case study about mathematical anxiety

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• Published: 25 February 2021

What impact does maths anxiety have on university students?

• Eihab Khasawneh   ORCID: orcid.org/0000-0002-9106-9008 1 , 2 ,
• Cameron Gosling 1 &
• Brett Williams 1  

BMC Psychology volume  9 , Article number:  37 ( 2021 ) Cite this article

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Maths anxiety is defined as a feeling of tension and apprehension that interferes with maths performance ability, the manipulation of numbers and the solving of mathematical problems in a wide variety of ordinary life and academic situations. Our aim was to identify the facilitators and barriers of maths anxiety in university students.

A scoping review methodology was used in this study. A search of databases including: Cumulative Index of Nursing and Allied Health Literature, Embase, Scopus, PsycInfo, Medline, Education Resources Information Centre, Google Scholar and grey literature. Articles were included if they addressed the maths anxiety concept, identified barriers and facilitators of maths anxiety, had a study population comprised of university students and were in Arabic or English languages.

Results and discussion

After duplicate removal and applying the inclusion criteria, 10 articles were included in this study. Maths anxiety is an issue that effects many disciplines across multiple countries and sectors. The following themes emerged from the included papers: gender, self-awareness, numerical ability, and learning difficulty. The pattern in which gender impacts maths anxiety differs across countries and disciplines. There was a significant positive relationship between students’ maths self-efficacy and maths performance and between maths self-efficacy, drug calculation self-efficacy and drug calculation performance.

Maths anxiety is an issue that effects many disciplines across multiple countries and sectors. Developing anxiety toward maths might be affected by gender; females are more prone to maths anxiety than males. Maths confidence, maths values and self-efficacy are related to self-awareness. Improving these concepts could end up with overcoming maths anxiety and improving performance.

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Introduction

Maths anxiety can be defined as a feeling of tension, apprehension and anxiety that interferes with maths performance ability the manipulation of numbers and the solving of mathematical problems in a wide variety of ordinary life and academic situations [ 1 ]. According to Olango [ 2 ] maths anxiety consists of an affective, behavioural and cognitive response to a perceived threat to self-esteem that occurs as a response to situations involving mathematics. Maths anxiety, which is rooted in emotional factors, can be differentiated from dyscalculia, which is characterized by a specific cognitive deficit in mathematics [ 3 ], in two ways. Firstly, maths anxiety can exist in people who have maths capability even though they may dislike maths. Secondly, maths anxiety has an emotional component which is not the case in dyscalculia [ 4 ].

Maths anxiety may occur in all levels of education from primary school to university education. Harari et al. [ 5 ] reported that negative reactions and numerical confidence are the most salient dimensions of maths anxiety in a sample of first-grade students. Similar findings were also observed at tertiary levels across multiple disciplines, including health care professions. For example, Roykenes and Larsen [ 6 ] studied 116 baccalaureate nursing students and found that there was a negative relationship between previous mathematic likes/dislikes and self-assessment of mathematic ability.

Many factors may contribute to or facilitate the maths anxiety. These factors or facilitators may include teachers, parents, peers and society. Negative experiences of maths learning in classroom or home can lead to maths anxiety [ 7 ]. Firstly, the teacher plays important role in making the class more attractive and reducing anxieties. Good maths teachers can create a learning environment in which students have a positive expectation about their learning [ 8 ]. Secondly, parents play an important part in developing or reducing the maths anxiety of their children. Parents' behaviours and relations with children are very important in this aspect [ 7 ]. By discussing the anxieties and the fears that their children might face, the parents are able to pinpoint any learning problem at early stage [ 8 ]. This might prevent the developing of any learning anxieties that the students might face later in life. Moreover, parents’ maths anxiety causes their children to learn less maths over the school year and to have more maths anxiety by the school year's end [ 9 ]. Thirdly, peers play important role in facilitating maths anxiety [ 7 ]. Peers at any stage of learning may have a negative impact on their colleagues, for example when students might feel inferior in front of their colleagues when they make mistakes [ 7 ]. Finally, society can contribute to the development of maths anxiety due to the misconception about mathematics, or maths myths [ 7 ].

Maths anxiety has negative impacts on individuals; many students who suffer from mathematics anxiety have little confidence in their ability to do mathematics and tend to take the minimum number of required mathematics courses, which greatly limits their career [ 10 ]. Fortunately, certain strategies can act as barriers, or prevent maths anxiety occurring. Uusimaki and Kidman [ 11 ] stated that whenever the persons become self-aware of maths anxiety and its consequences, their abilities to overcome it might increase [ 11 ]. On the other hand, activity-based learning and online/distance learning may reduce the fear of looking stupid in front of peers [ 12 ]. Another strategy is the use of untimed/unassessed (low stakes) tests to reduce the maths anxiety as well as to increase confidence [ 13 ]. Relevancy of studying maths can reduce maths anxiety; applying mathematics and statistics to real-life examples rather than pure maths can reduce maths anxiety [ 13 ].

Empirical investigations first began on maths anxiety in the 1950s, and Dreger and Alken [ 14 ] introduced the concept of maths anxiety to describe students’ attitudinal difficulty with maths. The aim of this study was to identify the facilitators and barriers of maths anxiety in university students using a scoping review methodology.

A scoping review methodology was used in conducting this study to allow for a greater breadth of literature to be investigated. Scoping reviews identify and map existing literature on a selected subject. This scoping review utilised the Arksey and O’Malley framework which includes six methodological steps: identifying the research question, identifying relevant studies, selecting studies, charting the data, collating, summarising and reporting the results and consulting experts [ 15 ]. The scoping approach systematically maps and reviews existing literature on a selected topic [ 16 ] including evidence from both peer-reviewed research and the non-peer reviewed literature.

Identify the research question

After several review iterations, the research team agreed on the question that guided this review: What are the barriers and facilitators of maths anxiety in university students? This question was broad so it could cover a wide literature in different disciplines that allowed a better summary of the available literature.

Identify relevant studies

A list of search terms was compiled from the available literature and previous research into maths anxiety and students. Suitable Medical Subject Headings (MeSH) terms and free text keywords were identified (Table 1 ). A search of databases included: Cumulative Index of Nursing and Allied Health Literature (CINHAL), Embase, Scopus, PsycInfo, Medline, ERIC, Trove, Google Scholar and Grey literature. The search involved any related studies from July-2018 backward. Studies in Arabic and English languages were filtered from the search yield and the abstracts scanned. The databases search were conducted by one of the researchers (EK). The search yield resulted in 656 records which were exported to EndNote17 referencing for screening.

Duplicates and irrelevant studies were removed by one of the researchers (EK) and potentially relevant abstracts were complied. The selection process was conducted at two levels: a title and abstract review and full-text review. The title and abstract of the retrieved studies were independently screened (EK and BW) for inclusion based on predetermined criteria. In the second stage, the selected studies full text of potentially eligible studies were assessed and inclusion confirmed by two of the authors (EK and BW). After removing the duplicates, (EK and BW) conducted the title and abstract review of 656 articles. After applying the inclusion criteria 20 articles resulted. These 20 articles were reviewed by (EK and BW) for the second time which ended in 10 articles to be involved in the scoping review.

Study selection (Fig.  1 )

Flow chart of study selection

Articles that met the following inclusion criteria were selected.

Research articles (of any design) available in full text.

The article addressed the maths anxiety concept.

The article identified the barriers and the facilitators of maths anxiety.

The article had a study population comprised of university students.

The article was in Arabic or English languages.

Articles that are systematic and scoping reviews, abstracts, editorials and letters for editors were excluded.

Charting the data

This stage allows data extraction from the included studies for more data description. A narrative review method was used to extract the data from each study. Narrative reviews summarise studies from which conclusions can be drawn into more holistic interpretation by the reviewers [ 17 ]. The data included: the author and the year of publication, the country the study was conducted in, the study design or type, the sample size, results and the theme emerges from the study (Table 2 ). Four themes emerged following full-text review of the 10 included papers, these included: gender, self-awareness, numerical ability and learning difficulties.

Collating, summarising and reporting the results

The data extracted from the included studies are reported in Table 2 . The table shows a summary of the selected articles in this scoping review study. It presents data on the different scales used to evaluate the maths anxiety across the different disciplines. Key outcome data from each of the included studies is presented and includes some of the causes or predictors of maths anxiety in university students such as gender and self-efficacy.

Consultation (optional)

Two experts were contacted for consultation to ensure no new or existing literature was missed; however no new articles were added following this consultation.

Maths anxiety is an issue that effects many disciplines across multiple countries and sectors. Literature analysed in this scoping review spanned disciplines as diverse as education, engineering, health and science while covering diverse geographical locations such as United States (US), Austria, United Kingdom (UK), Israel, Portugal and Canada. The included articles utilised an array of varied study designs, including, cross-sectional, randomised control trial, and prospective cohort studies. The main themes that emerged from this review include gender, self-awareness, numerical ability, and learning difficulty each of these will now be synthesised and discussed.

