focusing explicitly on “the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice” ([ 3 ] p. 1). The work of implementation science focuses on closing, or at least narrowing, the gap between research and practice. One contribution has been to map existing interventions, identifying 73 discreet strategies to support research implementation [ 4 ] which have been grouped into 9 clusters [ 5 ]. The authors note that they have not considered the evidence of effectiveness of the individual strategies and that a next step is to understand better which strategies perform best in which combinations and for what purposes [ 4 ]. Other authors have noted that there is also scope to learn more from other related fields of study such as policy implementation [ 6 ] and to draw on methods designed to support the evaluation of complex interventions [ 7 ].

The increase in activity designed to support the implementation of research into practice and improvements in reporting provided the impetus for an update of a review of systematic reviews of the effectiveness of interventions designed to support the use of research in clinical practice [ 8 ] which was itself an update of the review conducted by Grimshaw and colleagues in 2001. The 2001 review [ 9 ] identified 41 reviews considering a range of strategies including educational interventions, audit and feedback, computerised decision support to financial incentives and combined interventions. The authors concluded that all the interventions had the potential to promote the uptake of evidence in practice, although no one intervention seemed to be more effective than the others in all settings. They concluded that combined interventions were more likely to be effective than single interventions. The 2011 review identified a further 13 systematic reviews containing 313 discrete primary studies. Consistent with the previous review, four main strategy types were identified: audit and feedback; computerised decision support; opinion leaders; and multi-faceted interventions (MFIs). Nine of the reviews reported on MFIs. The review highlighted the small effects of single interventions such as audit and feedback, computerised decision support and opinion leaders. MFIs claimed an improvement in effectiveness over single interventions, although effect sizes remained small to moderate and this improvement in effectiveness relating to MFIs has been questioned in a subsequent review [ 10 ]. In updating the review, we anticipated a larger pool of reviews and an opportunity to consolidate learning from more recent systematic reviews of interventions.

This review updates and extends our previous review of systematic reviews of interventions designed to implement research evidence into clinical practice. To identify potentially relevant peer-reviewed research papers, we developed a comprehensive systematic literature search strategy based on the terms used in the Grimshaw et al. [ 9 ] and Boaz, Baeza and Fraser [ 8 ] overview articles. To ensure optimal retrieval, our search strategy was refined with support from an expert university librarian, considering the ongoing improvements in the development of search filters for systematic reviews since our first review [ 11 ]. We also wanted to include technology-related terms (e.g. apps, algorithms, machine learning, artificial intelligence) to find studies that explored interventions based on the use of technological innovations as mechanistic tools for increasing the use of evidence into practice (see Additional file 1 : Appendix A for full search strategy).

The search was performed in June 2022 in the following electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched for articles published since the 2011 review. We searched from January 2010 up to June 2022 and applied no language restrictions. Reference lists of relevant papers were also examined.

We uploaded the results using EPPI-Reviewer, a web-based tool that facilitated semi-automation of the screening process and removal of duplicate studies. We made particular use of a priority screening function to reduce screening workload and avoid ‘data deluge’ [ 12 ]. Through machine learning, one reviewer screened a smaller number of records ( n  = 1200) to train the software to predict whether a given record was more likely to be relevant or irrelevant, thus pulling the relevant studies towards the beginning of the screening process. This automation did not replace manual work but helped the reviewer to identify eligible studies more quickly. During the selection process, we included studies that looked explicitly at interventions designed to turn research evidence into practice. Studies were included if they met the following pre-determined inclusion criteria:

The study was a systematic review

Search terms were included

Focused on the implementation of research evidence into practice

The methodological quality of the included studies was assessed as part of the review

Study populations included healthcare providers and patients. The EPOC taxonomy [ 13 ] was used to categorise the strategies. The EPOC taxonomy has four domains: delivery arrangements, financial arrangements, governance arrangements and implementation strategies. The implementation strategies domain includes 20 strategies targeted at healthcare workers. Numerous EPOC strategies were assessed in the review including educational strategies, local opinion leaders, reminders, ICT-focused approaches and audit and feedback. Some strategies that did not fit easily within the EPOC categories were also included. These were social media strategies and toolkits, and multi-faceted interventions (MFIs) (see Table  2 ). Some systematic reviews included comparisons of different interventions while other reviews compared one type of intervention against a control group. Outcomes related to improvements in health care processes or patient well-being. Numerous individual study types (RCT, CCT, BA, ITS) were included within the systematic reviews.

We excluded papers that:

Focused on changing patient rather than provider behaviour

Had no demonstrable outcomes

Made unclear or no reference to research evidence

The last of these criteria was sometimes difficult to judge, and there was considerable discussion amongst the research team as to whether the link between research evidence and practice was sufficiently explicit in the interventions analysed. As we discussed in the previous review [ 8 ] in the field of healthcare, the principle of evidence-based practice is widely acknowledged and tools to change behaviour such as guidelines are often seen to be an implicit codification of evidence, despite the fact that this is not always the case.

Reviewers employed a two-stage process to select papers for inclusion. First, all titles and abstracts were screened by one reviewer to determine whether the study met the inclusion criteria. Two papers [ 14 , 15 ] were identified that fell just before the 2010 cut-off. As they were not identified in the searches for the first review [ 8 ] they were included and progressed to assessment. Each paper was rated as include, exclude or maybe. The full texts of 111 relevant papers were assessed independently by at least two authors. To reduce the risk of bias, papers were excluded following discussion between all members of the team. 32 papers met the inclusion criteria and proceeded to data extraction. The study selection procedure is documented in a PRISMA literature flow diagram (see Fig.  1 ). We were able to include French, Spanish and Portuguese papers in the selection reflecting the language skills in the study team, but none of the papers identified met the inclusion criteria. Other non- English language papers were excluded.

figure 1

PRISMA flow diagram. Source: authors

One reviewer extracted data on strategy type, number of included studies, local, target population, effectiveness and scope of impact from the included studies. Two reviewers then independently read each paper and noted key findings and broad themes of interest which were then discussed amongst the wider authorial team. Two independent reviewers appraised the quality of included studies using a Quality Assessment Checklist based on Oxman and Guyatt [ 16 ] and Francke et al. [ 17 ]. Each study was rated a quality score ranging from 1 (extensive flaws) to 7 (minimal flaws) (see Additional file 2 : Appendix B). All disagreements were resolved through discussion. Studies were not excluded in this updated overview based on methodological quality as we aimed to reflect the full extent of current research into this topic.

The extracted data were synthesised using descriptive and narrative techniques to identify themes and patterns in the data linked to intervention strategies, targeted behaviours, study settings and study outcomes.

Thirty-two studies were included in the systematic review. Table 1. provides a detailed overview of the included systematic reviews comprising reference, strategy type, quality score, number of included studies, local, target population, effectiveness and scope of impact (see Table  1. at the end of the manuscript). Overall, the quality of the studies was high. Twenty-three studies scored 7, six studies scored 6, one study scored 5, one study scored 4 and one study scored 3. The primary focus of the review was on reviews of effectiveness studies, but a small number of reviews did include data from a wider range of methods including qualitative studies which added to the analysis in the papers [ 18 , 19 , 20 , 21 ]. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. In this section, we discuss the different EPOC-defined implementation strategies in turn. Interestingly, we found only two ‘new’ approaches in this review that did not fit into the existing EPOC approaches. These are a review focused on the use of social media and a review considering toolkits. In addition to single interventions, we also discuss multi-faceted interventions. These were the most common intervention approach overall. A summary is provided in Table  2 .

Educational strategies

The overview identified three systematic reviews focusing on educational strategies. Grudniewicz et al. [ 22 ] explored the effectiveness of printed educational materials on primary care physician knowledge, behaviour and patient outcomes and concluded they were not effective in any of these aspects. Koota, Kääriäinen and Melender [ 23 ] focused on educational interventions promoting evidence-based practice among emergency room/accident and emergency nurses and found that interventions involving face-to-face contact led to significant or highly significant effects on patient benefits and emergency nurses’ knowledge, skills and behaviour. Interventions using written self-directed learning materials also led to significant improvements in nurses’ knowledge of evidence-based practice. Although the quality of the studies was high, the review primarily included small studies with low response rates, and many of them relied on self-assessed outcomes; consequently, the strength of the evidence for these outcomes is modest. Wu et al. [ 20 ] questioned if educational interventions aimed at nurses to support the implementation of evidence-based practice improve patient outcomes. Although based on evaluation projects and qualitative data, their results also suggest that positive changes on patient outcomes can be made following the implementation of specific evidence-based approaches (or projects). The differing positive outcomes for educational strategies aimed at nurses might indicate that the target audience is important.

Local opinion leaders

Flodgren et al. [ 24 ] was the only systemic review focusing solely on opinion leaders. The review found that local opinion leaders alone, or in combination with other interventions, can be effective in promoting evidence‐based practice, but this varies both within and between studies and the effect on patient outcomes is uncertain. The review found that, overall, any intervention involving opinion leaders probably improves healthcare professionals’ compliance with evidence-based practice but varies within and across studies. However, how opinion leaders had an impact could not be determined because of insufficient details were provided, illustrating that reporting specific details in published studies is important if diffusion of effective methods of increasing evidence-based practice is to be spread across a system. The usefulness of this review is questionable because it cannot provide evidence of what is an effective opinion leader, whether teams of opinion leaders or a single opinion leader are most effective, or the most effective methods used by opinion leaders.

Pantoja et al. [ 26 ] was the only systemic review focusing solely on manually generated reminders delivered on paper included in the overview. The review explored how these affected professional practice and patient outcomes. The review concluded that manually generated reminders delivered on paper as a single intervention probably led to small to moderate increases in adherence to clinical recommendations, and they could be used as a single quality improvement intervention. However, the authors indicated that this intervention would make little or no difference to patient outcomes. The authors state that such a low-tech intervention may be useful in low- and middle-income countries where paper records are more likely to be the norm.

ICT-focused approaches

The three ICT-focused reviews [ 14 , 27 , 28 ] showed mixed results. Jamal, McKenzie and Clark [ 14 ] explored the impact of health information technology on the quality of medical and health care. They examined the impact of electronic health record, computerised provider order-entry, or decision support system. This showed a positive improvement in adherence to evidence-based guidelines but not to patient outcomes. The number of studies included in the review was low and so a conclusive recommendation could not be reached based on this review. Similarly, Brown et al. [ 28 ] found that technology-enabled knowledge translation interventions may improve knowledge of health professionals, but all eight studies raised concerns of bias. The De Angelis et al. [ 27 ] review was more promising, reporting that ICT can be a good way of disseminating clinical practice guidelines but conclude that it is unclear which type of ICT method is the most effective.

Audit and feedback

Sykes, McAnuff and Kolehmainen [ 29 ] examined whether audit and feedback were effective in dementia care and concluded that it remains unclear which ingredients of audit and feedback are successful as the reviewed papers illustrated large variations in the effectiveness of interventions using audit and feedback.

Non-EPOC listed strategies: social media, toolkits

There were two new (non-EPOC listed) intervention types identified in this review compared to the 2011 review — fewer than anticipated. We categorised a third — ‘care bundles’ [ 36 ] as a multi-faceted intervention due to its description in practice and a fourth — ‘Technology Enhanced Knowledge Transfer’ [ 28 ] was classified as an ICT-focused approach. The first new strategy was identified in Bhatt et al.’s [ 30 ] systematic review of the use of social media for the dissemination of clinical practice guidelines. They reported that the use of social media resulted in a significant improvement in knowledge and compliance with evidence-based guidelines compared with more traditional methods. They noted that a wide selection of different healthcare professionals and patients engaged with this type of social media and its global reach may be significant for low- and middle-income countries. This review was also noteworthy for developing a simple stepwise method for using social media for the dissemination of clinical practice guidelines. However, it is debatable whether social media can be classified as an intervention or just a different way of delivering an intervention. For example, the review discussed involving opinion leaders and patient advocates through social media. However, this was a small review that included only five studies, so further research in this new area is needed. Yamada et al. [ 31 ] draw on 39 studies to explore the application of toolkits, 18 of which had toolkits embedded within larger KT interventions, and 21 of which evaluated toolkits as standalone interventions. The individual component strategies of the toolkits were highly variable though the authors suggest that they align most closely with educational strategies. The authors conclude that toolkits as either standalone strategies or as part of MFIs hold some promise for facilitating evidence use in practice but caution that the quality of many of the primary studies included is considered weak limiting these findings.

Multi-faceted interventions

The majority of the systematic reviews ( n  = 20) reported on more than one intervention type. Some of these systematic reviews focus exclusively on multi-faceted interventions, whilst others compare different single or combined interventions aimed at achieving similar outcomes in particular settings. While these two approaches are often described in a similar way, they are actually quite distinct from each other as the former report how multiple strategies may be strategically combined in pursuance of an agreed goal, whilst the latter report how different strategies may be incidentally used in sometimes contrasting settings in the pursuance of similar goals. Ariyo et al. [ 35 ] helpfully summarise five key elements often found in effective MFI strategies in LMICs — but which may also be transferrable to HICs. First, effective MFIs encourage a multi-disciplinary approach acknowledging the roles played by different professional groups to collectively incorporate evidence-informed practice. Second, they utilise leadership drawing on a wide set of clinical and non-clinical actors including managers and even government officials. Third, multiple types of educational practices are utilised — including input from patients as stakeholders in some cases. Fourth, protocols, checklists and bundles are used — most effectively when local ownership is encouraged. Finally, most MFIs included an emphasis on monitoring and evaluation [ 35 ]. In contrast, other studies offer little information about the nature of the different MFI components of included studies which makes it difficult to extrapolate much learning from them in relation to why or how MFIs might affect practice (e.g. [ 28 , 38 ]). Ultimately, context matters, which some review authors argue makes it difficult to say with real certainty whether single or MFI strategies are superior (e.g. [ 21 , 27 ]). Taking all the systematic reviews together we may conclude that MFIs appear to be more likely to generate positive results than single interventions (e.g. [ 34 , 45 ]) though other reviews should make us cautious (e.g. [ 32 , 43 ]).