Six articles addressed the gender concept; two American studies, three European and one Israeli study with mixed findings for the role gender plays in maths anxiety. Some of these articles found that gender has a role in maths anxiety [ 18 , 18 , 20 , 21 ], while others found there was no significant difference between males and females [ 20 , 22 ]. For example, a study of female psychology students in the US reported more maths anxiety than males [ 19 ] whereas there was no significant difference between males and females in maths anxiety in psychology students reported in the UK [ 20 ]. Psychology female students in the US [ 19 ] and Austria [ 21 ], and social science and education female students in Israel showed more maths anxiety than male students [ 22 ]. While in another study there was no significant difference in maths anxiety between males and females in the Portuguese engineering students [ 23 ].

The reasons why females frequently report higher maths anxiety than males is not well understood [ 24 ]. One explanation might be the different gender socialisation during childhood may differentially affect the anxiety experienced by males and females in certain situations which is known as the sex-role socialization hypothesis [ 24 ]. The sex-role socialization hypothesis argues that because mathematics has been traditionally viewed as a male domain, females may be socialised to think of themselves as mathematically incompetent and therefore females may avoid mathematics. When females do participate in mathematical activities they may experience more anxiety than males [ 24 ].

The pattern of gender effect on maths anxiety is different among disciplines and countries. In a recent study, Paechter et al. [ 21 ] administered the Revised Maths Anxiety Ratings Scale (R-MARS) to 225 psychology students at the University of Graz, Austria. This study showed that there were three antecedents of maths anxiety. Firstly, female gender who reported a higher level of maths anxiety β  = − 0.660. Secondly, a high proneness to experience anxiety in general report higher levels of maths anxiety β  = 0.385. Finally, poor grades in maths. According to Paechter et al. [ 21 ] maths anxiety is inversely related to maths grades β  = 0.393. Of the above three factors, female gender was the most strongly related to maths anxiety and is supported by the findings of other studies such as Devine et al. [ 23 ]. Developing anxiety toward maths might be effected by gender and highlights a specific area for future empirical work.

Self-awareness

Self-awareness helps people to manage themselves and improve performances while the opposite is true that lacking self-awareness leads to making the same mistakes repeatedly [ 25 ]. Being self-aware enables us to determine our strengths and areas that can be improved [ 25 ]. Four studies addressed the self-awareness concept in relation to maths anxiety, one American study, one UK study, one Israeli study and one Portuguese study. Under the self-awareness theme, a number of other subthemes emerged including self-efficacy, maths confidence, maths value, maths barriers and performance. McMullan et al. [ 26 ] developed a Drug Calculations Self-Efficacy Scale that measured critical skills of medication calculations (dose of liquid oral drugs, solid drugs, injections, percentage solutions and infusion and drip rates). McMullan et al. [ 26 ] reported that there was a significant positive correlation between students’ maths self-efficacy and maths performance and between maths self-efficacy, drug calculation self-efficacy and drug calculation performance. Low level of maths anxiety was demonstrated by 10% of the students, medium level by 70% and high level by 20% of the students. McMullan et al. [ 26 ] also noted that numerical skills can be improved by remedial approaches as lectures, study groups, workshops and computer assisted instructions [ 27 ]. The authors suggested that the lectures should be more student-directed not only didactic in nature. Study groups increase the cooperation and encourage students to exchange and clarify information leading to improve the self-efficacy.

Maths confidence, maths value and maths barriers are related to maths behaviour and performance. Hendy et al. [ 28 ] studied maths behaviours in 368 university maths students. They reported maths behaviours (attending class, doing homework, reading textbooks and asking for help) at week 8 of the 15 week-semester using self-reported questionnaires. The aim of their study was identify the subclasses of maths beliefs and their role in maths behaviours. The most commonly reported maths belief was maths confidence (mean rating = 3.79, SD = 0.90). This study reported that students with low maths confidence or high maths anxiety might benefit from the maths self-evaluation and self-regulation interventions. These interventions utilised suggestions which include: maths skills are learnable not innate, assessing current skills and believing in their development abilities, teaching student the specific strategies to solve maths problems and keeping self-regulatory records to track development in overcoming maths anxiety. These interventions may be used in overcoming maths anxiety. This study outlined the approach to develop interventional teaching methods that can be applied to students or course curriculum to help in reducing maths anxiety. Self-awareness might determine the person’s areas of strength that might help future career selection. Self-efficacy, maths confidence and values, maths barriers and performance are factors that related to self-awareness. Assessing these factors can determine the methods of improving self-awareness which may end in overcoming maths anxiety.

Numerical ability

Two articles addressed the numerical ability concept [ 25 , 2 ]. In their efforts to understand the origin of maths anxiety, Maloney et al. [ 29 ] investigated the processing of symbolic magnitude by high and low maths anxious individuals. They reported that high maths anxious individuals have less precise representations of numerical magnitude than their low maths anxious peers. Two experiments were performed on 48 undergraduate students in the University of Waterloo. A single Arabic digit in 18-font Arial font was presented at fixation. Numbers ranged from 1–4 to from 6–9. The participants were told to identify whether the number above five or below it. This study revealed that high maths anxious individuals have a less precise representation of numerical magnitude than the low maths anxious individuals. The results suggest that maths anxiety is associated with low level numerical deficits that compromise the development of higher level mathematical skills.

On the other hand, McMullan et al. [ 26 ] reported that numerical ability and maths anxiety are the main personal factors that might influence drug calculation ability in nursing students. The numerical ability test (NAT), used by McMullan et al. [ 26 ], is comprised of 15 questions that covered calculation operations like multiplication, addition, fraction, subtraction, percentage, decimals and conversion. McMullan et al. [ 26 ] reported that both numerical ability and drug calculation abilities of the participants (229 UK nursing students) were poor which might have been to an over-reliance on using calculators or not having adequate maths education in the past. Improving numerical ability and reducing maths anxiety can be achieved through teaching in a supportive environment using multiple teaching strategies that address the needs of all students and not being didactic [ 26 ]. Examples of these strategies include: accept and encourage students creative thinking, tolerate dissent, encourage students to trust their judgments, emphasise that everyone is capable of creativity, and serve as a stimulus for creative thinking through brainstorming and modelling [ 30 ].

Learning difficulty

Australian surveys have indicated that 10 to 16 per cent of students are perceived by their teachers to have learning difficulties according to Learning Difficulty Australia (LDA) (2012). Within the population of students with learning difficulties, there is a smaller subset of students who show persistent and long-lasting learning impairments and these are identified as students with a learning disability. It is estimated that approximately 4 per cent of Australian students have a learning disability (LDA 2012).

In this scoping review, one UK study addressed this concept, comparing undergraduate psychology students who represent 71% of the sample and nursing students who represent 14% of the sample who either had dyslexia ( n  = 28) or were assigned to the control group ( n  = 71). In 2014 Jordan et al. [ 31 ] reported that students with dyslexia had higher levels of maths anxiety relative to those without [ 31 ]. This study showed that significant correlations with maths anxiety were found for self-esteem ( r  = − 0.327; n  = 99, p .001), worrying ( r  = 393; n  = 99; p  < 0.001 the denial ( r  = 0.238; n  = 99; p  = 0.018, seeking instrumental support ( r  = 0.206; n  = 99; p  = 0.040 and positive reinterpretation ( r  = − 0.216; n  = 99; p  = 0.032). In addition, this study found that seeking instrumental support served as an indicator of students at high risk of maths anxiety. In explaining variation in maths anxiety. Jordan et al. [ 31 ] claimed that 36% of this variation is due to dyslexia, worrying, denial, seeking instrumental support and positive reinterpretation. The limitation of this study is that not all dyslexia cases were disclosed by the students. As long as some of the students with dyslexia are not reported, the generalisation of this study would be limited. This study recommends positive reframing and thought challenging as techniques to overcome difficult emotions and anxiety.

Limitations and future research

While multiple databases were used in this scoping review, some articles may be missed due to using specific terms in the search strategy. The disciplines covered in this scoping review were psychology, engineering, mathematics and some of the health disciplines such as nursing. Future research might focus on numerical ability and maths anxiety in university students who need maths and calculation in their future careers as engineers and health care professionals.

For example, the relationship between medication and drug calculation errors and maths anxiety in the health care field can be researched. Moreover, the relationship between self-awareness and numerical ability and maths anxiety and their impact on the performance and ability of the university students can be a future research topic. Finally, developing a new teaching package or strategy that reduces maths anxiety can be tested on university students.

Maths anxiety,which is an issue that affects many disciplines across multiple countries and sectors, is affected by gender, self-awareness, learning difficulties and numerical ability. Maths anxiety and its contributing factors at tertiary education should be researched more in the future addressing interventions and strategies to overcome maths anxiety. Maths anxiety level measuring tools should be used in determining its level among university students.

Availability of data and materials

It is a scoping review and all the articles that are analysed in this review are listed in the references section.

Abbreviations

Cumulative Index of Nursing and Allied Health Literature

Education Resources Information Centre

High Maths Anxious

Learning Difficulty Australia

Low Maths Anxious

Maths Barrier Scale

Maths Confidence Scale

Medical Subject Headings

Maths Value Scale

United Kingdom

United States

Revised Maths Anxiety Rating Scale

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EK conceived, designed and carried out the study, interpreted the analysis, and drafted and revised the manuscript. BW conceived, designed, drafted and revised the manuscript. CG helped conceive, drafted and revised the manuscript. All authors read and approved the final manuscript.