While multi-faceted interventions still seem to be more effective than single-strategy interventions, there were important distinctions between how the results of reviews of MFIs are interpreted in this review as compared to the previous reviews [ 8 , 9 ], reflecting greater nuance and debate in the literature. This was particularly noticeable where the effectiveness of MFIs was compared to single strategies, reflecting developments widely discussed in previous studies [ 10 ]. We found that most systematic reviews are bounded by their clinical, professional, spatial, system, or setting criteria and often seek to draw out implications for the implementation of evidence in their areas of specific interest (such as nursing or acute care). Frequently this means combining all relevant studies to explore the respective foci of each systematic review. Therefore, most reviews we categorised as MFIs actually include highly variable numbers and combinations of intervention strategies and highly heterogeneous original study designs. This makes statistical analyses of the type used by Squires et al. [ 10 ] on the three reviews in their paper not possible. Further, it also makes extrapolating findings and commenting on broad themes complex and difficult. This may suggest that future research should shift its focus from merely examining ‘what works’ to ‘what works where and what works for whom’ — perhaps pointing to the value of realist approaches to these complex review topics [ 48 , 49 ] and other more theory-informed approaches [ 50 ].

Some reviews have a relatively small number of studies (i.e. fewer than 10) and the authors are often understandably reluctant to engage with wider debates about the implications of their findings. Other larger studies do engage in deeper discussions about internal comparisons of findings across included studies and also contextualise these in wider debates. Some of the most informative studies (e.g. [ 35 , 40 ]) move beyond EPOC categories and contextualise MFIs within wider systems thinking and implementation theory. This distinction between MFIs and single interventions can actually be very useful as it offers lessons about the contexts in which individual interventions might have bounded effectiveness (i.e. educational interventions for individual change). Taken as a whole, this may also then help in terms of how and when to conjoin single interventions into effective MFIs.

In the two previous reviews, a consistent finding was that MFIs were more effective than single interventions [ 8 , 9 ]. However, like Squires et al. [ 10 ] this overview is more equivocal on this important issue. There are four points which may help account for the differences in findings in this regard. Firstly, the diversity of the systematic reviews in terms of clinical topic or setting is an important factor. Secondly, there is heterogeneity of the studies within the included systematic reviews themselves. Thirdly, there is a lack of consistency with regards to the definition and strategies included within of MFIs. Finally, there are epistemological differences across the papers and the reviews. This means that the results that are presented depend on the methods used to measure, report, and synthesise them. For instance, some reviews highlight that education strategies can be useful to improve provider understanding — but without wider organisational or system-level change, they may struggle to deliver sustained transformation [ 19 , 44 ].

It is also worth highlighting the importance of the theory of change underlying the different interventions. Where authors of the systematic reviews draw on theory, there is space to discuss/explain findings. We note a distinction between theoretical and atheoretical systematic review discussion sections. Atheoretical reviews tend to present acontextual findings (for instance, one study found very positive results for one intervention, and this gets highlighted in the abstract) whilst theoretically informed reviews attempt to contextualise and explain patterns within the included studies. Theory-informed systematic reviews seem more likely to offer more profound and useful insights (see [ 19 , 35 , 40 , 43 , 45 ]). We find that the most insightful systematic reviews of MFIs engage in theoretical generalisation — they attempt to go beyond the data of individual studies and discuss the wider implications of the findings of the studies within their reviews drawing on implementation theory. At the same time, they highlight the active role of context and the wider relational and system-wide issues linked to implementation. It is these types of investigations that can help providers further develop evidence-based practice.

This overview has identified a small, but insightful set of papers that interrogate and help theorise why, how, for whom, and in which circumstances it might be the case that MFIs are superior (see [ 19 , 35 , 40 ] once more). At the level of this overview — and in most of the systematic reviews included — it appears to be the case that MFIs struggle with the question of attribution. In addition, there are other important elements that are often unmeasured, or unreported (e.g. costs of the intervention — see [ 40 ]). Finally, the stronger systematic reviews [ 19 , 35 , 40 , 43 , 45 ] engage with systems issues, human agency and context [ 18 ] in a way that was not evident in the systematic reviews identified in the previous reviews [ 8 , 9 ]. The earlier reviews lacked any theory of change that might explain why MFIs might be more effective than single ones — whereas now some systematic reviews do this, which enables them to conclude that sometimes single interventions can still be more effective.

As Nilsen et al. ([ 6 ] p. 7) note ‘Study findings concerning the effectiveness of various approaches are continuously synthesized and assembled in systematic reviews’. We may have gone as far as we can in understanding the implementation of evidence through systematic reviews of single and multi-faceted interventions and the next step would be to conduct more research exploring the complex and situated nature of evidence used in clinical practice and by particular professional groups. This would further build on the nuanced discussion and conclusion sections in a subset of the papers we reviewed. This might also support the field to move away from isolating individual implementation strategies [ 6 ] to explore the complex processes involving a range of actors with differing capacities [ 51 ] working in diverse organisational cultures. Taxonomies of implementation strategies do not fully account for the complex process of implementation, which involves a range of different actors with different capacities and skills across multiple system levels. There is plenty of work to build on, particularly in the social sciences, which currently sits at the margins of debates about evidence implementation (see for example, Normalisation Process Theory [ 52 ]).

There are several changes that we have identified in this overview of systematic reviews in comparison to the review we published in 2011 [ 8 ]. A consistent and welcome finding is that the overall quality of the systematic reviews themselves appears to have improved between the two reviews, although this is not reflected upon in the papers. This is exhibited through better, clearer reporting mechanisms in relation to the mechanics of the reviews, alongside a greater attention to, and deeper description of, how potential biases in included papers are discussed. Additionally, there is an increased, but still limited, inclusion of original studies conducted in low- and middle-income countries as opposed to just high-income countries. Importantly, we found that many of these systematic reviews are attuned to, and comment upon the contextual distinctions of pursuing evidence-informed interventions in health care settings in different economic settings. Furthermore, systematic reviews included in this updated article cover a wider set of clinical specialities (both within and beyond hospital settings) and have a focus on a wider set of healthcare professions — discussing both similarities, differences and inter-professional challenges faced therein, compared to the earlier reviews. These wider ranges of studies highlight that a particular intervention or group of interventions may work well for one professional group but be ineffective for another. This diversity of study settings allows us to consider the important role context (in its many forms) plays on implementing evidence into practice. Examining the complex and varied context of health care will help us address what Nilsen et al. ([ 6 ] p. 1) described as, ‘society’s health problems [that] require research-based knowledge acted on by healthcare practitioners together with implementation of political measures from governmental agencies’. This will help us shift implementation science to move, ‘beyond a success or failure perspective towards improved analysis of variables that could explain the impact of the implementation process’ ([ 6 ] p. 2).

This review brings together 32 papers considering individual and multi-faceted interventions designed to support the use of evidence in clinical practice. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been conducted. As a whole, this substantial body of knowledge struggles to tell us more about the use of individual and MFIs than: ‘it depends’. To really move forwards in addressing the gap between research evidence and practice, we may need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of perspectives, especially from the social, economic, political and behavioural sciences in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed. Harvey et al. [ 53 ] suggest that when context is likely to be critical to implementation success there are a range of primary research approaches (participatory research, realist evaluation, developmental evaluation, ethnography, quality/ rapid cycle improvement) that are likely to be appropriate and insightful. While these approaches often form part of implementation studies in the form of process evaluations, they are usually relatively small scale in relation to implementation research as a whole. As a result, the findings often do not make it into the subsequent systematic reviews. This review provides further evidence that we need to bring qualitative approaches in from the periphery to play a central role in many implementation studies and subsequent evidence syntheses. It would be helpful for systematic reviews, at the very least, to include more detail about the interventions and their implementation in terms of how and why they worked.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Before and after study

Controlled clinical trial

Effective Practice and Organisation of Care

High-income countries

Information and Communications Technology

Interrupted time series

Knowledge translation

Low- and middle-income countries

Randomised controlled trial

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The authors would like to thank Professor Kathryn Oliver for her support in the planning the review, Professor Steve Hanney for reading and commenting on the final manuscript and the staff at LSHTM library for their support in planning and conducting the literature search.

This study was supported by LSHTM’s Research England QR strategic priorities funding allocation and the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. Grant number NIHR200152. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care or Research England.

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Boaz, A., Baeza, J., Fraser, A. et al. ‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice. Implementation Sci 19 , 15 (2024). https://doi.org/10.1186/s13012-024-01337-z

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Reframing Educational Outcomes: Moving beyond Achievement Gaps

  • Sarita Y. Shukla
  • Elli J. Theobald
  • Joel K. Abraham
  • Rebecca M. Price

School of Educational Studies, University of Washington, Bothell, Bothell, WA 98011-8246

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Department of Biology, University of Washington, Seattle, Seattle, WA 98195

Department of Biological Science, California State University–Fullerton, Fullerton, CA 92831

*Address correspondence to: Rebecca M. Price ( E-mail Address: [email protected] )

School of Interdisciplinary Arts & Sciences, University of Washington, Bothell, Bothell, WA 98011-8246

The term “achievement gap” has a negative and racialized history, and using the term reinforces a deficit mindset that is ingrained in U.S. educational systems. In this essay, we review the literature that demonstrates why “achievement gap” reflects deficit thinking. We explain why biology education researchers should avoid using the phrase and also caution that changing vocabulary alone will not suffice. Instead, we suggest that researchers explicitly apply frameworks that are supportive, name racially systemic inequities and embrace student identity. We review four such frameworks—opportunity gaps, educational debt, community cultural wealth, and ethics of care—and reinterpret salient examples from biology education research as an example of each framework. Although not exhaustive, these descriptions form a starting place for biology education researchers to explicitly name systems-level and asset-based frameworks as they work to end educational inequities.

INTRODUCTION

Inequities plague educational systems in the United States, from pre-K through graduate school. Many of these inequities exist along racial, gender, and socioeconomic lines ( Kozol, 2005 ; Sadker et al. , 2009 ), and they impact the educational outcomes of students. For decades, education research has focused on comparisons of these educational outcomes, particularly with respect to test scores of students across racial and ethnic identities. The persistent differences in these test scores or other outcomes are often referred to as “achievement gaps,” which in turn serve as the basis for numerous educational policy and structural changes ( Carey, 2014 ).

A recent essay in CBE—Life Sciences Education ( LSE ) questioned narrowly defining “success” in educational settings ( Weatherton and Schussler, 2021 ). The authors posit that success must be defined and contextualized, and they asked the community to recognize the racial undercurrents associated with defining success as limited to high test scores and grade point averages (GPAs; Weatherton and Schussler, 2021 ). In this essay, we make a complementary point. We contend that the term “achievement gap” is misaligned with the intent and focus of recent biology education research. We base this realization on the fact that the term “achievement gap” can have a deeper meaning than documenting a difference among otherwise equal groups ( Kendi, 2019 ; Gouvea, 2021 ). It triggers deficit thinking ( Quinn, 2020 ); unnecessarily centers middle and upper class, White, male students as the norm ( Milner, 2012 ); and downplays the impact of structural inequities ( Ladson-Billings, 2006 ; Carter and Welner, 2013 ).

This essay unpacks the negative consequences of using the term “achievement gap” when comparing student learning across different racial groups. We advocate for abandoning the term. Similarly, we suggest that, in addition to changing our terminology, biology education researchers can explicitly apply theoretical frameworks that are more appropriate for interrogating inequities among educational outcomes across students from different demographics. We emphasize that the idea that a simple “find and replace,” swapping out the term “achievement gap” for other phrases, is not sufficient.

In the heart of this essay, we review some of these systems-level and asset-based frameworks for research that explores differences in academic performance ( Figure 1 ): opportunity gaps ( Carter and Welner, 2013 ), educational debt ( Ladson-Billings, 2006 ), community cultural wealth ( Yosso, 2005 ), and ethics of care ( Noddings, 1988 ). Within each of these frameworks, we review examples of biology education literature that we believe rely on them, explicitly or implicitly. We conclude by reiterating the need for education researchers to name explicitly the systems-level and asset-based frameworks used in future research.

FIGURE 1. Research frameworks highlighted in the essay. The column in gray summarizes deficit-based frameworks that focus on achievement gaps. The middle column (in gold) includes examples of systems-based frameworks that acknowledge that student learning is associated with society-wide habits. The rightmost columns (in peach) include examples of asset-based models that associate student learning with students’ strengths. The columns are not mutually exclusive, in that studies can draw from multiple frameworks simultaneously or sequentially.

We will use the phrase “students from historically or currently marginalized groups” to describe the students who have been and still are furthest from the center of educational justice. However, when discussing work of other researchers, we will use the terminology they use in their papers. Our conceptualization of this phrase matches, as near as we can tell, Asai’s phrase “PEERs—persons excluded for their ethnicity or race” ( Asai, 2020 , p. 754). We also choose to capitalize “White” to acknowledge that people in this category have a visible racial identity ( Painter, 2020 ).

Positionality

Our positionalities—our unique life experiences and identities—mediate our understanding of the world ( Takacs, 2003 ). What we see as salient in our research situation arises from our own life experiences. Choices in our research, including the types of data we collect and how we clean the data and prepare it for analysis, adopt analytical tools, and make sense of these analyses are important decision points that affect study results and our findings ( Huntington-Klein et al. , 2021 ). We recognize that it is impossible to be free of bias ( Noble, 2018 ; Obermeyer et al. , 2019 ). Therefore, we put forth our positionality to acknowledge the lenses through which we make decisions as researchers and to forefront the impact of our identities on our research. Still, the breadth of our experiences cannot be described fully in a few sentences.

The four authors of this essay have unique and complementary life experiences that contribute to the sense-making presented in this essay. S.Y.S. has been teaching since 2003 and teaching in higher education since 2012. She is a South Asian immigrant to the United States, and a cisgender woman. E.J.T. has taught middle school, high school, and college science since 2006. She is a cisgender White woman. J.K.A. is a cisgender Black mixed-race man who comes from a family of relatively recent immigrants with different educational paths. He has worked in formal and informal education since 2000. R.M.P. is a cisgender Jewish, White woman, and she has been teaching college since 2006. We represent a team of people who explicitly acknowledge that our experiences influence the lenses through which we work. Our guiding principles are 1) progress over perfection, 2) continual reflection and self-improvement, and 3) deep care for students. These principles guide our research and teaching, impacting our interactions with colleagues (faculty and staff) as well as students. Ultimately, these principles motivate us to make ourselves aware of, reflect on, and learn from our mistakes.

Simply Changing Vocabulary Does Not Suffice

The term “achievement gap” is used in research that examines differences in achievement—commonly defined as differences in test scores—across students from different demographic groups ( Coleman et al. , 1966 ). Some studies replace “achievement gap” with “score gap” (e.g., Jencks and Phillips, 2006 ), because it defines the type of achievement under consideration; others use “opportunity gap,” because it emphasizes differences in opportunities students have had throughout their educational history (e.g., Carter and Welner, 2013 ; more on opportunity gaps later). The shift for which we advocate, however, does not reside only with terminology. Instead, we call for a deeper shift of using research frameworks that acknowledge and respect students’ histories and empower them now.