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Khasawneh, E., Gosling, C. & Williams, B. What impact does maths anxiety have on university students?. BMC Psychol 9 , 37 (2021). https://doi.org/10.1186/s40359-021-00537-2

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Systematic review article, reducing math anxiety in school children: a systematic review of intervention research.

• 1 Institute of Educational Research, University of Wuppertal, Wuppertal, Germany
• 2 Institute of Special Education, Leibniz University Hannover, Hanover, Germany
• 3 Department of Psychology, Federal University of Minas Gerais, Belo Horizonte, Brazil

Recent studies indicate that math anxiety (MA) can already be found in school-aged children. As early MA depicts a potential risk for developing severe mathematical difficulties and impede the socio-emotional development of children, distinct knowledge about how to reduce MA in school-aged children is of particular importance. Therefore, the goal of this systematic review is to summarize the existing body of research on MA interventions for children by identifying the approaches, designs, and characteristics as well as the effects of the interventions.

1 Introduction

In the last decade, a considerable amount of research focused on math anxiety (MA). Ramirez et al. (2018) sum up results of across 65 countries that participated in the 2012 PISA survey and highlight that “33% of 15-year-old students, on average, reported feeling helpless when solving math problems” (p.146). In accordance with the high prevalence in this age group, the majority of existing studies addressed MA in adolescents and young adults. However, more recent research described MA as early as in primary school children ( Ramirez et al., 2013 ; Cargnelutti et al., 2017 ; Gunderson et al., 2018 ; Sorvo et al., 2019 ; Primi et al., 2020 ) and highlighted negative impacts of early MA on their short- and long-term development and performance in mathematics ( Sorvo et al., 2017 ; Namkung et al., 2019 ; Zhang et al., 2019 ; Barroso et al., 2021 ). However, until now little attention has been paid to the investigation of interventions aiming at the reduction of MA in children ( Passolunghi et al., 2020 ). The paper at hand aims to systematically review the existing literature on interventions and approaches that target to reduce MA in school-aged children.

2 Theoretical Background

2.1 definition of ma.

MA can generally be defined as an “anxiety that interferes with manipulation of numbers and the solving of mathematical problems in a wide variety of ordinary life and academic situations” ( Richardson and Suinn, 1972 , p.551). There is, however, no consensus on the exact operationalization of MA in the field (e.g., Dowker et al., 2016 ). One important step towards a more precise definition and operationalization of MA is offered by the distinction of MA into trait and state anxiety. According to Spielberger (1972) trait anxiety refers to a relatively enduring individual disposition to feel anxious, whereas state anxiety refers to temporary and situational feelings of anxiety. Current MA studies either assess anxiety in math-related situations using hypothetical/retrospective questions (e.g., “How anxious would you feel if … ”) or assess anxiety about failure in math (e.g., “How worried are you if you have problems with … ”). The first type of question allows assumptions about state-like MA as not administered within the actual situation, the second type of question provides indications about trait MA ( Sorvo et al., 2017 ; Orbach et al., 2019 ). Considering empirical discrepancies between MA self-reports (see questions above) and real-time assessments ( Bieg, 2013 ), nowadays more studies apply questionnaires assessing state-MA within the actual mathematical situation (e.g., Vanbecelaere et al., 2021 ).

2.2 Explaining MA in Children

The development of MA and its relation to math performance has been investigated in only a few longitudinal studies ( Sorvo et al., 2019 ). According to these studies different etiological pathways have been suggested ( Carey et al., 2017 ; Sorvo et al., 2019 ) and it has been assumed that the MA-performance link is bidirectional ( Carey et al., 2016 ; Foley et al., 2017 ). In other words, MA can be considered as both the cause and the outcome of poor math performance ( Young et al., 2012 ).

Accordingly, MA could be elicited or increased over time because of math difficulties that often originate in early school years ( Ramirez et al., 2018 ). Ramirez et al. (2018) define this as reduced competency account and explain this link in two ways: A first explanation might be seen in lower numerical/spatial abilities which lead to underperformance in math and consequently to MA. Barroso et al. (2021) describe this association as the “deficit model” of MA (p.136). Ramirez et al. (2018) further summarize, that a second explanation could be seen in avoidance behavior that amplifies the development of math difficulties and consequent MA. In line with this, Ashcraft and Moore (2009) state that “avoidance of math is an overriding characteristic of math-anxious individuals” (p. 201). Therefore, experiencing math difficulties might cause a “vicious circle” ( Dowker et al., 2016 ) in which students avoid math-related situations leading to fewer opportunities to improve their math skills. Ramirez et al. (2018) consequently argue, that according to the assumption that MA may be the outcome of poor math performance, “interventions that aim to improve students’ math skills may be effective” to reduce MA (p. 156). Consequently, recent studies suggest a positive effect of mathematical interventions (MI) on MA in school children (e.g., Supekar et al., 2015 ; Passolunghi et al., 2020 ; Vanbecelaere et al., 2020 ).

Performance-inhibiting effects might, however, also be caused by MA. Such types of MA might be originally developed from environmental factors (e.g., adult role models: Casad et al., 2015 ; Lin et al., 2017 ) and genetic dispositions ( Wang et al., 2014 ; Malanchini et al., 2017 ). Such MA-related impacts on mathematical performance might be explained by the disruption of executive function processes and working memory ( disruption account ; Ramirez et al., 2018 ). This disruption may be caused by math-related worries (e.g., negative thoughts and rumination about one’s abilities or the consequences of failure). As a result, MA-evoking situations interfere with available cognitive resources (e.g., working memory) (e.g., Ramirez et al., 2013 ; Pizzie et al., 2020 ). Therefore, less resources are available for task-related problem-solving processes (e.g., arithmetical strategies). This might lead children either to switch to less sophisticated strategies (e.g., production deficiencies ) or apply advanced strategies unsuccessfully (e.g., utilization deficiencies ; Miller and Seier, 1994 ), both approaches leading to poorer performances. Barroso et al. (2021) summarize such links under the “processing efficiency theory” of MA (p.136). The links between MA and performance might additionally be influenced by the complexity of math tasks that children have to solve and the presence of time pressure. Studies using math assessments including more complex tasks show stronger MA-performance links ( Namkung et al., 2019 ; Zhang et al., 2019 ). Another stress-evoking factor might be seen in time pressure, as it seems to affect the arousal of children ( Caviola et al., 2017a ; Orbach et al., 2020 ). According to the assumption of a disruption of executive functions caused by math-related worries, cognitive-behavioral interventions (CBI) may help children to deal with maladaptive thoughts that e.g., attribute poor math grades to a lack of ability. Recent studies suggest a positive effect of CBI on MA in school children (e.g., Passolunghi et al., 2020 ).

2.3 Reducing MA in Children

With regard to the described manifold link between MA and mathematical performance, it becomes clear that reducing symptoms of MA might be a relevant approach in supporting children’s mathematical development ( Passolunghi et al., 2020 ). At the same time, the multiple explanations of the link between MA and mathematical performance might serve as a diverse foundation for designing appropriate interventional activities (e.g., addressing numerical/spatial abilities, executive functions, math self-concept). Previous work highlighted that the existing body of research can be subsumed into interventions that primarily target mathematical abilities as well as into cognitive-behavioral interventions that target anxiety related cognitions ( Dowker et al., 2016 ). Both directions can thereby be interpreted with regard to the described differential links between MA and mathematical performance.

As described, MI might be of particular relevance in light of the described reduced competency account ( Ramirez et al., 2018 ). They aim to break the vicious circle of MA and performance by promoting mathematical performance and thereby increasing math self-concept as well as decreasing MA. In line with this argument Dowker et al. (2016) propose that “interventions for children with mathematical difficulties may go some way toward preventing a vicious spiral, where mathematical difficulties cause anxiety, which causes further difficulties with mathematics” (p. 10). Similarly, math trainings moreover depict exposure interventions. Accordingly, Ramirez et al. (2018) argue that “the avoidance framework under the Reduced Competency Account states that avoidance tendencies may be responsible for the deficits in development (and explains why increased exposure is an effective solution)” (p. 156).

The effects of CBI can be mainly explained with regard to the described disruption account ( Ramirez et al., 2018 ) . Accordingly, CBI might decline the potential impact of anxiety-related cognitive processes and by that means improve mathematical performance. Dowker et al. (2016) as well as Ramirez et al. (2018) both highlight the potential impact of CBI such as re-appraisal and expressive writing on MA.

3 Objective of the Study and Research Questions

Most of the existing body of research on MA and MA interventions appears to focus on older adolescents and adults, as MA has been previously associated with more complex mathematics. At the same time, MA could already be observed in school-aged children and might be associated with early mathematical functioning and numeracy. Therefore, early identification and intervention of MA seems to be of high relevance to prevent negative developmental outcomes. As research on early MA interventions is limited, the exact conditions and characteristics of successful interventions in school-aged children remain unclear. To our knowledge, no existing work has summarized the existing evidence on the interventional approaches that target MA in childhood. Therefore, the objective of this study is to give an overview of interventional approaches in addressing MA in children and adolescents and to highlight potential characteristics of effective interventions. The study is guided by the following research questions:

1) What are the approaches, designs, and characteristics (e.g., setting, duration) of existing interventions aiming at the reduction of MA in school children?