The underlying framework in research that uses “achievement gap” or even “score gap” may not be immediately apparent. Take for example two studies that both use the seemingly benign term, “score gap.” A close read indicates that one study attributed the difference in test scores between Black and White students to deficient “culture and child-rearing practices” ( Farkas, 2004 , p. 18). Thus, even though the researcher uses what can be considered to be more neutral terminology, the phrase in this context represents deficit thinking and blame. On the other hand, another study uses the term “score gap” to explore differences that have been historically studied through cultures of poverty, genetic, and familial backgrounds ( Jencks and Phillips, 2006 ). While these researchers discuss the Black–White score gap, they present evidence that examines this phenomenon with nuanced constructs, such as stereotype threat ( Steele, 2011 ) and resources available. These authors also mention ways to reduce score gaps, such as smaller class sizes and high teacher expectations ( Jencks and Phillips, 2006 ).

Some researchers who use the phrase “achievement gap” explicitly avoid deficit thinking and instead embrace an asset-based framework. Jordt et al. (2017) address systemic racism, just as Jencks and Phillips (2006) do. Specifically, Jordt et al. (2017) identified an intervention that affirmed student values that might also be a potential tool for increasing underrepresented minority (URM) student exam scores in college-level introductory science courses. The researchers found that this intervention produced a 4.2% increase in exam performance for male URM students and a 2.2% increase for female URM students. Thus, while they use “achievement gap” throughout the paper to refer to racial and gender differences in exam scores, the study focused on ways to support URM student success.

In pursuit of improved language and clarity of intent, the term “achievement gap” should be replaced to reflect the research framework used to interrogate educational outcomes within and across demographic groups.

DEFICIT THINKING

Deficit thinking describes a mindset, or research framework, in which differences in outcomes between members of different groups, generally a politically and economically dominant group and an oppressed group, are attributed to a quality that is lacking in the biology, culture, or mindset of the oppressed group ( Valencia, 1997 ). Deficit thinking has pervaded public and academic discourse about the education of students from different races and ethnicities in the United States for centuries ( Menchaca, 1997 ).

Tenacious deficit-based explanations blame students from historically or currently marginalized groups for lower educational attainment. These falsities include biological inferiority due to brain size or structure ( Menchaca, 1997 ), negative cultural attributes such as inferior language acquisition ( Dudley-Marling, 2007 ), and accumulated deficits due to a “culture of poverty” ( Pearl, 1997 ; Gorski, 2016 ). More recently, lower achievement has been attributed to a lack of “grit” ( Ris, 2015 ) or the propensity for a “fixed” mindset ( Gorski, 2016 ; Tewell, 2020 ). While ideas around grit and mindset have demonstrable value in certain circumstances (e.g., Hacisalihoglu et al. , 2020 ), they fall short as primary explanations for differences in educational outcomes, because they focus attention on perceived deficits of students while providing little information about structural influences on failure and success, including how we define those constructs ( Harper, 2010 ; Gorski, 2016 ). In other words, deficit models often posit students as the people responsible for improving their own educational outcomes ( Figure 1 ).

Deficit thinking, regardless of intent, blames individuals, their families, their schools, or their greater communities for the consequences of societal inequities ( Yosso, 2006 ; Figure 1 ). This blame ignores the historic and structural drivers of inequity in our society, placing demands on members of underserved groups to adapt to unfair systems ( Valencia, 1997 ). A well-documented example of structural inequity is the consistent underresourcing of public schools that serve primarily students of color and children from lower socioeconomic backgrounds ( Darling-Hammond, 2013 ; Rothstein, 2013 ). Because learning is heavily influenced by factors outside the school environment, such as food security, trauma, and health ( Rothstein, 2013 ), schools themselves reflect gross disparities in resourcing based on historic discrimination ( Darling-Hammond, 2013 ). Deficit thinking focuses on student or cultural characteristics to explain performance differences and tends to overlook or minimize the impacts of systemic disparities. Deficit thinking also strengthens the narrative around student groups in terms of shortcomings, reinforces negative stereotypes, and ignores successes or drivers of success in those same groups ( Harper, 2015 ).

Achievement Gaps

The term “achievement gap” has historically described the difference in scores attained by students from racial and ethnic minority groups compared with White students on standardized tests or course exams ( Coleman et al. , 1966 ). As students from other historically or currently marginalized groups, such as female or first-generation students, are increasingly centered in research, the term is now used more broadly to compare any student population to White, middle and upper class, men ( Harper, 2010 ; Milner, 2012 ). Using White men as the basis for comparison comes at the expense of students from other groups ( Harper, 2010 ; Milner, 2012 ). Basing comparisons on the cultural perspectives of a single dominant group leads to “differences” being interpreted as “deficits,” which risks dehumanizing people in the marginalized groups ( Dinishak, 2016 ). Furthermore, centering White, wealthy, male performance means that even students from groups that tend to have higher test scores, like Asian-American students, risk dehumanization as “model minorities” or “just good at math” ( Shah, 2019 ).

Many researchers have highlighted the fact that the term “achievement gap” is a part of broader deficit-thinking models and rooted in racial hierarchy ( Ladson-Billings, 2006 ; Gutiérrez, 2008 ; Martin, 2009 ; Milner, 2012 ; Kendi, 2019 ). Focusing on achievement gaps emphasizes between-group differences over within-group differences ( Young et al. , 2017 ), reifies sociopolitical and historical groupings of people ( Martin, 2009 ), and minimizes attention to structural inequalities in education ( Ladson-Billings, 2006 ; Alliance to Reclaim Our Schools, 2018 ). Gutiérrez (2008) names this obsession with achievement gaps as a “gap-gazing fetish” that draws attention away from finding solutions that promote equitable learning ( Gutiérrez, 2008 ). Under a deficit-thinking model, achievement gaps are viewed as the primary problem, rather than a symptom of the problem ( Gutiérrez, 2008 ), and for decades they have been attributed to different characteristics of the demographics being compared ( Valencia, 1997 ). As such, proposed solutions tend to be couched in terms of remediation for students ( Figure 1 ).

Ignoring the social context of students’ education necessarily limits inferences that can be drawn about their success. Limiting measures of educational success, also conceptualized as achievement, to performance on exams or overall college GPA, often leaves out consideration of other potential data sources ( Weatherton and Schussler, 2021 ; Figure 2 ). This narrow perspective tends to perpetuate the systems of power and privilege that are already in place ( Gutiérrez, 2008 ). The biology education research community can instead broaden its sense of success to recognize the underlying historical and current contexts and the intersections of identities (e.g., racial, gender, socioeconomic) that contribute to those differences ( Weatherton and Schussler, 2021 ).

FIGURE 2. A selection of potential data sources that could inform researchers about within- and between-group differences in educational outcomes. This list does not encompass the full range of possible data sources, nor does it imply a hierarchy to the data. Instead, it reflects some of the diversity of quantitative and qualitative data that are directly linked to student outcomes and that are used under multiple research frameworks.

In biology education research, many papers still use the language of “achievement gap,” even in instances when researchers explicitly or implicitly use other nondeficit frameworks. While some may argue that this language merely describes a pattern, its origin and history is explicitly and inextricably linked to deficit-thinking models ( Gutiérrez, 2008 ; Milner, 2012 ). Thus, we join others in the choice to abandon the term “achievement gap” in favor of language—and frameworks—that align better to the goals of our research and to avoid the limitations and harm that can arise through its use.

Example: Focusing on Achievement Gaps Can Reinforce Racial Stereotypes

Messages of perpetual underachievement can inadvertently reinforce negative stereotypes. For example, Quinn (2020) demonstrated that, when participants watched a 2-minute video of a newscast using the term “achievement gap,” they disproportionately underpredicted the graduation rate of Black students relative to White students, even more so than participants in a control group who watched a counter-stereotypical video. They also scored significantly higher on an instrument measuring bias. Because bias is dynamic and affected by the environment, Quinn concludes that the video discussing the achievement gap likely heightened the bias of the participants ( Quinn, 2020 ).

Education researchers, just like the participants in Quinn’s (2020) study, inadvertently carry implicit bias against students from the different groups they study, and those biases can shift depending on context. Quinn (2020) demonstrates that just using the term “achievement gap” can reinforce the pervasive racial hierarchy that places Black students at the bottom. Researchers, without intending to, can be complicit in a system of White privilege and power if the language and frameworks underlying their study design, data collection, and/or data interpretation are aligned with bias and stereotype. If the goal is to dismantle inequities in our educational systems and research on those systems, the biology education research community must consider the historical and social weight of its literature to address racism head on, as progressive articles have been doing (e.g., Eddy and Hogan, 2014 ; Canning et al. , 2019 ; Theobald et al. , 2020 ).

SYSTEMS-LEVEL FRAMEWORKS

To move away from the achievement gap discourse—because of the history of the term, the perceived blame toward individual students, as well as the deficit thinking the term may imbue and provoke—we highlight some of the other frameworks for understanding student outcomes. We conclude discussion of each framework with an example from education research that can be reinterpreted within it, keeping in mind that multiple frameworks can be applied to different studies. We acknowledge two caveats about these reinterpretations: first, we are adding another layer of interpretation to the original studies, and we cannot claim that the original authors agree with these interpretations; second, each example could be interpreted through multiple frameworks, especially because these frameworks overlap ( Figure 1 ).

In this section, we begin at the systems level by examining opportunity gaps and educational debt. Rather than blaming students or their cultures for deficits in performance, these systems-level perspectives name white supremacy and the concomitant policies that maintain power imbalances as the cause of disparate student experiences.

Opportunity Gaps

The framework of opportunity gaps shifts the onus of differential student performance away from individual deficiencies and assigns solutions to actions that address systemic racism ( Milner, 2012 ; Figure 1 ). Specifically, opportunity gaps embody the difference in performance between students from historically and currently marginalized groups and middle and upper class, White, male students, with primary emphasis on opportunities that students have or have not had, rather than on their current performance (i.e., achievement) in a class ( Milner, 2012 ). Compared with deficit models, the focus shifts from assigning responsibility for the gap from the individual to society ( Figure 1 ).

Some researchers explore opportunity gaps by discussing the structural challenges that students from historically and currently marginalized groups have been facing (e.g., Rothstein, 2013 ). For example, poor funding in K–12 schools leads to inconsistent, poorly qualified, and poorly compensated teachers; few and outdated textbooks ( Darling-Hammond, 2013 ); limited field trips; a lack of extracurricular resources ( Rothstein, 2013 ); and inadequately supplied and cleaned bathrooms ( Darling-Hammond, 2013 ). Additional structural challenges that occur outside school buildings, but impact learning, include poor health and lack of medical care, food and housing insecurity, lead poisoning and iron deficiency, asthma, and depression ( Rothstein, 2013 ).

While the literature about opportunity gaps focuses more on K–12 than higher education ( Carter and Welner, 2013 ), college instructors can exacerbate opportunity gaps by biasing who has privilege (i.e., opportunities) in their classrooms. For example, some biology education literature focuses on how instructors’ implicit biases impact our students, such as by unconsciously elevating the status of males in the classroom ( Eddy et al. , 2014 ; Grunspan et al. , 2016 ).

Example: CUREs Can Prevent Opportunity Gaps.

Course-based undergraduate research experiences (CUREs) are one way to prevent opportunity gaps (e.g., Bangera and Brownell, 2014 ; CUREnet, n.d. ). Specifically, we interpret the suggestions that Bangera and Brownell (2014) make about building CUREs as a way to recognize that some students have the opportunity to participate in undergraduate research experiences while others do not. For example, students who access extracurricular research opportunities are likely relatively comfortable talking to faculty and, in many cases, have the financial resources to pursue unpaid laboratory positions ( Bangera and Brownell, 2014 ). More broadly, when research experiences occur outside the curriculum, they privilege students who know how to pursue and gain access to them. However, CUREs institutionalize the opportunity to conduct research, so that every student benefits from conducting research while pursuing an undergraduate degree.

Educational Debt

Ladson-Billings (2006) submits that American society has an educational debt, rather than an educational deficit. This framework shifts the work of finding solutions to educational inequities away from individuals and onto systems ( Figure 1 ). The metaphor is economic: A deficit refers to current mismanagement of funds, but a debt is the systematic accumulation of mismanagement over time. Therefore, differences in student performances are framed by a history that reflects amoral, systemic, sociopolitical, and economic inequities. Ladson-Billing ( 2006 ) suggests that focusing on debts highlights injustices that Black, Latina/o, and recent immigrant students have incurred: Focusing on student achievement in the absence of a discussion of past injustices does not redress the ways in which students and their parents have been denied access to educational opportunities, nor does it redress the ways in which structural and institutional racism dictate differences in performance. This approach begins by acknowledging the structural and institutional barriers to achievement in order to dismantle existing inequities. This reframing helps set the scope of the problem and identify a more accurate and just lens through which we make sense of the problem ( Cho et al. , 2013 ).

Example: NSF Supports Historically Black Colleges and Universities.

From my own (yet to be published) research, a participant described the HBCU where he studied physics as providing a “dome of security and safety.” In contrast, he recounted that when he attended a predominantly White institution, he constantly needed to be guarded and employ “his body sense,” an act that made him tense, defensive, and unable to listen. ( Rankins, 2019 , p. 50)

Example: Institutions Can Repay Educational Debt.

Institutions can repay educational debt by ensuring that their students have the resources and support structures necessary to succeed. The Biology Scholars Program at the University of California, Berkeley, is a prime example ( Matsui et al. , 2003 ; Estrada et al. , 2019 ). This program, begun in 1992 ( Matsui et al. , 2003 ) and still going strong ( Berkeley Biology Scholars Program, n.d. ), creates physical and psychological spaces that support learning: a study space and study groups, paid research experiences, and thoughtful mentoring. The students recruited to the program are from first-generation, low-socioeconomic status backgrounds and from groups that are historically underrepresented. When the students enter college, they have lower GPAs and Scholastic Aptitude Test scores than their counterparts with the same demographic profile who are not in the program. And yet, when they graduate, students in the Biology Scholars Program have higher GPAs and higher retention in biology majors than their counterparts ( Matsui et al. , 2003 ), perhaps because of the extended social support they receive from peers ( Estrada et al. , 2021 ). Moreover, students in this program report lower levels of stress and a greater sense of well-being ( Estrada et al. , 2019 ).

ASSET-BASED FRAMEWORKS

In this section, we continue to explore frameworks that move away from the achievement gap discourse, now focusing on models that build from students’ strengths. We have chosen two frameworks whose implications seem particularly relevant to and coincident with anti-racist research in biology education: community cultural wealth ( Yosso, 2005 ) and ethics of care ( Noddings, 1988 ). As before, we reinterpret articles from the education literature to illustrate these frameworks, and we once again include the caveats that we extend beyond the authors’ original interpretations and that other frameworks could also be used to reinterpret the examples.

Community Cultural Wealth

One asset-based way to frame student outcomes is to begin with the strengths that people from different demographic groups hold ( Yosso, 2005 ). Rather than focusing on racism, this approach focuses on community cultural wealth. The premise is that everyone can contribute a wealth of knowledge and approaches from their own cultures ( Yosso, 2005 ).

Community cultural wealth begins with critical race theory (CRT; Yosso, 2005 ). CRT illuminates the impact of race and racism embedded in all aspects of life within U.S. society ( Omi and Winant, 2014 ). CRT acknowledges that racism is interconnected with the founding of the United States. Race is viewed in tandem with intersecting identities that oppose dominant ones, and the constructs of CRT emerge by attending to the experiences of people from communities of color ( Yosso, 2005 ). Therefore, the experiences of students of color are central to transformative education that addresses the overrepresentation of White philosophies. CRT calls on research to validate and center these perspectives to develop a critical understanding about racism.

Community cultural wealth builds on these ideas by viewing communities of color as a source of students’ strength ( Yosso, 2005 ). The purpose of schooling is to build on the strengths that students have when they arrive, rather than to treat students as voids that need to be filled: students’ cultural wealth must be acknowledged, affirmed, and amplified through their education. This approach is consistent with those working to decolonize scientific knowledge (e.g., Howard and Kern, 2019 ).

Example: Community Cultural Wealth Can Improve Mentoring.

Thompson and Jensen-Ryan (2018) offer advice to mentors about how to use cultural wealth to mentor undergraduate students in research. They identify the forms of scientific cultural capital that research mentors typically value, finding that these aspects of a scientific identity are closely associated with majority culture. They challenge mentors to broaden the forms of recognizable capital. For example, members of the faculty can actively recruit students into their labs from programs aimed to promote the diversity of scientists, rather than insisting that students approach them with their interest to work in the lab ( Thompson and Jensen-Ryan, 2018 ). They can recognize that undergraduate students may not express an interest in a research career–especially initially—but that research experience is still formative. They can recognize that students who are strong mentors to their peers are valuable members of a research team and that this skill is a form of scientific capital. They can value the diverse backgrounds of students in their labs, rather than insisting that they come from families that have prioritized scientific thinking and research. In sum, the gaps that Thompson and Jensen-Ryan (2018) identify are in research mentors’ attitudes, rather than in student performance.

Assets can also be developed in the classroom. We interpret Parnes et al. ’s (2020) analysis of the Connected Scholars program as stemming from community cultural wealth. The Connected Scholars program normalized help-seeking and increased the help network available to first-generation college students, 90% of whom were racial or ethnic minorities, in a 6-week summer program that bridged students from high school to college. First-generation college students were provided explicit instruction on how to sustain these two types of support. The Connected Scholars intervention promoted help-seeking behaviors and seemed to mediate higher GPAs. Additionally, students in the intervention reported through a survey that they had better relationships with their instructors than students in the control group ( Parnes et al. , 2020 ). In other words, cultural wealth can be amplified in college for first-generation students (see also the Biology Scholars Program, discussed in the Opportunity Gaps section; Matsui et al. , 2003 ; Estrada et al. , 2019 ).

Ethics of Care

As a framework, ethics of care complements community cultural wealth, in that both are asset-based. A key difference is that community cultural wealth focuses on the assets that students bring, and ethics of care focuses on the assets that an instructor brings to create a classroom of respect and confidence in students.

A foundation of biology education research is that instructors want their students to learn, and it is buttressed by literature concerning students’ emotional well-being. For example, the field considers how students with disabilities experience active learning ( Gin et al. , 2020 ) and how group work promotes collaboration and learning ( Wilson et al. , 2018 ). Studies like these echo the philosophy of ethics of care developed by Noddings (1988) .

The premises of teaching through the ethics of care are that everyone—including students and instructors—has both an innate desire to learn and the capacity to nurture ( Pang et al. , 2000 ). In teaching, these premises form the basis for student–instructor relationships. Nieto and Bode (2012) caution against the oversimplification that caring means being nice: the ethics of care encompasses niceness, in addition to articulating high standards of performance. Instructors must also support and respect students as they meet those standards, especially when students did not recognize that they could meet those goals at the outset. This framework is about nurturing students to accomplish more than they thought possible.

Combining an inclusive culture, for example, through positive instructor talk ( Seidel et al. , 2015 ; Harrison et al. , 2019 ; Seah et al. , 2021 ), growth mindset ( Canning et al. , 2019 ), or increased course structure ( Eddy and Hogan, 2014 ), with evidence-based practices for teaching content ( Freeman et al. , 2014 ; Theobald et al. , 2020 ) has garnered recent attention as a way to create a powerful ethic of care in classrooms. For example, instructor talk, that is, what instructors say in class other than the content they are teaching, addresses student affect. Seidel et al. (2015) and Harrison et al. (2019) analyzed classroom transcripts to identify different categories of instructor talk. While further research can probe the impacts of instructor talk on student outcomes, the idea is consistent with the principles of ethics of care: for example, one category of talk describes the instructor–student relationship as one of respect, fostered through statements such as “People are bringing different pieces of experience and knowledge into this question and I want to kind of value the different kinds of experience and knowledge that you bring in” ( Seidel et al. , 2015 , p. 6). Instructor talk also generates a classroom culture of support and validation for marginalized students and overall builds classroom community ( Ladson-Billings, 2013 ).

Example: Departments Can Implement Care.

Gutiérrez (2000) presents an example of an entire department applying ethics of care to support how African-American students learn math. This study is an ethnography of a particularly successful STEM magnet program in a public high school with a population that is majority African American. In her analysis of the math department, Gutiérrez avoids the phrase “achievement gap,” while also recognizing that people outside the school assume a deficit model when considering the students . Instead, she illustrates how researchers can use an asset-based lens to build from knowledge about differences in performance ( Gutiérrez, 2000 ).

Gutierrez ( 2000 ) examines pedagogy that supports African-American students. She documents how a culture of excellence is developed within a school setting that promotes student achievement. This culture is complex, in that there are multiple layers of support that provide students with repertoires for advancement ( Gutiérrez, 2000 )—the emphasis is on how teachers create an environment where students are both challenged through the curriculum and supported along the way. The teachers in this study have a dynamic conception of their students, and they demonstrate a unified commitment to support the broadest array of students at their school. The institution itself, represented in part through the departmental chair, has values that empower teachers to support students, proactive commitment from teachers to find innovative practices to serve students, and a supportive chairperson.

The math department exhibited a student-centered approach that epitomizes ethics of care. The teachers in the math department rotated through all of the courses and were therefore familiar with the entire curriculum. This knowledge helped them support one another, sharing successful strategies and working to improve the courses. It set up an environment in which they prioritized making decisions collectively. This collaboration led to a sense of togetherness among teachers and a sense of investment in individual students’ successes. As a result, the teachers decided to remove less-challenging courses from the curriculum and replaced them with more advanced courses—against the recommendations of the school district. The chair of the department worked with the faculty to support student learning, consider course assignments, and choose topics for and frequency of faculty meetings. The chair also attended to teachers’ emotional needs, for example, by talking to teachers every day, working with teachers to determine the best strategies for evaluating teaching practices, and enacting a teaching philosophy that valued problem solving over achieving correct answers.

The support that the teachers provided each other coincided with strong support for students. For example, students attended the magnet program because they were interested in science; they notably did not have to take entrance exams or maintain a certain GPA. If students struggled with a subject, they received tutoring. The teachers also invited graduates of the program to come back and visit, keeping the students motivated by showing them success.

Example: Biology Instructors Can Adopt an Ethics of Care.

In much of the research on differential performance in our field, researchers focus on identifying strategies that help students, regardless of their histories, in their learning success. This asset-based approach acknowledges that students start at different places, but also that instructors can implement strategies that support all students in a trajectory toward common learning goals. This argument is often posited in terms of inclusive teaching (e.g., Dewsbury and Brame, 2019 ).

Some papers that measure the effect of inclusive teaching practices may use “gap” language, perhaps as a historical artifact of our discipline. These papers emphasize the just mission to “close the gap”—or, in anti-deficit language, for all students to learn the material and perform well on assessments. For example, Theobald et al. (2020) conducted a meta-analysis of undergraduate STEM classes, drawing on 26 studies of courses reporting failure rates (44,606 students) and 15 studies (9238 students) that reported exam scores. Within these samples, they compared instruction in lecture format with instruction using active-learning strategies. The analysis compared the success of students from minoritized groups using these two teaching strategies and found conclusive evidence of the efficacy of active teaching for underrepresented student success in STEM courses. The powerful implication of this study is that college STEM instructors can mitigate some of the effects of oppression that students have experienced in their lifetime.

In another study demonstrating the philosophy of ethics of care, Canning et al. (2019) found narrower racial disparities in performance in courses taught by instructors who had a growth mindset about their students’ ability to learn, compared with instructors who viewed level of achievement as fixed. In fact, they found that the instructor mindset had a bigger impact on student performance than other faculty characteristics ( Canning et al. , 2019 ). While they focused on the negative consequences of instructors’ fixed mindset, the corollary is that a growth mindset can reflect an ethics of care that both motivates students and generates a positive classroom environment.

The successful instructors will also work to recognize their implicit biases and to ensure that they support a growth mindset for all students, regardless of demographic. This is particularly relevant, because implicit biases have “more to do with associations we’ve absorbed through history and culture than with explicit racial animus” ( Eberhardt, 2019 , p. 160). Realizing how our own socialization may have conditioned us to automatically produce harmful but hidden narratives warrants our attention ( Eberhardt, 2019 ).

MOVING FORWARD

Ladson-Billings (2006) reframed the performance of students from historically and currently marginalized groups from achievement gap to educational debt; this reframing has contributed to a movement to critically examine the term. At the same time, however, the term “achievement gap” has become a catchall used by researchers untethered from its deeper historical context.

Researchers choose words to describe their research that reflect their personal worldviews and research frameworks; in turn, these worldviews and frameworks influence future researchers. Every discipline grapples with terminology, and phrases that were common historically may fall out of use. In some instances, the terms themselves no longer suffice, so a simple “search and replace” may be all that is required to address the issue. The term “achievement gap,” however, is tied to specific frameworks that need to be acknowledged and redressed; it affects how research is designed, how results are interpreted, and what conclusions are drawn. Simply replacing “achievement gap” would not address the undermining nature of deficit-based research frameworks.

Researchers who used the term “achievement gap” may not have intended to use a deficit-thinking framework in their study. In fact, as we have demonstrated with our examples, some powerful articles exist in biology education research that used the term and also implicitly used one of the systems-level or asset-based frameworks we identified.

In these examples, we have reinterpreted the results of primary research with the frameworks we identified. This leads to two points of caution. The first is that we are adding another layer of interpretation, one that the original authors may not have intended. The second is that each example could be interpreted through multiple frameworks, especially because these frameworks overlap ( Figure 1 ). For example, Bangera and Brownell (2014) identify barriers to participating in independent undergraduate research experiences. Course-based undergraduate research experiences (CUREs) offer research opportunities to students who previously could not access them. As discussed earlier, we posited CUREs as an example of a way to reduce opportunity gaps. However, we could also have interpreted the act of implementing a CURE as repaying an educational debt by repairing a form of bias typical within the academy ( Figure 1 ).

Addressing educational inequities requires that biology education researchers quantify differences in performance across demographic groups ( Figure 2 ) and must be done with the utmost care. Disaggregating data is necessary, as is analyzing those data with a just framework that dismantles racial hierarchies and carefully considers the sources of data used to understand those inequities. The frameworks we choose affect our analysis; we must avoid the common trap of assuming that quantitative data and data analysis are free from bias. To illustrate the degree of subjectivity that enters data analysis, Huntington-Klein et al. (2021) found that when seven different researchers received copies of the same data set, each reported different levels of statistical significance, including one researcher who found an effect that was opposite to what the others found. Moving away from analyses based on the phrase “achievement gap” will avoid unintentionally reinforcing the racial bias and better reflect the intention of disaggregating data to quantify differences in performance across demographic groups to actively dismantle persistent educational inequities.

In addition to disaggregating and diversifying data on outcomes ( Figure 2 ), the biology education research community must consider how definitions of success may center White, middle-class ways of knowing and performing ( Weatherton and Schussler, 2021 ). In their recent essay, Weatherton and Schussler (2021) reported that, in articles published in LSE between the years 2015 and 2020, the word “success,” when defined, largely meant high GPAs and exam scores. This narrow definition of success prioritizes scientific content, whereas there are additional admirable goals by which success could be measured ( Figure 2 ; see also Weatherton and Schussler, 2021 and references therein). Moreover, the scientific skills that are valued are Eurocentric, rather embodying a diversity of scientific approaches ( Howard and Kern, 2019 ). In addition to the limitations of narrowly defining success as exam performance, it should be noted that tests themselves are not always fair or equitable across all student populations ( Martinková et al. , 2017 ); success measured in this way should be interpreted with caution, particularly when comparing students across different courses, institutions, or identities.

As we discussed earlier, instructors’ and researchers’ deep beliefs about educational success and achievement necessarily impact their actions. For this reason, we propose that interrogating the frameworks we use is necessary and that such interrogation should acknowledge harm that may have been inflicted. While writing this essay, for example, our understandings of the frameworks underlying our own research, teaching, and other engagements have grown. Much like the research studies we discuss, our intentions, actions, and frameworks can be and have been out of alignment. For example, our own actions with respect to departmental policies, course designs, and program structures have not always reflected the principles to which we subscribe. Although this essay focuses on frameworks in research, we provide a list of some questions that we have asked of ourselves and that could catalyze reflection in all areas of our professional work ( Table 1 ).

In conclusion, we have presented four ways to frame differences in academic performance across students from different demographic groups that firmly reject deficit-based thinking ( Figure 1 ). The notions of opportunity gaps and educational debt demonstrate how systems thinking can recognize socio-environmental barriers to student learning. Asset-based frameworks that include community cultural wealth and ethics of care can help identify actions that institutions, instructors, and students can take to meet learning goals. We hope that researchers in the field move forward by 1) avoiding, or at least minimizing, deficit thinking; 2) explicitly stating asset-based and systems-level frameworks that celebrate students’ accomplishments and move toward justice; and 3) using language consistent with their frameworks.