2) What are the effects of these existing interventions?

Answers to these questions might contribute to the field of MA intervention research, as they might serve as a foundation and orientation for future intervention studies aiming at improving children’s emotional well-being and academic development in schools, especially regarding mathematics.

As MA has been addressed in previous research, we aim to identify characteristics of effective interventions based on the existing body of research. Therefore, we conduct a systematic (scoping) review. Thereby, we will describe the main findings of the included studies and highlight specific components using a narrative approach.

4.1 Search Procedure

To identify all relevant studies, we used a two-step approach. In a first step we conducted a systematic search in the most widely used electronic databases in psychological and educational research. Therefore, we focused on the databases PsycINFO and PubPsych. PubPsych is a multilingual database that includes entries from additional databases, such as PSYNDEX, MEDLINE and ERIC (Educational Resources Information Center). We used the descriptors: math (ematics) anxiety AND intervention OR treatment OR therapy OR program OR training OR tutoring OR support OR strategies OR best practice, AND alleviation as well as its synonyms reduction OR decrease OR remediation. Additionally, a German translation of the descriptors was used. To prevent the exclusion of relevant studies at an early stage no filters were used except the exclusion of dissertations as full texts are often difficult to access. We additionally identified studies by hand search, i.e., visually scanning reference lists from relevant studies or theoretical papers. The literature search was conducted in July 2020 and October 2021.

4.1.1 Inclusion and Exclusion Criteria

Studies were eligible for the systematic review if they met all the following inclusion criteria:

• Participants received intervention or a combination of interventions.

• Participants were assessed with a quantitative and/or qualitative measure of MA.

• Participants were of school-age (5–17 years old).

Studies were not eligible if they met one of the following exclusion criteria:

• The study was no intervention study (e.g., theoretical paper, literature review, meta-analysis, or correlation study).

• Participants did not match the target population (e.g., university students or (pre-service) teachers).

• The study was published in a language other than English or German.

The selection of eligible studies was conducted in two stages. Firstly, we employed an initial screening of titles and abstracts against the inclusion and exclusion criteria. Screening procedures followed PRISMA guidelines ( Moher et al., 2009 ). All studies were screened using the tool for systematic reviews Rayyan ( Ouzzani et al., 2016 ). Rayyan is an open access online application that enables a semi-automated collaborative screening process. Secondly, all studies that appeared to meet the inclusion criteria, or when a decision could not be made based on the title and/or abstract, were screened again based on their full texts.

4.2 Study Selection

The described inclusion and exclusion criteria were applied during the selection process (for an overview of the study selection process see Figure 1 ). The initial search in the databases PsycINFO and PubPsych led to the identification of 521 records. Additionally, 13 records were identified by hand search. After removing duplicates, the titles, and abstracts of 479 records were screened for potential eligibility. This step led to the exclusion of 452 records. The full texts of 27 records were consequently assessed for eligibility. As a result, three more records were excluded. These steps led to the inclusion of 24 records. A second search run was conducted in October 2021 to include most recent studies. This led to the inclusion of ten more studies. The final number of studies for the qualitative synthesis was 34.

FIGURE 1 . Study selection process following PRISMA guidelines.

4.3 Data Extraction and Coding Procedure

Next to general information about the studies, such as author(s), year of publication, and title, we extracted relevant data to address our specific research questions. Regarding our first research question (approaches, designs, and characteristics of existing interventions) we coded all information given by the author(s) about the study design, interventions, and their respective settings. This included information about the general study approach (quantitative, qualitative, mixed method), the study design (pre-post-test, follow up, control/comparison group), the operationalization of MA, as well as data about sample size and age group of the participants. Regarding the intervention we extracted information about the content as well as the intended goal of the interventions. We also coded the duration of the interventions (overall time span and number of sessions), the duration of single sessions, the intervention mode (computer-based, face-to-face), and the social arrangement (single, partner, small groups, class). Concerning our second research question (effects of these existing interventions) we coded the key results of the studies regarding the effectiveness of the intervention(s) to reduce MA as reported by the authors.

Relevant information has been coded using a spread sheet covering the previously described categories. The number of free text fields has been limited as much as possible to enable an unambiguous extraction and analysis of the data. Preferably fixed text such as yes/no decisions and drop-down lists has been used to code the data. The data extraction spread sheet has been previously piloted and adapted.

For a complete overview over all included studies (reference, sample, design, MA measure, operationalization type of MA, intervention, setting, and main findings) see Table 1 .

TABLE 1 . Overview of included studies.

5.1 Intervention Approach

Most of the included studies applied either a mathematical intervention (MI) approach (see section I in Table 1 ) or a cognitive-behavioral intervention (CBI) approach (see section II in Table 1 ). Four studies used other interventional approaches that could not clearly assigned to one of these two approaches (see section III in Table 1 ).

5.2 Sample and Study Design

The study samples differed between the two main intervention approaches (MI and CBI) in regards to the age groups of the participants. 82% of the MI studies targeted school age children (6–12 years), whereas 57% of the studies within the CBI approach focused on adolescents (13–17 years). Regarding the sample size and choice of study design there appears to be no systematic difference between MI and CBI studies. The majority of the included studies applied a quantitative study design to examine the effects of various interventions on MA. Thereby, the sample size of the included studies varies strongly, M = 138 ( SD = 171). Whilst some studies used large samples of over 300 participants ( Shapka and Keating, 2003 ; Brandenberger and Moser, 2018 ; Vanbecelaere et al., 2020 ), other studies only collected information of approximately 20 participants ( Kamann and Wong, 1993 ; Supekar et al., 2015 ; Choi-Koh and Ryoo, 2019 ). Most of the quantitative studies applied a pre-post design and included a control or comparison group. Whilst some studies used a waiting list procedure for the control group (i.e., the group received the same intervention with some time delay after the intervention group), other studies applied alternative interventions (e.g., Shapka and Keating, 2003 ; Asikhia and Mohangi, 2015 ) or applied modified version of the actual target intervention ( Kramarski et al., 2010 ; Huang et al., 2014 ). Four studies additionally followed up on their participants in the intervention and control group ( Sheffield and Hunt, 2006 ; Rauscher et al., 2017 ; Vanbecelaere et al., 2020 ; Vanbecelaere et al., 2021 ). Two of the identified studies applied single-case procedures to address potential effects of interventions on MA. LaGue et al. (2019) applied a multiple baseline approach within an experimental single-case design. Hord et al. (2018) used a qualitative approach to single-case research and focused on two eighth grade students with learning disabilities using a descriptive, qualitative microanalysis.

5.3 MA Measure

Different quantitative measures have been used to assess the level of MA (for an overview see Table 1 ). Some of the measures have been extensively researched and validated, such as the Math Anxiety Scale for children (MASC; Chiu and Henry, 1990 ) or the Math Anxiety Rating Scale—Revised (MARS-R; Plake and Parker, 1982 ). Often measures were translated and/or adapted for the specific contexts and needs of the studies. Some studies used measures that were self-developed or not as commonly known (e.g., Kramarski et al., 2010 ; Tok et al., 2015 ; Singh, 2016 ) Also, qualitative measures such as observational field notes and self-talk recordings have been used ( Kamann and Wong, 1993 ; Hord et al., 2018 ). According to the differentiations by Sorvo et al. (2017) and Orbach et al. (2019) , one study ( Vanbecelaere et al., 2021 ) used a real-time assessment measuring individuals math-related anxiety reaction during a math test situation (state-MA), 19 studies (approx. 54%) applied questionnaires with hypothetical/retrospective questions asking how anxious the individual would feel during a math-related situation (anxiety in math-related situations/statelike-MA) and nine studies (approx. 26%) used questionnaires with hypothetical/retrospective questions about anxiety in math-related situations (statelike-MA) and questions focusing anxiety about failure in math (trait-MA). Two studies used unclassifiable qualitative approaches ( Kamann and Wong, 1993 ; Hord et al., 2018 ). Four studies provided no clear information about the MA operationalization ( Idris, 2006 ; Lavasani et al., 2012 ; Mehdizadeh et al., 2013 ; Huang et al., 2014 ).

5.4 Intervention Activity

5.4.1 mathematical interventions.

The MI covered a wide range of different activities and programs, such as educational games or formalized math programs. Due to the amount of activities, only selected studies are presented in more detail below. The study selection does not constitute an evaluation of the quality of the studies. For a comprehensive overview of all MI see the first section of Table 1 .

Alanazi (2020) , Huang et al. (2014) , and Vanbecelaere et al. (2021) investigated the effect of educational math games on MA and performance in primary school children. The intervention group in Alanazi (2020) study participated in face-to-face recreational math games (e.g., movement games containing mathematical problems) in addition to their regular math teaching. The comparison group received regular math teaching. The intervention group obtained lower MA scores and higher math performance than the control group. Huang et al. (2014) and Vanbecelaere et al. (2021) applied a digital game-based learning approach. Huang et al. (2014) designed a digital math game to train basic arithmetic operations that provided the children in the intervention group with interactive diagnostic feedback. The children in the comparison group also played the game but without diagnostic feedback. Both groups obtained lower MA scores and enhanced levels of learning motivation. Vanbecelaere et al. (2021) compared an adaptive version with a nonadaptive version of the Number Sense Game ( Maertens et al., 2016 ). The Number Sense Game contained two types of exercises, a comparison game and a number line estimation game. Both groups obtained lower MA scores and improved on early numeracy skills.