ACKNOWLEDGMENTS

We thank Starlette Sharp and our external reviewers for helpful feedback on this article. We live and work on the lands of the Kizh/Tongva/Gabrieleño, Duwamish, and Willow (Sammamish) People past, present, and future. We also acknowledge the people whose uncompensated labor built this country, including many of its academic institutions.

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educational achievement research paper

Submitted: 3 June 2021 Revised: 20 December 2021 Accepted: 2 February 2022

© 2022 S. Y. Shukla et al. CBE—Life Sciences Education © 2022 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

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The 2024 Chemistry Olympiad round one results

Deborah van Wyk

The Royal Society of Chemistry reveals the grade boundaries for the first round of the 56th Chemistry Olympiad

A bar chart showing UK Chemistry Olympiad scores. 29.9% of candidates got no award, 36.8% got bronze, 25% got silver and 8.3% got gold with a mark of 30 or more.

Students had to score 10–17 marks for the Bronze award, 18–29 marks for the Silver award and 30 or more for the Gold award

The Royal Society of Chemistry (RSC) has disclosed the 2024 grade boundaries for the first round of the  UK Chemistry Olympiad . On 25 January, 14,915 students from 1,025 schools took part – a fantastic new participation record.

More than 70% of the students who took part achieved Bronze, Silver and Gold awards. To receive a Bronze award, participants had to score 10–17 marks, they needed 18–29 marks for the Silver award and 30 or more marks for the prestigious Gold award. Students can request their scores from their teachers, and pdf certificates will be distributed in March.

The 2024 paper covered topics such as the composition of the FIFA 2023 Women’s World Cup trophy, iodate salts, fuel-producing bacteria, the MRI contrast agent gadopiclenol and sulfur-containing molecules in the atmosphere. This year’s paper was more challenging than last year’s, which is reflected in the grade boundaries, with a decrease in the marks required to obtain each award.

RSC Education executive and competition organiser, Sophie Redman, congratulates and thanks the teachers and students involved: ‘I would like to congratulate all the students who took part in the first round of the UK Chemistry Olympiad. We are delighted to see a big increase in the number of students participating, year on year, and we extend our thanks to all the teachers who gave their time to facilitate this stage of the competition.’

A total of 34 students have been selected for the second round of the competition (one more than last year), which will take take place at the University of Nottingham from 4–7 April. Four standout participants will then go on to represent the UK in the highly prestigious international final , which will take place in take place in Saudi Arabia from 21–30 July.

Past papers

Students who would like to practise answering questions can access past papers with mark schemes with answers. The 2023 question paper, student answer booklet, mark scheme and examiners’ report are now available .

Deborah van Wyk

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  • Published: 21 February 2024

Making cities mental health friendly for adolescents and young adults

  • Pamela Y. Collins   ORCID: orcid.org/0000-0003-3956-448X 1 ,
  • Moitreyee Sinha 2 ,
  • Tessa Concepcion 3 ,
  • George Patton   ORCID: orcid.org/0000-0001-5039-8326 4 ,
  • Thaisa Way 5 ,
  • Layla McCay 6 ,
  • Augustina Mensa-Kwao   ORCID: orcid.org/0000-0001-8136-6108 1 ,
  • Helen Herrman 7 , 8 ,
  • Evelyne de Leeuw 9 ,
  • Nalini Anand 10 ,
  • Lukoye Atwoli 11 ,
  • Nicole Bardikoff 12 ,
  • Chantelle Booysen   ORCID: orcid.org/0000-0001-7218-8039 13 ,
  • Inés Bustamante 14 ,
  • Yajun Chen 15 ,
  • Kelly Davis 16 ,
  • Tarun Dua 17 ,
  • Nathaniel Foote 18 ,
  • Matthew Hughsam 2 ,
  • Damian Juma 19 ,
  • Shisir Khanal 20 ,
  • Manasi Kumar   ORCID: orcid.org/0000-0002-9773-8014 21 , 22 ,
  • Bina Lefkowitz 23 , 24 ,
  • Peter McDermott 25 ,
  • Modhurima Moitra 3 ,
  • Yvonne Ochieng   ORCID: orcid.org/0000-0002-9741-9814 26 ,
  • Olayinka Omigbodun 27 ,
  • Emily Queen 1 ,
  • Jürgen Unützer 3 ,
  • José Miguel Uribe-Restrepo 28 ,
  • Miranda Wolpert 29 &
  • Lian Zeitz 30  

Nature ( 2024 ) Cite this article

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  • Public health
  • Risk factors

Urban life shapes the mental health of city dwellers, and although cities provide access to health, education and economic gain, urban environments are often detrimental to mental health 1 , 2 . Increasing urbanization over the next three decades will be accompanied by a growing population of children and adolescents living in cities 3 . Shaping the aspects of urban life that influence youth mental health could have an enormous impact on adolescent well-being and adult trajectories 4 . We invited a multidisciplinary, global group of researchers, practitioners, advocates and young people to complete sequential surveys to identify and prioritize the characteristics of a mental health-friendly city for young people. Here we show a set of ranked characteristic statements, grouped by personal, interpersonal, community, organizational, policy and environmental domains of intervention. Life skills for personal development, valuing and accepting young people’s ideas and choices, providing safe public space for social connection, employment and job security, centring youth input in urban planning and design, and addressing adverse social determinants were priorities by domain. We report the adversities that COVID-19 generated and link relevant actions to these data. Our findings highlight the need for intersectoral, multilevel intervention and for inclusive, equitable, participatory design of cities that support youth mental health.

More than a decade ago, Galea posed the question “Can we improve mental health if we improve cities?” 4 . In the past two centuries, urbanization has shaped landscapes and lives, making it the “sentinel demographic shift” of our times 4 . The relationships between mental health status and the social, cultural and physical environment have been explored for at least as long; nineteenth-century researchers proposed environmental exposures as possible explanations of ‘insanity’ 5 . Faris and Dunham’s classic 1930s study 6 linked social disorganization and unstable communities to mental disorders. Two decades later, Leonard Duhl sought to create healthy societies through liveable cities, informing the World Health Organization’s Healthy Cities initiative 7 , 8 . The question remains pertinent today even as we recognize the multiple and complex forces that shape mental health 9 . Today we understand that urban environments influence a broad range of health outcomes for their populations, positively and negatively, and this impact is manifested unequally 10 . Opportunities for education and connection exist for some, whereas rising levels of urban inequality, violence, stressful racial or ethnic dynamics in urban neighbourhoods, exposure to environmental toxins, lack of green space, inadequate infrastructure and fear of displacement increase risk for poor mental health and disproportionately affect marginalized groups 11 . Disparate outcomes also pertain to distinct developmental stages, and the mental health of adolescents and young adults is particularly vulnerable to urban exposures.

Adolescents, youth and urban mental health

Young people under the age of 25 are the demographic group most likely to move to cities for educational and employment opportunities, and by 2050 cities will be home to 70% of the world’s children 3 . Cities concentrate innovation 3 and have long been considered the consummate source of skills, resources and talent 12 . They offer greater opportunities for health and economic development, education, employment, entertainment and social freedoms (that is, the ‘urban advantage’), but rapid urbanization also deepens disparities and exposes individuals to considerable adversity, placing their mental health at risk 13 . In fact, most evidence points to urban living as a risk factor for poorer mental health, yielding increased risk for psychosis, anxiety disorders and depression 1 , 2 . Adolescence and young adulthood, specifically, encompass a critical period of risk for the incidence of mental disorders: an estimated half of mental disorders evident before age 65 begin in adolescence and 75% begin by age 24 (ref.  14 ). Mental disorders are the leading causes of disease burden among 10–24-year-olds worldwide 15 , responsible for an estimated 28.2 million disability-adjusted life years globally, with 1 disability-adjusted life year being equivalent to a healthy year of life lost to the disability caused by mental disorders. Public awareness of these issues rose as the incidence of mental disorders and suicide increased in some countries among adolescents and young adults during the coronavirus pandemic 16 , 17 . Urban environments probably have a role in these processes.

Fundamental to adolescents’ growth and development are their interactions with the complex urban environment: physical, political, economic, social and cultural 18 . Adolescents have a heightened sensitivity to context and social evaluation, and a stronger neural response to social exclusion, as well as to threat and reward stimuli 19 , and it is plausible that they may be particularly sensitive to social and environmental cues in the urban context, such as discrimination or violence. Discriminatory policies and norms are entrenched in many of the institutions with which young people interact (for example, schools, housing, justice and policing), and minoritized youth may experience the emotional and mental health consequences 20 . In fact, in settings of structural inequality (for example, high neighbourhood poverty and unemployment), young people are at greater risk for low self-efficacy and feelings of powerlessness and depression 21 . Social cohesion and collective efficacy can reduce the effects of concentrated disadvantage and nurture social and emotional assets among young people, families and their networks 21 .

At present, the world’s largest population of adolescents and young adults so far is growing up amid the sequelae of a tenacious pandemic, rapid population growth in urban centres and increasing urbanization, demanding an urgent response to support youth mental health 22 . Investing in adolescent well-being is said to yield a triple dividend through actions that reduce mortality and disability in adolescence, prolong healthy life in adulthood, and protect the health of the next generation by educating and strengthening the health of young parents 23 . Interventions in urban settings that align with developmental needs of adolescents and young adults could remediate insults from early life and establish healthy behaviours and trajectories for adult life 19 , 24 , potentially averting chronic conditions such as human immunodeficiency virus (HIV) and the associated mental health, social and physical sequelae 25 . In fact, investment in a package of adolescent mental health interventions can yield a 24-fold return in health and economic benefits 26 . At the societal level, shaping the aspects of urban life that influence youth mental health—through services, social policies and intentional design—could have an enormous impact 4 . Proposals for ‘restorative urbanism’ that centre mental health, wellness and quality of life in urban design may move cities in the direction of moulding urban environments for better adolescent health 27 , 28 . Young people, who contribute to the creativity of urban environments and drive movements for social change 29 , have a central part to play in this transformation.

Mental Health Friendly Cities, a global multi-stakeholder initiative led by citiesRISE, mobilizes youth-driven action and systems reform to promote and sustain the mental health and well-being of young people in cities around the world 30 , 31 ( Supplementary Information ). To guide transformative actions that will enable cities to promote and sustain adolescent and youth mental health, we studied global priorities for urban adolescent mental health. One aim of this study is to contribute data-driven insights that can be used to unite several sectors in cities to act within and across their domains in favour of mental health promotion and care that is responsive to the needs of young people. To that end, we administered a series of linked surveys that permitted the influence of ideas from young people and multidisciplinary domain experts through an anonymous sequential process, following established methods for research priority setting 32 .

Framework and top-ranked recommendations

To determine the elements of an urban landscape that would support mental health for adolescents and youth and would amplify their voices, we recruited a panel of 518 individuals from 53 countries to participate in a series of three digitally administered surveys that began in April 2020 (Table 1 ). Figure 1 shows the panel participation at each round. In survey 1, panellists responded to the open-ended question: “What are the characteristics of a mental health-friendly city for young people?”. Analysis of survey 1 data produced 134 statements about mental health-friendly cities for young people ( Methods ). In survey 2, participants selected their preferred 40 of the 134 statements. They were also presented with a second question related to the influence of the COVID-19 pandemic on their ideas about youth well-being in cities. In survey 3, we categorized survey 2 statements by socioecological domains (Fig. 2 ) and asked panellists to rank-list their preferred statements in each domain. Before ranking, panellists were required to choose one of three framings that informed their selected ranking: immediacy of impact on youth mental health; ability to help youth thrive in cities; and ease or feasibility of implementation.

figure 1

The composition of the project leadership structures, sample recruitment and participation by each survey round are shown below. We invited 801 individuals to participate in the survey panel through recommendations and direct invitations from advisory board members. Participants recruited through snowball sampling received the Research Electronic Data Capture (REDCap) link ( n  = 24). Individuals who gave informed consent in REDCap were deemed to have accepted the survey panel invitation. S1, survey 1; S2, survey 2; S3, survey 3.

figure 2

The socioecological model with six levels (personal, interpersonal, community, organization, policy and environment) that are used to categorize the characteristics of a mental health friendly city.

We present the findings of the third survey within a socioecological model (Figs. 3 – 5 ) because of this model’s relevance to the combination of social and environmental exposures in an urban setting and their interaction with the developing adolescent 33 . Bronfenbrenner’s model begins by recognizing that young people’s personal experiences and development are shaped by their interactions with the people around them 34 ; that is, they react to and act on their immediate environment of familial and peer relationships (microlevel). These interpersonal relationships are also influenced by neighbourhood and community dynamics and exposure to institutions and policies (mesolevel). These, in turn, are nested within the organizational, political, historical, cultural (for example, values, norms and beliefs) and physical environments (macrolevel) whose interplay directly or indirectly affects the adolescent’s mental health and well-being. A high court ruling (policy environment) could have direct or indirect effects on the community, household and personal well-being of a young person seeking asylum. The socioecological framework encompasses the dynamic relationships of an individual with the social environment.

figure 3

Mean ranks and standard deviations (s.d.) values for each mental health-friendly city (MHFC) characteristic are reported grouped by socioecological level and three framings described in the Analysis: immediacy of impact; ability to help youth thrive in cities; and ease or feasibility of implementation. Overall ranks (along with mean and s.d. values) for the total sample are reported. n values in bold represent the number of participants responding for each domain; the percentages in bold represent the percentage of respondents per domain. The number and percentage of the sample that assigned the highest rank for each characteristic are also reported (column 2). The colour continuum from light blue to dark blue shows the highest ranked means in the lightest shades and the lower ranks in darker blue.

figure 4

See the caption of Fig. 3 for details.

figure 5

See the caption of Fig. 3 for details. LGBT+, people from sexual and gender minorities.

The characteristics

We grouped 37 city characteristics across 6 socioecological domains: personal, interpersonal, community, organizational, policy and environmental. Figures 3 – 5 show the mean ranking for each framing and the total mean ranking averaged across frames. We show, for each characteristic statement, the number and percentage of panellists who ranked it highest. The five characteristics in the personal domain centre on factors that enable healthy emotional maturation for young people, future orientation and self-reflexivity. Most panellists (53%) ranked these characteristics according to immediacy of impact on youth mental health in cities, and mean rankings were identical to those linked to ability to help youth thrive in cities. The characteristic that describes prioritizing teaching life skills, providing opportunities for personal development and providing resources that allow young people to flourish rose to the top mean rank for each frame and was also ranked first in this domain by the largest number of panellists ( n  = 93). Notably, the characteristic that describes preparing youth to handle their emotions and overcome challenges was ranked first by 62 panellists, although its mean rank was much lower.

Characteristics in the interpersonal domain refer to young people’s interactions with others in the environment. Prioritized characteristics in this domain centred on relationships marked by acceptance and respect for young people and noted the value of intergenerational relationships. The top-ranked characteristic emphasized age friendliness and interactions that value the feelings and opinions of young people as well as safe and healthy relationships. In this domain, ranked means for characteristics framed according to immediacy of impact on youth mental health and ability to help youth thrive were the same for the top two characteristics. Notably, the two highest-ranked means for ease of implementation focused on opportunities for safe and healthy relationships and strengthening intergenerational relationships.

Young people’s intrapersonal experiences and interpersonal relationships are nested within a system of community and organizational relationships. Study participants prioritized access to safe spaces for youth to gather and connect among the three characteristics in the domain of community, and rankings were identical for each framing. At the organizational domain, two characteristics shared high mean rankings: employment opportunities that allow job security and satisfaction and a responsive and supportive educational system. Health-care services and educational services were the organizations most frequently referenced in relation to youth mental health. Whereas employment opportunities ranked first in terms of feasibility of implementation, provision of youth-friendly health services ranked first for immediacy of impact on youth mental health. With the exception of the community and organizational domains, more panellists chose to frame their responses in terms of immediacy of impact on youth mental health.

Of the four statements in the policy domain, the design and planning of cities with youth input and gender sensitivity ranked highest overall and was most frequently ranked first by panellists (30.68%). Promoting democratic cooperation and equal opportunity and anti-discrimination in all institutions received the highest mean rank for feasibility of implementation.

The sixth socioecological domain lists 13 characteristics related to the social, cultural and physical environments. Addressing adverse social determinants of health for young people had the highest overall ranked mean; however, normalizing youth seeking mental health care and addressing service gaps ranked first when framed by feasibility of implementation and immediacy of impact. Having access to affordable basic amenities was most frequently ranked first in this domain by panellists, but panellist preferences were distributed across the list.

COVID-19 and urban youth well-being

Our data collection began in April 2020 during the COVID-19 pandemic, and by survey 2 (August 2020), most countries were experiencing the pandemic’s public health, social and economic effects. In light of this, we added an open-ended survey question to which 255 participants responded “How has the COVID-19 pandemic changed your ideas about the wellbeing of young people in cities?” ( Methods ). Most respondents reported changes in perspective or new emphases on inequities as determinants of youth well-being and mental health, whereas nine reported that COVID-19 did not change their ideas. For one such respondent (in the >35 years age category), the pandemic merely confirmed the powerful effect of social vulnerabilities on risk and outcomes during an emergency: “COVID-19 has not changed my ideas about the wellbeing of young people in cities. I found that the young people in cities who did well during the lockdown period and the difficult period of the pandemic were those who were already doing well in terms of a rich social network, good interpersonal relations with family and friends, enjoyable work life, a close religious network, membership [in] a young people’s club so that they were able to stay connected via social media. Those who had access to food and essential commodities and those who knew they would return to school or work after the pandemic. Those who had access to good living conditions and some space for recreation also did well. ... The impact of COVID19 was felt much more by those with existing mental health conditions, living in crowded slums, poverty, unemployment, who were uncertain about the next step”.

Respondents highlighted losses young people experienced as a result of the pandemic. These included loss of the city as a place of opportunity; loss of jobs, familial and individual income, and economic stability; loss of a planned future and loss of certainty; loss of rites of passage of youth; loss of access to friends, social networks and social support; loss of access to quality education and to health care, especially mental health care and sexual and reproductive health services; loss of opportunities for psychological and social development; and loss of loved ones who died from COVID-19. We summarize the qualitative findings according to the socioecological framework. We present sample quotes in Table 2 , along with the age category of the respondents (18–24, 25–35 and >35) and actions for cities to take.

Policy and environment

Governance and equity.

Freedom from discrimination and the value of equity were listed among the mental health-friendly city characteristics; however, respondents pointed out the dearth of equity that COVID-19 unveiled (see the first quote in Table 2 ).

Respondents observed that policy responses to COVID-19, including mandated curfews and quarantines, shifted the social and economic environment of cities. Young people and their families lost economic opportunities, and cities also became less affordable during the pandemic. Participants explained that poverty and job loss worsened young people’s mental health and well-being and exposed youth to more risk factors because they needed to “hustle or work to place food on the table”. The loss of jobs also deprived youth of hope and underlined the economic inequities that some felt marked their generation more than previous ones. One participant (18–24) reported “Before, I used to think youths need someone who can understand them, empathize with them, but looking at the current scenario, I feel youths need security and a hopeful future too”. In some settings, these economic shifts resulted in an exodus from cities. A respondent (18–24) observed “Cities have always attracted young people but since the pandemic started the cost of living has gone from being a barrier to being another factor in encouraging young people to leave”.

Urban built environment

For those who remained in the city, the urban built environment could also offer respite from pandemic-related restrictions in mobility when green spaces and other open spaces were accessible. Participants alluded to cramped urban housing, crowded slums and poor housing infrastructure as stressors that the availability of safe public spaces alleviated. Green space in particular provided solace for young people. A participant (18–24) responded “It’s difficult when you’re confined to the limited space especially when you’re not closer to nature. Negative thoughts get you one way or another even if you try your best. Pandemic has caused more depression I reckon among the youths”. Accessible green space was highlighted as a need and an area for investing effort and policy change (Table 2 ). A desire for clean, youth-friendly green space for safe gathering and recreation was contrasted with unplanned land use and confined spaces, the latter of which some participants linked to greater risks for young people.

Community and organizations

Respondents reported diminished access to education and health care, and a disregard of young people’s needs by decision-makers (Table 2 ). Some responses criticized the lack of forethought before the pandemic to budget for and provide supportive learning environments for youth of all socioeconomic strata. The closure of schools generated stress for young people with the disruption of routines and opportunities to socialize. The pandemic generated greater uncertainty about job opportunities and future trajectories. At the same time, the pandemic brought opportunities to position youth as either contributors and leaders or detractors from community life. Young people reflected on how they experienced inclusion, empathy and exclusion, as well as opportunity for leadership. One respondent (25–35) commented “Our worlds are changing and with it many of our expectations about our education, work, personal interactions and relationships. Instead of being met with understanding, we are collectively positioned as transgressors of social distancing in a way that fails to understand that we are often incredibly vulnerable in this new world and left exposed by lack of infrastructure, service provision and support”.

A respondent (18–24) noticed possibilities for involving young people in responses that could mitigate their numerous losses: “Given the opportunities and resources, young people can be a carrier of change and wellbeing if adults trust them enough to be”.

Interpersonal domain

Getting through difficult times required interpersonal supports: connectedness through in-person encounters in safe spaces, complemented by digital interactions. Multiple respondents emphasized the relationship between social isolation and poor mental health among city youth during the pandemic, noting the difficulty of making meaningful connection during a time of physical isolation. Two young respondents (18–24) said the well-being of young people was linked to being “in a group of people”, which provides “safety and unity”, and to “inclusion, activity, and interpersonal relationships”. Space repeatedly emerged as a theme, as a conduit to facilitate social connection for young people without risk of COVID-19 transmission, violence, sexual abuse or exposure to drug use. Some participants called for greater investment in creating strong, safe virtual communities for young people; however, although participants identified virtual spaces as a resource for mental health support, a young panellist (18–24) remarked of social media and technology that “It isolated people, even though we have … ways of staying connected 24/7, we still feel lonely.”

Consistent with the lead mental health-friendly city characteristic in the personal domain (Figs. 3 – 5 ), the pandemic prompted realization of the need for personal skills development to support youth mental well-being. Some respondents expressed concern about the loss of social skills among young people as a result of confinement and an 18–24-year-old commented “… Youths are in that stage where they need to be equipped with skills to promote positive mental wellbeing”. Another young person (18–24) remarked “Most of us do not really have the capacity and necessary skills to support each other when it comes to mental health”. Participants described the importance of being prepared for unpredictable circumstances and enabling youth to “manage themselves, their emotions, and wellbeing”.

Pandemic-related gains

In some cases, the pandemic brought positive experiences for young people, including more time for self-reflection and discovery, engaging in healing practices, more opportunities to connect with friends, and overall, a greater societal and individual focus on strengthening mental health. A participant (25–35) referred to young people: “They are more conscious about health and their wellbeing by reducing workload and connecting with nature”. Others believed the pandemic revealed young people’s capacity to adapt and to consider the needs of their elders. Some viewed the social justice uprisings that occurred in many countries as a positive vehicle for change and cooperation with others. Changing these conditions would require longer-term solutions: strengthening urban infrastructure and addressing the underlying drivers of inequity. Another participant (>35) lauded the power of youth activism: “… the pandemic has shown us that the resilience of youth is great, as well as the commitment and solidarity with their communities through volunteering, advocacy and youth mobilization”.

Our study convened a multinational and multidisciplinary panel of researchers, practitioners, advocates and young people to identify the characteristics of a mental health-friendly city for youths. The characteristics are distributed among six socioecological domains (Figs. 3 – 5 ) that encompass the personal development of young people, supportive educational systems, people-centred health care, a built environment responsive to the needs of young people, and equity-focused policy-making and governance. Within each of these domains, the characteristics we identified are associated with an evolving evidence base linked to youth mental health outcomes and to potential policy intervention.

Intrapersonal characteristics in our list underline the centrality of enabling young people to cultivate skills to manage their interior lives. The targets of such skills-building activities align with proposed ‘active ingredients’ of mental health interventions, such as intervention components related to mechanisms of action or clinical effects on depressive or anxiety symptoms 35 . Examples include affective awareness skills that enable young people to differentiate and describe emotions 36 and emotion regulation skills to increase and maintain positive emotions 37 . Youth-friendly mental health and educational services, a priority theme at the community level of the framework, could support the intrapersonal realm by deploying a variety of interventions for self-control that benefit adolescent and young adult academic, behavioural and social functioning 38 . Such interventions can also be implemented in earlier childhood educational settings through integration into the curriculum or through other community-based medical or social service organizations 39 . Interventions implemented in selected high-income settings include Promoting Alternative Thinking Strategies 40 , the Incredible Years 41 and Family Check-up 42 . For young adults, interventions that convey skills to alleviate common psychological problems such as procrastination, perfectionism, low self-esteem, test anxiety and stress could potentially reduce the prevalence of specific mental health conditions while possibly providing acceptable and non-stigmatizing options for care 43 , 44 .

Our data suggest that a defining theme of any mental health-friendly city for youth is the quality of young people’s social fabric and the city’s ability to provide young people with the skills, opportunities and places required to build and maintain healthy social relationships with their peers, across generations, and as members of a community. The relationships of concern in the interpersonal realm have intrinsic value for healthy adolescent and youth development, promoting well-being 45 and prevention of depression 46 , 47 . Panellists also linked opportunities to socialize and build social networks to the availability of safe spaces, the top-ranked priority in the community domain. Achieving safety necessitates equitable and violence-free institutions and cities 48 , a priority that panellists ranked first for ease of implementation in the policy domain. Thus, policies and legislation are required that reduce neglect, bullying, harassment, abuse, censorship, exposure to violence and a wide range of threats towards young people, from homelessness to crime to intimidation by officials 48 , 49 .

Exposure to community violence and household violence consistently worsens mental health outcomes for youth 50 , 51 , 52 , 53 ; successful reduction of urban violence should be prioritized. Equity-focused responses to safety needs should include reducing discriminatory physical and structural violence against young people based on race, ethnicity, gender, sexuality or mental health status, which place youth at risk of harmful exposures: rape or trafficking of adolescent girls or police killings of North American Black youth. To create urban spaces in which young people can experience safety, freedom and belongingness requires approaches that actively prevent discrimination 54 and that consider young people’s multiple identities in the design of institutional as well as outdoor spaces. Women-only parks create greater security for girls and young women and potentially more positive social interaction in some settings 55 .

The benefits of green space, measured as self-satisfaction for adolescents, are linked to greater social contact (for example, more close friends), underscoring space as a conduit for social connection 55 . The advantages of healthy urban spaces for adolescents have emerged not only in health sciences research but also in allied fields such as urban design and sociology 27 , 56 , 57 . Urban spaces with opportunities for active commute options to and from school are associated with increased physical activity and environmental supportiveness 58 . Similarly, the presence of community spaces, such as town centres, is associated with improved social connectedness and sense of belonging 59 .

The critical importance of social connectedness was reinforced in the COVID-19 responses. Yet, in many cities the pandemic eliminated spaces that foster urban conviviality, often with lasting effects 60 . Restricted movement and COVID-19 transmission risk associated with public transport may have contributed to greater stress for urban dwellers and ongoing reluctance to use these services 61 . Such factors contribute to social isolation, which may persist in the near term. Consistent with our COVID-19 data, responses from a sample of Australian youth identified social isolation, interrupted education and work, and uncertainty about the future among the primary negative effects of COVID-19 pandemic 62 . In several studies, loneliness increased the risk of mental health conditions among young people during prior epidemics; of relevance to the COVID-19 pandemic, the duration of loneliness predicted future mental health problems 63 .

Analysis of our survey 2 data revealed differences in the priorities of young participants (18–24 and 25–35) compared with panellists over age 35. This discrepancy could have implications for urban decision-makers whose plans to implement positive actions on behalf of young people may not align with what is most salient for youth. Thus, youth involvement in policy development is even more crucial. Soliciting youth perspectives about what supports their mental health based on their personal experiences could simplify and improve interventions intended for them 64 . Several actions could facilitate meaningful youth engagement in governance: encourage collaboration between governments and youth organizations to co-create and co-lead national action plans; implement mechanisms within global governance organizations for youth consultation at local, national and international levels; require inclusion of young people on relevant conference agendas; and improve access to funding for youth-led organizations 65 , 66 .

Notably, the themes of equity and elimination of discrimination due to race, gender, sexual orientation and neurodiversity arose frequently in the responses to the survey and the COVID-19 question, as did the adversities to which minoritized groups are vulnerable (for example, community violence, police violence and bullying; Figs. 4 and 5 ). A city that is free of discrimination and racism ranked first among policy responses with immediacy of impact on the mental health of youth—even though no statements proposed dismantling systems of oppression that underlie racism and discrimination, as one respondent noted (Fig. 4 ). Globally, racism, xenophobia and other forms of discrimination increase mortality and harm the mental health of affected groups through stress-related physiological responses, harmful environmental exposures and limited access to opportunities and health services 20 , 67 , 68 , 69 . Embedded racist and xenophobic norms, policies and practices of institutions—including those that govern educational, labour and health care systems—yield racialized outcomes for young people around the world (for example, high incidence of HIV infection among adolescent girls in southern sub-Saharan Africa) 20 . To disrupt these forces requires multiple approaches, including recognition and remedy of historical injustices, the activism of social movements committed to change, and implementation of legal frameworks based in human rights norms 70 .