Jansen et al. (2013) , Rauscher et al. (2017) , and Supekar et al. (2015) investigated the effect of formalized math training programs on primary school students’ math performance and anxiety. Jansen et al. (2013) and Rauscher et al. (2017) applied specific math training software, namely Math Garden ( Klinkenberg et al., 2011 ) and Calcularis ( Käser et al., 2013 ). In Jansen et al. (2013) study the control group received regular math teaching. Both groups obtained lower MA scores and the math performance only improved in the intervention group. Rauscher et al. (2017) compared the intervention group with two control groups; one was a waiting list group, the other received a control training. The results showed that the intervention group obtained lower MA scores than the waiting list control group, but there was no difference in MA between the intervention group and the control training group. Supekar et al. (2015) examined an adaption of MathWise ( Fuchs et al., 2013 ), a training program that aims to improve number knowledge, counting speed and the application of calculation strategies. Comparing children with high MA and low MA levels, the children with high MA significantly decreased their MA. In regards to math performance both groups benefited equally from the training.

5.4.2 Cognitive-Behavioral Interventions

The CBI also included different techniques and activities, such as coping strategy training or expressive writing. Due to the amount of activities, only selected studies are presented in more detail below. The study selection does not constitute an evaluation of the quality of the studies. For a comprehensive overview of all CBI see the second section of Table 1 .

Collingwood and Dewey (2018) , Kamann and Wong (1993) , Passolunghi et al. (2020) , and Ruff and Boess (2014) investigated the effect of coping strategy trainings on primary school students’ MA. Kamann and Wong (1993) examined a coping strategy based on cognitive behavior modification ( Meichenbaum, 1977 ) to reduce MA. They compared children with and without learning disabilities (LD) providing both groups with sample self-instruction statements on cue cards to assist them in applying those statements at each level of the coping process. The LD group showed increased positive self-talk compared to the group without LD indicating enhanced coping with MA. Collingwood and Dewey (2018) examined a multi-dimensional cognitive intervention called Thinking your problems away ( Martin, 2008 ) that encouraged (among other things such as self-regulation) the use of positive-self-coping statements based on Kamann and Wong (1993) . The control group was a waiting list control group. The intervention group showed no reduction of MA or enhancement of math self-concept but higher math performance than the control group. Passolunghi et al. (2020) trained the primary school children in strategy-based techniques (among others things such as the recognition of emotions) to decrease their MA. These techniques included breathing exercises, safe place visualizations and re-appraisal of negative thoughts based on Ellis and Bernard (2006) . The control group received a control training composed of playful activities with comic strips. The intervention group obtained lower MA scores but no increase in math performance compared to the control group.

Hines et al. (2016) and Ruark (2021) investigated the effect of expressive writing on MA in secondary school students. In the intervention group of Hines et al. (2016) study the participants wrote about their math related feelings 15 min a day for 3 days. The control group did the same amount of expressive writing but on a neutral topic. The intervention group reported reduced levels of general anxiety and MA, whereas the control group also indicated reduced levels of MA. The students in Ruark (2021) study wrote about their math homework problems every day for 2 weeks. The intervention group was requested to write about their feelings when encountering problems during math homework for at least 1 minute. The control group wrote about their math homework problems only. Both groups showed no reduction of MA.

5.5 Intervention Mode and Setting

The interventions were either carried out face-to-face (67.6%) or via computer (23.5%). Three studies (8.8%) did not fit into one of the two categories. Segumpan and Tan (2018) used both settings—face-to-face and computer—as they investigated the effect of a Flipped Classroom on secondary school students’ MA and performance. In Hines et al. (2016) and Ruark (2021) studies the participants performed expressive writing activities at home without specifications whether to use paper and pencil or a computer.

Within the mathematics intervention approach computers were predominantly used to train basic arithmetic operations in primary school children (e.g., Mevarech et al., 1991 ; Jansen et al., 2013 ; Huang et al., 2014 ; Rauscher et al., 2017 ). Jansen et al. (2013) , Rauscher et al. (2017) , and Vanbecelaere et al. (2021) explicitly mentioned the adaptivity of their training software, i.e. the selection of training tasks was regulated by an adaptive algorithm ( Klinkenberg et al., 2011 ). The only study within the CBI approach that utilized computers was Kim et al. (2017) . In this study secondary school students were guided through a computer-based learning environment by a so-called embodied agent. The learning environment covered fundamental algebra topics. In the intervention group the embodied agent provided not only instructional guidance (control condition) but also anxiety treating messages. Results indicated that both groups obtained lower MA scores and higher math performance. All other CBI were conducted face-to-face.

The interventions were either held in classrooms (29.4%), small groups (32.4%), or individual settings (26.5%). Four studies (11.8%) did not specify the setting of their intervention. There were no significant differences between the settings in regards to the intervention approach.

5.6 Intervention Length

On average, the included studies applied interventions for M = 7.04 weeks ( SD = 6.78). However, the span of the overall duration was large. The interventions ranged between a 1-h session ( Sheffield and Hunt, 2006 ) and one school year ( Brandenberger and Moser, 2018 ). Similarly, the number of training sessions varied between the included studies, M = 10.51 sessions ( SD = 7.86). Again, the span of the number of sessions was large. The interventions took between one session (e.g., Sheffield and Hunt, 2006 ) and 30 sessions ( Rauscher et al., 2017 ). Accordingly, the number of sessions per week differed, M = 2.6 sessions/week ( SD = 1.4). Moreover, the duration of the individual session varied, M = 46.82 min ( SD = 19.85), ranging from 15 min (e.g., Jansen et al., 2013 ) to 90 min of intervention time (e.g., Asanjarani and Zarebahramabadi, 2021 ) in each session.

5.7 Intervention Effects on MA

The intervention effects reported by the authors were mixed. 59% of the studies reported a positive effect of the intervention on MA in the intervention group compared to no effect in the control/comparison group (e.g., Kramarski et al., 2010 ; Tok et al., 2015 ; Alanazi, 2020 ; Passolunghi et al., 2020 ). In Passolunghi et al. (2020) study math strategy training influenced and improved not only math ability, but also contributed to a decrease in students’ MA level. In the same study the cognitive-behavioral MA training showed only effects in reducing MA level, but there was no improvement of math abilities. Verkijika and De Wet (2015) provided evidence that MA could be effectively reduced by means of neuropsychological feedback while playing a math game. LaGue et al. (2019) reported positive effects of mindfulness-based cognitive therapy on students’ MA levels using an experimental single-case study design.

21% of the studies found a positive effect of intervention(s) on MA in both the intervention as well as the control/comparison group (e.g., Jansen et al., 2013 ; Huang et al., 2014 ; Hines et al., 2016 ; Kim et al., 2017 ; Arias Rodriguez et al., 2019 ). Rauscher et al. (2017) showed that students who trained with the online math training Calcularis obtained significant lower MA scored compared the waiting list control group (intervention vs. waiting list control group). When compared to the control group that received a control training MA was, however, reduced equally in both groups (intervention vs. control training). Other studies reported a positive effect of the intervention(s) on MA for certain groups of students, such as highly anxious ( Supekar et al., 2015 ; Choi-Koh and Ryoo, 2019 ) or low achieving students (e.g., Mevarech et al., 1991 ).

15% of the studies did not find a positive effect of the intervention on the students’ level of MA (e.g., Shapka and Keating, 2003 ; Tok, 2013 ; Collingwood and Dewey, 2018 ; Vanbecelaere et al., 2020 ). Collingwood and Dewey (2018) reported a positive impact of intervention on the mathematical performance of students in the intervention group, however, no significant impact on the level of MA. Tok (2013) also found increased achievement after teaching students to use the Know-Want-Learn strategy as well as improved metacognitive abilities, but no significant impact on MA. Shapka and Keating (2003) did not find evidence that girls-only math teaching would reduce female students’ MA in comparison to co-educated math teaching.

The findings did not differ in relation to the applied MA questionnaires. The only study that used a real-time assessment (state-MA) reported a positive effect of a math training on MA, approx. 80% of the studies using questionnaires with hypothetical/retrospective items (statelike-MA/anxiety in math-related situations) reported lower MA after the intervention and approx. 90% of the studies using questionnaires focusing anxiety about failure (trait-MA) and anxiety in math-related situations (statelike-MA) reported lower MA after the intervention.

6 Discussion

The goal of this study was to summarize the existing body of research on MA interventions for school children. Therefore, we conducted a systematic (scoping) review and presented the results in a narrative manner. Table 1 gives a comprehensive overview of the included studies and their main characteristics. Note that not all studies provided all relevant information.

Generally, the overall number of eligible studies identified in this review was still relatively small, for example compared to general mathematical intervention studies ( Reynvoet et al., 2021 ). Given the potential negative impact of early MA on children’s short- and long-term development, one would have expected a greater attention to this field of research. This finding indicates that research on MA interventions is still emerging. The fact that most studies included in this review are relatively recent underpins this assumption. At the same time, the categorization of interventions into either MI or CBI as described in adults, can be similarly found in MA research in children and adolescents. The application of both approaches might be justified by different explanations of the MA-performance link (e.g., the reduced competency account and the disruption account of MA; Ramirez et al., 2018 ). Our findings do not justify any judgments on potential empirical advantages of either approach, as no direct comparisons of the described effects are possible. Future meta-analyses are required to address this issue. At the same time, our findings give qualitative insights into the existing body of research in MA interventions.

More than half of the included studies primarily focused on math performance rather than MA. Hence, MA was often assessed as an affective covariate but was not necessarily the actual target of the intervention. Despite that, almost half of the included MI still reported a positive side-effect of the intervention on students’ MA compared to the control/comparison group. This supports the assumption that MI can reduce anxiety responses, but might also allow children to re-evaluate dysfunctional cognitive beliefs (“I am bad at math”) and to stimulate the formation of new basic cognitive assumptions (e.g., increase of math self-concept).

As for the CBI, more than half of the included studies reported a positive effect of the intervention on the level of MA compared to the control/comparison group. At the same time, the effect of CBI on math performance was comparatively low. One possible explanation could be that the physiological arousal that comes with an anxious response (e.g., increased heart rate, faster breathing) can also support performance. Therefore, reducing this arousal through breathing or self-regulation exercises might not always be beneficial to enhance performance. Instead re-appraising the arousal as a sign of challenge or excitement rather than threat, might help children to capitalize on the performance enhancing effects of their physiological response see Biopsychological model of Challenge and Threat, ( Blascovich, 2008 ). Similar effects have already been observed in adults (e.g., Brooks, 2014 ; Jamieson et al., 2016 ).

The mixed effects of the MI and CBI on MA and performance might indicate that a combination of both approaches could be most beneficial for school children. This means, on the one hand, to develop sound arithmetic skills that build not only the foundation for more complex math content but would also help children to form a positive math self-concept. On the other hand, combined interventions could also provide children with cognitive-behavioral tools to cope with their anxious thoughts and arousal in math related situations. These tools should, however, take effect models into account, such as the Biopsychological model of Challenge and Threat ( Blascovich, 2008 ), that aim to capture the complex interrelations between cognitive processes and affective, physiological, and behavioral responses.

Furthermore, almost a quarter of the described studies, that either apply MI or CBI, reported positive effects on MA for both the intervention and the control/comparison group. This surprising result raises questions on potential third factors that led to a reduction of MA in these studies, and that have not yet been taken explicitly into account. These third factors could be school- and teaching-related variables that might be associated with the development of MA (e.g., teacher’s beliefs). At the same time, the differences between the control groups of the included studies hinder potential discussions of these third factor variables. Of course, methodological issues might explain the non-existing differences between control and intervention groups (e.g., non-randomized controls leading to an unbalanced study design, unknown background interventions). In addition, reductions in the level of MA in both groups might be explained by the applied MA measures. To make differentiated conclusions about impacts of intervention programs on math-related anxiety reactions and/or math anxious cognitive beliefs, it may be useful for future studies to carefully consider the conceptualizations of MA questionnaires. E.g., intervention programs focusing emotional-regulation strategies could benefit from real-time assessments, measuring math-related anxiety reactions (state-MA), whereas studies that incorporate CBI might be more likely to evaluate effects on cognitive beliefs and trait-dispositions. However, to account for all influences, it would be best to consider both situation- and disposition-related approaches.

When comparing the mode and settings of the MI and CBI, it becomes clear that the majority of CBI was based in a one-to-one or small group setting. A classroom-based application of CBI was rare. Hence, future research might try to apply CBI or to combine CBI and MI on a classroom level. Despite the fact that interventions addressing MA are of relevance for students with high levels of MA, all students might profit from adequate strategies targeting anxiety related cognitions.

To conclude, a few limitations of our systematic review need to be mentioned. Firstly, the review only included intervention studies that target MA. This approach might have excluded a range of studies and findings, that highlighted the relevance of potential variables that might also be associated with the development of MA but had not been part of an intervention study (e.g., environmental factors). Secondly, although we tried to capture all relevant information of the included studies as accurate and complete as possible, the transparency within the studies was lacking at times. This implies, that important information might be missing or incomplete for some of the included studies. Especially missing information on the format and duration of the interventions makes it difficult to compare the effectiveness of the different approaches. And thirdly, our review is not a meta-analysis. Insights in described effects are therefore on a descriptive level and do not allow a direct statistical comparison or aggregation of the described effects.

In the end, no clear picture can be drawn yet of how effective MA intervention for school children should look like. However, this literature review still offers valuable insights into the current state in the field of MA intervention research. Both approaches (MI and CBI) show potential positive effects. The findings of this review at hand might therefore serve as an orientation for future research and for the development of effective interventions that aim to reduce MA in children.

Data Availability Statement

The raw data supporting the conclusion of this article will be made available by the authors on request, without undue reservation.

Author Contributions

LO, MB and MB-R drafted the theoretical background. MB and MB-R were responsible for data analysis and discussion of the findings. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We thank Julia Gehlhaus, Lisa Marie Flebbe, and Kristin Busse for their support in screening and evaluating the studies for this systematic review and for piloting the data extraction spread sheet as part of their Bachelor theses. We acknowledge support from the Open Access Publication Fund of the University of Wuppertal.

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Keywords: math anxiety, intervention, review, school, children

Citation: Balt M, Börnert-Ringleb M and Orbach L (2022) Reducing Math Anxiety in School Children: A Systematic Review of Intervention Research. Front. Educ. 7:798516. doi: 10.3389/feduc.2022.798516

Received: 20 October 2021; Accepted: 06 January 2022; Published: 03 February 2022.

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Copyright © 2022 Balt, Börnert-Ringleb and Orbach. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Miriam Balt, [email protected]

This article is part of the Research Topic

Cognitive and Affective Factors in Relation to Learning

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A Cross-national Study of Mathematics Anxiety

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• Volume 32 , pages 295–306, ( 2023 )

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• Zhenguo Yuan 1 ,
• Jiang Tan 2 &
• Renmin Ye   ORCID: orcid.org/0000-0002-4513-1050 3  

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Using an international education database, PISA 2012, this study compares 15-year-old students’ mathematics anxiety and its relationships with educational issues among five Asia Pacific economies (Indonesia, Korea, Malaysia, Shanghai, & Singapore), using descriptive, figural, partial correlative, multiple regressive, and factor analysis methods. The main variable, math anxiety, is made up primarily from six of ten items around student worry, tense, nervousness, or helplessness for mathematics difficulties, homework, tough problems, or poor grades. New findings present a perspective of student math anxiety and its impacts across economies with three different models: math anxiety has strong negative correlations with student standardized test scores, interests, and knowledge of mathematics in all economies; it has reverse relations with student abilities, importance, self-attribution in the high and low achievement economies; and it has weak correlations with teaching methods, parents’ influences, or friends’ performances. This study also discusses a series of questions for further study in mathematics learning.

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Zhenguo Yuan

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Yuan, Z., Tan, J. & Ye, R. A Cross-national Study of Mathematics Anxiety. Asia-Pacific Edu Res 32 , 295–306 (2023). https://doi.org/10.1007/s40299-022-00652-7

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Mathematics Anxiety: A Case Study of Students’ Learning Experiences through Cognitive, Environment and Behaviour

Nur hafizah musa, siti mistima maat.

• Pages 932-956
• Received: 21 Jan, 2021
• Revised: 23 Feb, 2021
• Published Online: 17 Mar, 2021

http://dx.doi.org/10.6007/IJARBSS/v11-i3/8992

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Prior research indicated that mathematics anxiety (MA) is considered as significant educational problems since it affected students’ emotion, thought and action. Thus, this article aims to explore the issue of MA among secondary school students as viewed through students’ learning experiences focusing on three main aspects – cognitive, environment and behaviour. The study also reports on students’ mathematics learning anxiety and mathematics assessment anxiety particularly within educational context. A qualitative research approach using case study design was structured in this study. Participants involved 7 highly math-anxious students (aged 16) from a secondary school in Malaysia. The data was collected through semi-structured interview, transcribed and analyzed to establish themes and subthemes. Findings revealed that for anxiety in mathematics learning, stress or pressure and mentality were students main concern for MA whereas for anxiety in mathematics assessment, self-confidence and anxiousness were indicated. As for students’ learning experience five subthemes were developed from the findings – (1) self-conflict for cognitive aspect, (2) external influence and (3) content or nature of mathematical knowledge for environmental aspect, (4) the importance of mathematics and (5) strategies in mathematics learning for behavioural aspect. This study also found that MA was experienced by both math high- and low-achieving students. However, for high-achieving students, motivational factor was their main influence towards MA whereas for low-achieving students, poor math competency and self-skills were the major contributors. The article offers in-depth understanding for educators particularly in terms of diagnostic study for MA through student’s perspective and they can use this information to identify ways of reducing MA.

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In-Text Citation: (Musa & Maat, 2021) To Cite this Article: Musa, N. H., & Maat, S. M. (2021). Mathematics Anxiety: A Case Study of Students’ Learning Experiences through Cognitive, Environment and Behaviour. International Journal of Academic Research in Business and Social Sciences, 11(3), 932-956.