When participants ranked characteristics for ease of implementation (Figs. 3 – 5 ), they coalesced around a broad set of factors demonstrating the need for collaboration across urban sectors (for example, normalizing seeking mental health care, promoting democratic cooperation and equal opportunity, and creating employment opportunities and progressive educational systems). This need for cooperation is perhaps most apparent for actions that increase equity. Successful cooperation requires a clear, shared vision and mission, allocation of funding in each sector, diversity of funding sources, distributed decision-making and authority across sectors, and policies that facilitate collaboration 71 . However, well-intentioned cross-sectoral responses to urban needs may inadvertently increase inequities by designing programmes influenced by market forces that magnify environmental privilege (that is, unequal exposure to environmental problems according to social privilege) 54 . Examples include gentrification and development that use land to create green spaces but further dislocate and marginalize communities in need of affordable housing 54 . Implementing community- and youth-partnered processes for urban health equity policy co-creation could yield unified agendas and help to circumvent inequitable outcomes 54 , 72 . A mental health-friendly city must be positioned to support, integrate and enable the thriving of marginalized and vulnerable young people of the society, who should be involved in its governance.

Strengths and limitations

Our study has several strengths. First, this priority-setting study yielded a rich dataset of recommended characteristics of a mental health-friendly city for young people from a globally diverse panel of more than 480 individuals from 53 countries. Second, we welcomed expertise from participants with roles relevant to urban sectors: researchers, policymakers and practice-based participants, and we engaged young people in the study advisory board and as study participants, capitalizing on their lived experience. Third, we captured information about how the COVID-19 pandemic influenced participants’ ideas about urban adolescent mental health. Fourth, to our knowledge, this is the first study that brings together a large and multidisciplinary set of stakeholders concerned for cities (for example, urban designers) and for youth mental health (for example, teachers and health professionals) to identify priorities for intersectoral action.

Our study also has several limitations. First, the participants recruited do not reflect the full social and economic diversity of urban populations whom city governments and decision-makers must serve. Our decision to use a web-based format following standard health research priority-setting methods required tradeoffs. We sought disciplinary, age and geographic diversity; however, our sample does not represent the most marginalized groups of adolescents or adults. Rather, the recruitment of academics, educators, leaders and well-networked young people through an online study probably minimizes the number of participants living in adversity. Although we also recruited young people who were not necessarily established experts, many were students or members of advocacy or international leadership networks and were not likely to exemplify the most disadvantaged groups. We risk masking the specific viewpoints or needs of marginalized and at-risk young people. However, we are reassured by the prominence of equity as a theme and the call to address social determinants of health. Second, it is possible that participants recruited through the authors’ professional networks may be more likely to reflect the viewpoints of the advisory committee members who selected them, given collaborative or other professional relationships. This may have shaped the range of responses and their prioritization. Third, the aspirational calls for an end to discrimination and inequalities highlighted in our results require confronting long-standing structural inequities both within and between countries. Structural violence frequently maintains these power imbalances. Although we do not view their aspirational nature as a limitation, we note that our study data do not outline the complexity of responses required to address these determinants of mental health or to dismantle discriminatory structures. Fourth, our data present several aggregated characteristics that may require disaggregation as cities contextualize the findings for their settings. Fifth, our network recruitment strategy led to skewed recruitment from some geographic regions (for example, North America and Nepal), which may have biased responses (Extended Data Figs. 1 – 3 ). Extended Data Table 1 shows the similarities and differences in the rankings for Nepal, USA and the remaining countries in survey 3. Additionally, we recruited few 14–17-year-olds. We experienced attrition over the three rounds of surveying, ending with complete responses from 261 individuals from 48 countries, with the greatest loss in participants between surveys 1 and 2 (Table 1 ), among the 14–17-, 18–24- and 25–35-year-old age groups, and among participants from Nepal (Extended Data Fig. 2 ).

Conclusions

We identified a set of priorities for cities that require intervention at multiple levels and across urban sectors. A clear next step could involve convenings to build national or regional consensus around local priorities and plans to engage stakeholders to co-design implementation of the most salient characteristics of a mental health-friendly city for youth in specific cities (Box 1 ). It is likely that many variables (for example, geography, politics, culture, race, ethnicity and sexual identity) will shape priorities in each city. Therefore, essential to equitable action is ensuring that an inclusive community of actors is at the table formulating and making decisions, and that pathways for generating knowledge of mental health-friendly city characteristics remain open. This includes representation of sectors beyond mental health that operate at the intersection of areas prioritized by young people. Preparing for implementation will require avenues for youth participation and influence through collective action, social entrepreneurship and representation in national, regional and community decision-making. Enlisting the participation of youth networks that bring young people marginalized owing to sex, gender, sexual orientation, race, economic status, ethnicity or caste; young people with disabilities; and youth and adults with lived experience of mental health conditions in the design of mental health-friendly cities will help to level power imbalances and increase the likelihood that cities meet their needs.

Action for adolescent mental health aligns well with actions nations should take to achieve development targets, and collective action to draw attention to these areas of synergy could benefit youth and cities. Specifically, supporting the mental health of young people aligns with Sustainable Development Goal 11 (sustainable cities and communities) and the New Urban Agenda that aims to “ensure sustainable and inclusive urban economies, to end poverty and to ensure equal rights and opportunities … and integration into the urban space” 73 , 74 , 75 .

Additionally, the list of mental health-friendly city characteristics presents a starting point for strengthening the evidence base on intervening at multiple levels (for example, individual, family, community, organizations and environment) to better understand what works for which youth in which settings. Cities function as complex systems, and systems-centred research can best enable us to understand how individuals’ interactions with one another and with their environments influence good or poor mental health 76 . Similarly, interdisciplinary inquiry is needed that investigates urban precarity and sheds light on social interventions for youth mental health 77 . New research that tests implementation strategies and measures mental health outcomes of coordinated cross-sectoral interventions in cities could be integrated with planned actions. Innovative uses of data that measure the ‘racial opportunity gap’ can help cities to understand how race and place interact to reduce economic well-being for minoritized young people on their trajectory to adulthood 78 . Even heavily studied relationships, such as mental health and green space, can benefit from new methodologies for measuring exposures, including application of mixed methods, and refined characterization of outcomes by gender and age with a focus on adolescents and youth 79 . Globally, mental health-supporting actions for young people in urban areas have an incomplete evidence base, with more peer-reviewed publications skewed towards North American research 73 .

Designing mental health-friendly cities for young people is possible. It requires policy approaches that facilitate systemic, sustained intersectoral commitments at the global as well as local levels 80 . It also requires creative collaboration across multiple sectors because the characteristics identified range from transport to housing to employment to health, with a central focus on social and economic equity. Acting on these characteristics demands coordinated investment, joint planning and decision-making among urban sectoral leaders, and strategic deployment of human and financial resources across local government departments that shape city life and resources 75 , 81 . This process will be more successful when cities intentionally and accountably implement plans to dismantle structural racism and other forms of discrimination to provide equitable access to economic and educational opportunities for young people, with the goal of eliminating disparate health and social outcomes. The process is made easier when diverse stakeholders identify converging interests and interventions that allow them each to achieve their goals.

Box 1 Considerations for implementing a mental health-friendly city for youth

Considerations for implementing a mental health-friendly city for youth using a structure adapted from UNICEF’s strategic framework for the second decade of life 82 and integrating selected characteristics identified in the study with examples distilled from scientific literature and from project advisory group members. Objectives for implementation along with corresponding examples and selected initiatives are shown.

Youth are equipped with resources and skills for personal and emotional development, compassion, self-acceptance, and flourishing.

Youth develop and sustain safe, healthy relationships and strong intergenerational bonds in age-friendly settings that respect, value and validate them.

Communities promote youth integration and participation in all areas of community life.

Communities establish and maintain safe, free public spaces for youth socializing, learning and connection.

Institutions facilitate satisfying, secure employment; progressive, inclusive, violence-free education; skills for mental health advocacy and peer support.

Policies support antiracist, gender equitable, non-discriminatory cities that promote democratic cooperation and non-violence.

Urban environments provide safe, reliable infrastructure for basic amenities and transportation; affordable housing; access to green and blues space; and access to recreation and art.

Cities minimize adverse social determinants of health; design for safety and security for vulnerable groups; and orient social and built environments to mental health promotion, belonging and purpose.

Use rights-based approaches

Prioritize equity for racially, ethnically, gender, sexually and neurologically diverse young people

Ensure sustained and authentic participation of youth

Schools and other educational settings

Health and social services

Families and communities

Religious and spiritual institutions

Child protection and justice systems

Peer groups

Civil society

Digital and non-digital media

Implementation objectives

Build consensus and contextualize the mental health-friendly city approach at local, regional, national levels

Engage diverse youth in co-design of mental health-friendly city plans

Expand opportunities for youth governance

Enable collaboration among sectors for policy alignment

Engage communities, schools, health services, media for intervention delivery

Legislate social protection policies

Scale interventions to improve economic and behavioral outcomes

Link implementation to achievement of national or international objectives

Selected implementation strategies

Youth co-design and participation: Growing Up Boulder is an initiative to create more equitable and sustainable communities in which young people participate and influence issues that affect them. It is a partnership between local schools, universities, local government, businesses and local non-profit organizations in the USA that has enabled young people to formally participate in visioning processes such as community assessments, mapping, photo documentation and presentations to city representatives 83 .

Engaging schools for interventions: universal school-based interventions for mental health promotion 84 ; linkage to mental health care for school-based programs 85 ; “Whole-school approaches” that engage students and families, communities, and other agencies to support mental health and improve academic outcomes 84 , 86 .

Digital platforms for youth mental health: Chile’s HealthyMind Initiative digital platform launched during the COVID-19 pandemic and provided a one-stop resource for information and digital mental health services. The platform included targeted evidence-based resources for children and adolescents 87 .

Interventions to test at scale: Stepping Stones and Creating Futures is a community-based intervention for intimate partner violence reduction and strengthening livelihoods in urban informal settlements in South Africa that reduced young men’s perpetration of intimate partner violence and increased women’s earning power 88 .

Shared international objectives: support Sustainable Development Goal 11 and New Urban Agenda targets and Sustainable Development Goals 1–6, 8, 10 and 16.

Project structure and launch

This study aimed to identify priorities for creating cities that promote and sustain adolescent and youth mental health. Central to achieving this aim was our goal of engaging a multidisciplinary, global, age-diverse group of stakeholders. As we began and throughout the study, we were cognizant of the risk of attrition, the importance of maintaining multidisciplinary participation throughout the study and the value of preserving the voices of young people. We used a priority-setting methodology explicitly aimed to be inclusive while simultaneously limiting study attrition. To ensure that we were inclusive of the voices of young people and our large and diverse sample, we limited our study to three surveys, which we determined a priori. Our approach was informed by standard methodologies for health research priority setting 32 .

The project was led by a collaborative team from the University of Washington Consortium for Global Mental Health, Urban@UW, the University of Melbourne and citiesRISE. We assembled three committees representing geographic, national, disciplinary, gender and age diversity to guide the work. First, a core team of P.Y.C., T.W., G.P., M.S. and T.C., generated an initial list of recommended members of the scientific advisory board on the basis of their research and practice activities related to adolescent mental health or the urban setting. We sought a multidisciplinary group representing relevant disciplines. The 18-member scientific advisory board, comprising global leaders in urban design and architecture, social entrepreneurship, education, mental health and adolescent development, provided scientific guidance. We invited members of an executive committee, who represented funding agencies as well as academic and non-governmental organizational leadership, to provide a second level of feedback. A youth advisory board, recruited through citiesRISE youth leaders and other global mental health youth networks, comprised global youth leaders in mental health advocacy. A research team from the University of Washington (Urban@UW, the University of Washington Population Health Initiative and the University of Washington Consortium for Global Mental Health) provided study coordination. The study received institutional review board approval at the University of Washington (STUDY00008502). Invitations to advisory groups were sent in December 2019, along with a concept note describing the aims of the project, and committee memberships were confirmed in January 2020. In February 2020, the committees formulated the question for survey 1: “What are the characteristics of a mental health friendly city for young people?”.

Study recruitment

The members of the scientific advisory board, youth advisory board and executive committee were invited to nominate individuals with expertise across domains relevant to urban life and adolescent well-being. The group recommended 763 individuals to join the priority-setting panel; individuals invited to serve on the scientific advisory board, youth advisory board and executive committee were included in panel invitations ( n  = 38). Our goal was to establish a geographically diverse panel of participants with scientific, policy and practice-based expertise corresponding to major urban sectors and related challenges (for example, health, education, urban planning and design, youth and criminal justice, housing and homelessness, and violence). Many of the nominees were experts with whom the core group and scientific advisory board members had collaborated, as well as individuals recruited on the basis of their participation in professional and scientific associations and committees (for example, Lancet Commissions and Series) or global practice networks (for example, Teach for All). Nominees’ names, the advisory member who nominated them, gender, country and discipline were tracked by T.C. We used snowball sampling to recruit participants from geographic regions that were under-represented: an additional 24 people were recruited through referrals. The scientific advisory board and youth advisory board sought to maximize the number of young people participating in the study, and invitations were extended to adolescents and young adults through educational, professional, advocacy and advisory networks. Nominees received an invitation letter by e-mail, accompanied by a concept note that introduced the study, defined key constructs, described the roles of the study advisory groups and provided an estimated study timeline. Youth participants (14–24) received a more abbreviated introductory letter. A link to a REDCap survey with an informed consent form and round 1 question was embedded in the invitation e-mail, which was offered in English and Spanish. Of the 824 individuals invited, 518 individuals from 53 countries provided informed consent and agreed to participate, resulting in a nomination acceptance rate of 62.8%.

Data collection

We administered a series of three sequential surveys using REDCap version 9.8.2. Panellists were asked to respond to the survey 1 question “What are the characteristics of a mental health friendly city for young people?” by providing up to five characteristics and were invited to use as much space as needed. In survey 2, panellists received 134 characteristic statements derived from survey 1 data and were asked to select their 40 most important statements. From these data, we selected 40 most frequently ranked statements. These were presented in the round 3 survey with three redundant statements removed. The remaining 37 characteristic statements were categorized across 6 socioecological domains and panellists were asked to select 1 of 3 framings by which to rank the statements in each domain: immediacy of impact on youth mental health in cities, ability to help youth thrive in cities, and ease or feasibility of implementation. Of individuals who consented to participate, 93.4% completed round 1, 58.5% completed round 2 and 56.2% completed round 3 (Table 1 ).