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The neuroscience basis and educational interventions of mathematical cognitive impairment and anxiety: a systematic literature review

Introduction.

Mathematics is a fundamental subject with significant implications in education and neuroscience. Understanding the cognitive processes underlying mathematical cognition is crucial for enhancing educational practices. However, mathematical cognitive impairment and anxiety significantly hinder learning and application in this field. This systematic literature review aims to investigate the neuroscience basis and effective educational interventions for these challenges.

The review involved a comprehensive screening of 62 research articles that meet the ESSA evidence levels from multiple databases. The selection criteria focused on studies employing various methodologies, including behavioral experiments and neuroimaging techniques, to explore the neuroscience underpinnings and educational interventions related to mathematical cognitive impairment and anxiety.

The review identified key themes and insights into the neuroscience basis of mathematical cognitive impairment and anxiety. It also examined their impact on educational practices, highlighting the interplay between cognitive processes and educational outcomes. The analysis of these studies revealed significant findings on how these impairments and anxieties manifest and can be addressed in educational settings.

The review critically analyzes the shortcomings of existing research, noting gaps and limitations in current understanding and methodologies. It emphasizes the need for more comprehensive and diverse studies to better understand these phenomena. The discussion also suggests new directions and potential improvement strategies for future research, aiming to contribute to more effective educational interventions and enhanced learning experiences in mathematics.

This systematic review provides valuable insights into the neuroscience basis of mathematical cognitive impairment and anxiety, offering a foundation for developing more effective educational strategies. It underscores the importance of continued research in this area to improve educational outcomes and support learners facing these challenges.

1. Introduction

Mathematics, as a universal and foundational subject, has extensive applications in various fields such as society, technology, and economics. Particularly in education and neuroscience, the study of mathematical cognition has gradually become an important research object ( Sotiropoulos, 2014 ). This involves complex issues of how humans acquire, process, and apply mathematical knowledge ( Moustafa et al., 2017 ). In education, exploring the processes and mechanisms of mathematical cognition helps optimize teaching methods, improve students’ learning processes, and enhance overall teaching effectiveness ( Gilmore, 2023 ; Medrano and Prather, 2023 ). Simultaneously, in neuroscience, studying the neural basis of mathematical cognition allows us to understand the operating mechanisms of the brain more deeply ( Matejko and Ansari, 2015 ; Looi et al., 2016 ). However, in the research and application of mathematical cognition, mathematical cognitive impairment and mathematical anxiety are two universal and severe problems ( Moustafa et al., 2019 ). Mathematical cognitive impairment often leads to persistent difficulties in mathematical learning and application, severely affecting academic achievements and potentially negatively impacting future career development and social adaptability. Mathematical anxiety typically manifests as tension and anxiety when individuals face mathematical tasks, likely exacerbating the problems of mathematical cognitive impairment ( Henschel and Roick, 2020 ). To explore the causes, manifestations, and intervention methods of these two problems, researchers have conducted extensive research using various methods such as behavioral experiments and neuroimaging, achieving some research results. However, there are still many unresolved issues and deficiencies regarding the neuroscience basis of mathematical cognitive impairment and anxiety and how to effectively alleviate these two problems through educational interventions.

This paper aims to explore and analyze the neuroscience basis of mathematical cognitive impairment and anxiety and how these bases affect the methods and effects of educational practices by reviewing related literature. It will focus on exploring the neural level performance characteristics of mathematical cognitive impairment and anxiety, their mutual influences, and effective ways to alleviate these problems through educational interventions. It will also summarize and analyze the shortcomings of existing research, aiming to find new directions and possible improvement strategies for future research. We hope this paper can provide valuable references and inspirations for the theory and practice of mathematical education.

The objective of this systematic literature review is to construct an understanding of mathematical anxiety and cognitive impairment from the existing research foundation, aiming to identify significant themes within the current knowledge base. Our review process included formulating research questions; determining the scope of search terms; selecting databases; establishing reasonable inclusion and exclusion criteria; evaluating evidence levels; expanding database search results; identifying meaningful outcomes, patterns, and trends; and conveying contributions(see Figure 1 ).

Systematic literature review process.

For this study, we utilized several search engines: Web of Science, EBSCO, Scopus, and ERIC. These engines were chosen based on their breadth and tendency to be more comprehensive than other databases. The initial primary search terms used were “mathematics,” “math education,” “mathematical,” and “math cognition,” each paired with “neuroscience,” “anxiety,” and “cognitive impairment.” We did not use a specific year range for the search, opting instead for an open search to observe a more comprehensive representation. The preliminary search yielded 2,328 articles. From the results, 419 duplicate articles were removed. Our systematic literature review included a snowball sampling strategy, identifying additional studies by reviewing citations from articles in our preliminary search, incorporating an additional 29 articles, totaling 1938. The screening process involved two rounds of review, applying our search inclusion and exclusion criteria to narrow down the pool of articles suitable for inclusion. The first round of screening primarily focused on the titles and abstracts of the articles, excluding 1,526 articles, leaving 369. Table 1 provides examples of the excluded articles.

Sample of excluded articles.

The second round of screening involved using the ESSA evidence levels to assess the eligibility of full-text articles, determining which articles should be included in the review based on the rigor of the research (see Figure 2 ) ( Thomas and Harden, 2008 ). 307 articles were excluded as they did not meet the ESSA evidence levels. The final 62 articles included: 7 meeting ESSA Tier 1, 9 meeting ESSA Tier 2, 15 meeting ESSA Tier 3, and 31 meeting ESSA Tier 4.

ESSA Evidence Hierarchy.

We employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, an evidence-based standard for systematic reviews and meta-analyses, to guide the selection and analysis of literature methods. Figure 3 displays a PRISMA flow diagram summarizing the process used to identify studies.An information database was constructed using the articles included in the systematic literature review. The database comprised general information (e.g., article titles, authors, publication dates, publications, and abstracts), research types (e.g., research questions, methods), and summaries of findings. The authors used the PRISMA guidelines to elucidate the data collection and analysis methods for each study.

Preferred reporting items for PRISMA diagram of selection process.

We did not use a conceptual framework to construct the coding process but employed thematic synthesis to analyze the articles, where themes were generated from the primary studies. The three stages of this method were: (1) coding selected texts, (2) developing descriptive themes, and (3) generating analytical themes. For each article, findings, discussions, and implications were independently coded by the authors of this manuscript and then discussed collectively. Emergent codes were identified, descriptive themes developed, and analytical themes generated. After a rigorous screening process and analysis, we selected a series of primary literatures on mathematical anxiety and cognitive impairment. These literatures mainly fall into several categories (see Table 2 ).

Main classification of literature.

Through the comprehensive analysis and summary of these literatures, we can gain a deeper understanding of the neuroscience basis of mathematical anxiety and cognitive impairment and how to apply this knowledge in educational practices. This not only helps us understand the nature of mathematical anxiety and cognitive impairment more comprehensively but also assists us in finding more effective assessment and intervention methods.

This review aims to explore the neural foundations of mathematical cognition, mathematical cognitive impairment, and math anxiety, as well as the implications of neuroscience research findings for math education.

3.1. Neural foundations of mathematical cognition

Mathematical cognition, as a complex psychological process, involves activities in multiple brain regions. While studies have indicated associations between different components of mathematical cognition and specific brain regions, it’s crucial to note that these relationships are not always straightforward or one-to-one. For instance, while number representation and processing, arithmetic rules and calculations, and problem-solving have been linked to distinct cognitive mechanisms and brain activities ( Newman et al., 2011 ; Bulthé et al., 2014 ; Park et al., 2014 ), Cappelletti et al. (2012) found that most patients with focal brain lesions exhibited calculation deficits but did not necessarily show deficits in nonsymbolic quantity recognition. This was observed both in patients with parietal lesions and those with lesions in other brain areas, suggesting a more intricate relationship between brain regions and numerical cognition than previously assumed.

3.2. Mathematical cognitive impairment

Mathematical cognitive impairment is a complex phenomenon, covering two main areas: developmental calculation disorders and acquired calculation disorders. Developmental calculation disorders typically manifest during the natural developmental process of children, appearing as difficulties in number and calculation abilities without evident neural system damage or specific brain region developmental delays ( Lu et al., 2020 ). These difficulties extend beyond mathematical calculations, including number recognition, comparison, and estimation, possibly due to fundamental neurobiological defects such as working memory, attention, and executive function deficits ( Li et al., 2013 ; Bulthé et al., 2019 ; Klados et al., 2019 ). Acquired calculation disorders, on the other hand, result from external factors like brain injuries or diseases, leading to the loss or decline of previously acquired mathematical abilities ( Siemann and Petermann, 2018 ), typically emerging in adulthood and potentially affecting individuals’ daily lives and career development.