We added a new open-ended question to survey 2: “How has the COVID-19 pandemic changed your ideas about the wellbeing of young people in cities?”. Panellists were invited to respond using as many characters (that is, as much space) as needed.

Data analysis

Three-survey series.

We managed the survey 1 data using ATLAS.ti 8 software for qualitative data analysis and conducted a conventional content analysis of survey 1 data 89 . Given the multidisciplinarity of the topic and our multidisciplinary group of respondents, we selected an inductive method of analysis to reflect, as simply as possible, the priorities reported by the study sample without imposing disciplinary frameworks. In brief, responses were read multiple times, and characteristics were highlighted in the text. A list of characteristics (words and phrases) was constructed, and we coded the data according to emerging categories (for example, accessibility, basic amenities, career, built environment, mental health services and so on). The analysis yielded 19 broad categories with 423 characteristics. Within each category, characteristics were grouped into statements that preserved meaning while streamlining the list, which yielded 134 characteristic statements. The University of Washington research team convened a 1-week series of data discussions with youth advisers to review the wording of the characteristics and ensure their comprehensibility among readers from different countries. The survey 1 categorized data were reviewed by members of the scientific advisory board, who recommended that using relevant domains to group characteristics would provide meaningful context to the final list. We used IBM SPSS 28.0 for quantitative analyses of data from surveys 2 and 3. In survey 2, we analysed the frequency of endorsement of the 40 characteristics selected by panellists and generated a ranked list of all responses, with the most frequently endorsed at the top. The decision to select 40 characteristics aligned with methods applied in a previous priority-setting exercise 90 and permitted a list of preferred characteristics that could subsequently be categorized according to a known framework, allowing city stakeholders a broad list from which to select actions. We also analysed frequency of endorsement by age categories (18–24, 25–35 and >35). To amplify the viewpoints of younger participants (under age 35), we combined the top 25 characteristic statements of panellists over 35 with the top 26 characteristic statements of participants under 35 to generate a list of 40 statements, including 11 shared ranked characteristics. As noted, we removed three of these statements because of their redundancy. In survey 3, we analysed data consisting of 37 characteristic statements divided across 6 socioecological domains. Characteristics in each domain were ranked according to one of three framings. We calculated mean ranking and standard deviation for characteristics in each framing category per socioecological domain. Mean rankings (with standard deviation) were calculated across framing categories to arrive at the total mean rank per characteristic and they reflect the proportional contribution of each domain. We also calculated the frequency with which panellists ranked each characteristic statement number 1.

Our study methods align with good practices for health research priority setting as follows 32 .

Context: we defined a clear focus of the study.

Use of a comprehensive approach: we outlined methods, time frame and intentions for the results before beginning the study; however, we modified (that is, simplified) the methods for survey 3 to minimize study attrition.

Inclusiveness: we prioritized recruiting for broad representation and maintaining engagement of an inclusive participant group, and methodological decisions were made in service of this priority.

Information gathering: our reviews of the literature showed that a study bringing together these key stakeholders had not been conducted, despite the need.

Planning for implementation: we recognized from the outset that additional convening at regional levels would be required to implement action, and our network members are able to move the agenda forwards.

Criteria: we determined criteria for the priorities (framing: feasibility of implementation, immediacy of impact and ability to help youth thrive) that study participants used and which we believe will be useful for practical implementation.

Methods for deciding on priorities: we determined that rank order would be used to determine priorities.

Evaluation: not applicable; we have not planned an evaluation of the impact of priority setting in this phase of work.

Transparency: the manuscript preparation, review and revisions enable us to present findings with transparency.

COVID-19 qualitative data

We managed the COVID-19 qualitative data using Microsoft Excel and Microsoft Word. We carried out a rapid qualitative analysis 91 . First, the text responses were read and re-read multiple times. We coded the data for content related to expressions of change, no change or areas of emphasis in participants’ perceptions of youth mental health in cities during the pandemic. We focused our attention on data that highlighted changes. We further segmented the data by participant age categories, domains of change and suggested actions, and we assigned socioecological level of changes. We created a matrix using excerpted or highlighted text categorized according to these categories. Three data analysts (P.Y.C., T.C. and A.M.-K.) reviewed the domains of change and identified emerging themes, which were added to the matrix and linked to quotes. The team discussed the themes and came to consensus on assignment to a socioecological level. We prioritized reporting recurring concepts (for example, themes of loss, inequity, green space, isolation and mental illnesses) and contrasting concepts (for example, gains associated with COVID-19) and associated actions 92 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

Survey data that support the findings of this study are available from the corresponding author, P.Y.C., on reasonable request. The sharing of data must comply with institutional policies that require a formal agreement (between the corresponding author and the requester) for sharing and release of data under limits permissible by the institutional review board.

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Acknowledgements

We thank M. Antia, S. Talam and J. Vollendroft for contributions to this project; H. Jack for contributions to the manuscript revision; and the survey panellists without whom this work would not have been possible. M.K. was supported in part by funding from the Fogarty International Center (K43 TW010716) and the National Institute of Mental Health (R21 MH124149) of the National Institutes of Health. This study was supported in part by funding to citiesRISE (M.M. and M.H.) from the Rural India Supporting Trust and from Pivotal Ventures. This study was conducted while P.Y.C. was on the faculty at the University of Washington, Seattle. The University of Washington (P.Y.C. and T.C.) received funding from citiesRISE by subcontract. T.D. is a staff member of the World Health Organization (WHO). The content and views expressed in this manuscript are solely the responsibility of the authors and do not necessarily represent the official views, decisions or policies of the institutions with which they are affiliated, including WHO, the US Department of Health and Human Services and the National Institutes of Health.

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Contributions

This study was led by a core group, P.Y.C., G.P., M.S. and T.W., who were members of the project’s scientific advisory board and executive committee and part of the group of 32 co-authors (P.Y.C., M.S., T.C., G.P., T.W., L.M., A.M.-K., L.A., N.B., I.B., Y.C., T.D., E.d.L., N.F., H.H., S.K., M.K., B.L., O.O., J.M.U.-R., C.B., K.D., M.H., D.J., M.M., E.Q., Y.O., L.Z., N.A., P.M., J.U. and M.W.). P.Y.C. and T.C. regularly updated the core group members by e-mail, and P.Y.C. led online meetings with updates on study progress and data collection and study outcomes with members of the scientific advisory board (N.B., I.B., Y.C., T.D., E.d.L., N.F., H.H., S.K., M.K., B.L., O.O., J.M.U.-R. and K.D.), youth advisory board (K.D., C.B., D.J., Y.O., E.Q. and L.Z.) and executive committee (N.A., J.U. and M.W.). P.Y.C. (the core group lead) and members of the scientific advisory board and executive committee were involved with conceptualization, study design and methodology. Youth advisers assisted with qualitative data analysis. P.Y.C., T.C. and A.M.-K. were also responsible for data curation and formal analysis; P.Y.C. and T.C. wrote the original draft, with contribution from G.P., M.S., T.W., H.H. and L.M. P.Y.C., T.C., A.M.-K., M.M., H.H. and E.d.L. reviewed and organized responses to reviewers. All co-authors reviewed responses to the reviewers. P.Y.C. led the manuscript revision with A.M.-K., M.M. and T.C. All co-authors had the opportunity to discuss the results, review full drafts of the manuscript and provide comments on the manuscript at all stages.

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Correspondence to Pamela Y. Collins .

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Extended data figures and tables

Extended data fig. 1 distribution of participants by nationality (n = 518) a,b,c ..

a Countries Participating: Argentina, Australia, Bangladesh, Cameroon, Canada, China, Colombia, Croatia, Czech Republic, Ecuador, Egypt, Ethiopia, France, Germany, Ghana, Haiti, Hong Kong, India, Iran, Italy, Kenya, Malawi, Mauritius, Mexico, Nepal, Netherlands, New Zealand, Nigeria, Norway, Pakistan, Papua New Guinea, Peru, Philippines, Poland, Rwanda, Samoa, Sierra Leone, Slovenia, South Africa, South Korea, Sweden, Switzerland, Taiwan, Tanzania, The Gambia, Tunisia, Turkey, Uganda, UK, USA, Venezuela, Zambia, Zimbabwe (53 total); b Two responses (“Asian” and “Indigenous and European”) do not list a nation but capture verbatim open-text responses; c Countries with one participant removed from graph and include: Argentina, Bangladesh, Cameroon, Croatia, Czech Republic, Ecuador, Egypt, Ethiopia, France, Haiti, Hong Kong, Indigenous and European, Mauritius, New Zealand, Norway, Papua New Guinea, Samoa, Slovenia, South Africa, South Korea, Switzerland, Taiwan, Tanzania, The Gambia, Tunisia, Turkey, Uganda, Venezuela.

Extended Data Fig. 2 Participant Nationality by Survey Round.

a SEA = South-East Asia, NA = North America*, AF = Africa, LSA = Latin & South America*, EU = Europe, WP = Western Pacific, EM = Eastern Mediterranean.

Extended Data Fig. 3 Distribution of Participants by WHO Region * and Survey Round.

a SEA = South-East Asia, NA = North America*, AF = Africa, LSA = Latin & South America*, EU = Europe, WP = Western Pacific, EM = Eastern Mediterranean; *We separated North America from Latin & South America for more transparent display of participant distribution.

Supplementary information

Supplementary information.

Supplementary Note which describes citiesRISE and lists the project team members of Making cities mental health-friendly for adolescents and young adults.

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Collins, P.Y., Sinha, M., Concepcion, T. et al. Making cities mental health friendly for adolescents and young adults. Nature (2024). https://doi.org/10.1038/s41586-023-07005-4

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February 27, 2024 | Lauren Woods - Schools of Medicine and Dental Medicine

Mary Beth Bruder, Ph.D., Awarded the Council for Exceptional Children’s Special Education Lifetime Achievement Award

Bruder will receive the award at the 2024 Council for Exceptional Children (CEC) Convention and Expo in San Antonio, Texas on March 14.

educational achievement research paper

UConn's Mary Beth Bruder, Ph.D.

Mary Beth Bruder, Ph.D., director of the University of Connecticut’s Center for Excellence in Developmental Disabilities and professor at the UConn School of Medicine and the UConn Neag School of Education, was named recipient of the 2024 Council for Exceptional Children (CEC) Special Education Lifetime Achievement Award.

The CEC Special Education Lifetime Achievement award annually recognizes an individual in the field who has made significant, continuous contributions to special education, through leadership in the field, development of effective programs, effective personnel preparation, and advocating for diversity, equity, inclusion, and accessibility, among other areas. Recipients of the Special Education Lifetime Achievement Award demonstrate CEC’s core values: visionary thinking, integrity, and inclusiveness.

Founded in 1922, The Council for Exceptional Children is a professional association of educators dedicated to empowering educators who work with individuals with disabilities. Members of the CEC advocate for educators and individuals with disabilities, participate in professional development and research, foster networks and communities, and work to promote diversity, equity, inclusion, and accessibility in all that they do.

Bruder has been in the early intervention field for 48 years. Across the span of her career, she has been at the head of multiple federal and state research, demonstration, training, and technical assistance projects. Bruder has brought in over $100 million to UConn Health in funding for programs to advance special education personnel preparation.

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    Gender and educational achievement. In many countries, gender differences in educational success are part of long standing political, public and scientific debates about education. Whereas, for example, at the end of the nineteenth century and in the 1960s, the educational disadvantages of women (but also their difference) were central to ...

  19. American Educational Research Journal: Sage Journals

    The American Educational Research Journal (AERJ) is the flagship journal of AERA, with articles that advance the empirical, theoretical, and methodological understanding of education and learning. It publishes original peer-reviewed analyses spanning the field of education research across all subfields and disciplines and all levels of analysis, all levels of education throughout the life span ...

  20. PDF The Impact of School Bullying On Students' Academic Achievement ...

    The research results indicated that school bullying exists in all schools regardless if they are governmental or private ones. The study also concluded that school bullying affect student's academic achievement either victims or the bullies. Keywords: school bullying, academic achievement, teachers 1. Introduction

  21. How does family background affect children's educational achievement

    Education is a lasting process. Academic performance in primary education plays a crucial role in obtaining further educational opportunities. Thus, it is necessary to examine how family background affects children's academic achievement at an early stage. Through analysis of data from the Chinese Family Panel Study in 2010(CFPS2010), this paper proposes two pathways through which family ...

  22. COVID-19 and the widening learning gap

    New evidence on school segregation and racial academic achievement gaps, Stanford Center for Education Policy Analysis working paper, number 19-06, September 2021, cepa.stanford.edu. An inclusive economic recovery will be important to avoid further exacer­bating widening gaps in learning outcomes.

  23. Single-Parent Households and Children's Educational Achievement: A

    Second, research shows that children in single-parent households score below children in two-parent households, on average, on measures of educational achievement (Amato, 2005; Brown, 2010; McLanahan and Sandefur, 1994). The combination of these two observations suggests that the rise in single parenthood has lowered (or slowed improvements in ...

  24. 'It depends': what 86 systematic reviews tell us about what strategies

    The gap between research findings and clinical practice is well documented and a range of interventions has been developed to increase the implementation of research into clinical practice [1, 2].In recent years researchers have worked to improve the consistency in the ways in which these interventions (often called strategies) are described to support their evaluation.

  25. Reframing Educational Outcomes: Moving beyond Achievement Gaps

    In biology education research, many papers still use the language of "achievement gap," even in instances when researchers explicitly or implicitly use other nondeficit frameworks. While some may argue that this language merely describes a pattern, its origin and history is explicitly and inextricably linked to deficit-thinking models ...

  26. 2024 Chemistry Olympiad round one results

    The 2024 paper covered topics such as the composition of the FIFA 2023 Women's World Cup trophy, iodate salts, fuel-producing bacteria, the MRI contrast agent gadopiclenol and sulfur-containing molecules in the atmosphere.

  27. Making cities mental health friendly for adolescents and young adults

    Urban life shapes the mental health of city dwellers, and although cities provide access to health, education and economic gain, urban environments are often detrimental to mental health1,2.

  28. Mary Beth Bruder, Ph.D., Awarded the Council for Exceptional Children's

    Recipients of the Special Education Lifetime Achievement Award demonstrate CEC's core values: visionary thinking, integrity, and inclusiveness. Founded in 1922, The Council for Exceptional Children is a professional association of educators dedicated to empowering educators who work with individuals with disabilities.