3.3. Math anxiety

Math anxiety refers to the tension and unease experienced by individuals when facing mathematical tasks. This anxiety can negatively impact the completion of mathematical tasks, such as reducing task accuracy and efficiency ( Foley et al., 2017 ). Math anxiety may be related to individuals’ self-evaluation, math self-efficacy, and past math experiences. Research has found that math anxiety is associated with activities in specific brain regions, such as the amygdala ( Atabek et al., 2022 ). Individuals with high math anxiety show excessive activity in the amygdala region of the brain when facing mathematical tasks, indicating that math anxiety is not only a psychological phenomenon but also related to brain physiological activities. Math anxiety affects mathematical cognition, including number representation, calculation, and problem-solving ( Suárez-Pellicioni et al., 2013 ; Peña and Suárez-Pellicioni, 2014 ; Klados et al., 2015 ). For instance, math anxiety might interfere with individuals’ working memory, making them more likely to feel confused and lose focus when solving mathematical problems. Additionally, math anxiety may also affect individuals’ math learning and long-term math achievements ( Peña et al., 2019 ).

3.4. Interaction between math anxiety and cognitive impairment

There is a close interaction between math anxiety and cognitive impairment. Math anxiety might exacerbate the manifestations of cognitive impairment and vice versa ( Devine et al., 2018 ). For example, math anxiety might affect individuals’ attention allocation and information processing strategies, thereby affecting their mathematical cognition performance ( Pizzie and Kraemer, 2017 ). At the same time, individuals with cognitive impairments might develop higher math anxiety due to their mathematical ability deficiencies ( Young et al., 2012 ).

3.5. Implications of neuroscience research findings for math education

The findings from neuroscience provide new perspectives and theoretical support for math education. For instance, educators can design teaching strategies more aligned with students’ cognitive development based on the neural foundations of mathematical cognition ( Tschentscher and Hauk, 2014 ). Moreover, for mathematical cognitive impairment and math anxiety, educators can formulate more scientific and effective teaching and intervention strategies based on neuroscience research findings ( Faramarzi and Sadri, 2014 ).

In conclusion, math anxiety and cognitive impairment play significant roles in mathematical cognition. Understanding their neural foundations and mechanisms of mutual influence is crucial for optimizing math education strategies and improving teaching outcomes. In the subsequent discussion section, we will explore issues related to mathematical cognitive impairment and math anxiety more deeply, aiming to provide more comprehensive and profound theoretical support for the improvement and development of math education.

4. Discussion

4.1. neural mechanisms of math anxiety.

Math anxiety, as a significant research subject in psychology and education fields, has its neurobiological foundations becoming a current research hotspot. Through neuroimaging technologies such as functional magnetic resonance imaging (fMRI), researchers aim to unveil the neural mechanisms and network connections behind math anxiety ( Newman et al., 2011 ). Firstly, from an emotional processing perspective, studies have found that individuals with math anxiety, when facing mathematical tasks, show more active neural activities in brain regions related to emotional processing, such as the amygdala ( Atabek et al., 2022 ). The amygdala, as a core area for emotional responses and processing, indicates that math anxiety is closely related to the brain’s emotional regulation mechanisms ( Atabek et al., 2022 ). This connection might be a crucial neural foundation for the emergence and maintenance of math anxiety. Secondly, math anxiety is also related to the brain’s cognitive control areas. Research indicates that in individuals with math anxiety, brain regions related to executive functions and attention regulation, such as the prefrontal and parietal lobes, might display activity patterns different from those of normal individuals when processing mathematical tasks ( Chen et al., 2006 ). These differences might lead to issues in cognitive resource allocation and utilization in individuals with math anxiety, thereby affecting task completion efficiency and accuracy. Additionally, math anxiety might also be related to the brain’s default mode network (DMN). Some studies have found that the DMN’s activity might be affected in individuals with math anxiety during mathematical tasks ( Pletzer et al., 2015 ). The DMN is usually active when individuals are at rest and decreases in activity during specific cognitive tasks ( Gotlieb et al., 2016 ). Math anxiety might affect this network’s normal functions, thereby affecting individuals’ cognitive performance. In conclusion, the neural mechanisms of math anxiety might involve multiple brain regions and networks, including those related to emotional processing, cognitive control, and the default mode network ( Young et al., 2012 ; Pletzer et al., 2015 ; Meijer et al., 2022 ). This multi-region, multi-network involvement suggests that math anxiety is a complex psychological phenomenon, influenced by various neural factors. A deeper understanding of the neural mechanisms of math anxiety can not only help us comprehend this phenomenon more comprehensively and accurately but also provide more scientific and effective theoretical guidance for clinical interventions and educational practices. For instance, targeted regulation and training of related brain regions and networks might help alleviate the degree of math anxiety, improving individuals’ math learning and performance ( Liu et al., 2019 ). This will contribute to the advancement of math education, enhancing the quality and effectiveness of education.

4.2. Relationship between mathematical anxiety and components of mathematical cognition

Math anxiety is a specialized, negative emotional reaction directed towards math learning and evaluation. This emotional reaction is not merely limited to superficial emotional experiences; more profoundly, it is intricately linked to the cognitive components of math—such as number representation, calculation, and problem-solving ( Suárez-Pellicioni et al., 2013 ; Peña and Suárez-Pellicioni, 2014 ; Klados et al., 2015 ). Through in-depth analysis and exploration, we can more comprehensively understand how math anxiety permeates and influences an individual’s mathematical cognitive processes.

Firstly, we explore the relationship between math anxiety and number representation. Number representation is foundational to mathematical cognition, involving the cognitive representation of numbers and quantities ( Bulthé et al., 2014 ). Individuals with high math anxiety may encounter difficulties in number representation ( Piazza and Eger, 2016 ). For instance, when engaging in tasks such as number magnitude comparison, number ordering, and number line positioning, they may not perform as well as those with low math anxiety. This could be because the excessive attention and worry triggered by math anxiety interfere with the normal processing and encoding of numerical information, affecting the process of number representation ( Kanayet et al., 2018 ).

Secondly, we examine how math anxiety impacts calculation abilities. Calculation abilities are a core component of mathematical cognition, encompassing basic arithmetic calculations and complex mathematical operations ( Yi-Rong et al., 2011 ; Van Der Ven et al., 2016 ). Research has found that individuals with high math anxiety may make more errors and take longer reaction times when performing arithmetic calculations ( Kanayet et al., 2018 ). This might be due to math anxiety consuming substantial cognitive resources, such as attention and working memory, preventing individuals from fully concentrating when processing calculation tasks, thereby affecting the accuracy and efficiency of calculations.

Lastly, we investigate the relationship between math anxiety and problem-solving abilities. Problem-solving is an advanced process in mathematical cognition, involving metacognitive skills such as strategy selection, planning, and self-monitoring. Individuals with high math anxiety, when facing mathematical problems, may feel more anxious and uneasy, which could affect their strategy selection and problem-solving processes. For example, they might exhibit more hesitation and uncertainty during problem-solving, and the strategies they choose may not be as effective and reasonable ( Peters et al., 2016 ).

By deeply exploring the relationship between math anxiety and components of mathematical cognition, we can see how math anxiety influences mathematical cognition through various pathways and mechanisms. Math anxiety might indirectly affect the execution of mathematical tasks by occupying limited cognitive resources and disrupting the normal functioning of attention and working memory ( Evans et al., 2016 ). Simultaneously, math anxiety might also directly interfere with the problem-solving process by affecting individuals’ metacognitive skills, such as self-monitoring and strategy selection ( Peters et al., 2016 ).

In conclusion, there exists a complex and multi-layered interaction and influence between math anxiety and the components of mathematical cognition. To more effectively understand and alleviate the negative impacts of math anxiety, future research could further delve into the specific mechanisms and pathways of these interactions, providing more scientific and targeted guidance and recommendations for educational practice.

4.3. Manifestations and characteristics of cognitive impairments

In discussing cognitive impairments in mathematical cognition, literature primarily focuses on Developmental Calculation Disorder (DCD) and Acquired Calculation Disorder (ACD) ( Kucian et al., 2013 ; Van Beek et al., 2015 ). Both types of impairments are mainly characterized by deficiencies in numerical and calculation abilities, but their manifestations and etiologies differ.

4.3.1. Developmental calculation disorder (DCD)

DCD, as a specific learning impairment, predominantly manifests during the mathematical learning process in children. This impairment is usually persistent, affecting individuals’ performance in various areas such as numerical processing, basic arithmetic skills, and mathematical problem-solving ( Kaufmann et al., 2011 ; Kucian et al., 2011 ; Kucian and Von Aster, 2014 ). Notably, studies like Demeyere et al. (2010) and Demeyere et al. (2012) have highlighted dissociations between components like subitizing and counting in patients with dyscalculia. Furthermore, the work of Brian Butterworth has extensively delved into the intricacies of mathematical cognition and its disorders. While DCD is often associated with unusual patterns of brain functioning, particularly involving the parietal lobes, it is rarely the result of brain damage and is unlikely to have an exact parallel with acquired dyscalculia (ACD). There is still controversy about the exact nature and causes of DCD ( Murphy et al., 2007 ; Kaufmann et al., 2013 ; Träff et al., 2017 ). A detailed exploration and analysis of DCD manifestations, based on literature, will be conducted (see Table 3 ) ( Van Harskamp and Cipolotti, 2001 ; Kaufmann et al., 2011 ; Kucian et al., 2011 ; Faramarzi and Sadri, 2014 ; Kucian and Von Aster, 2014 ).

Manifestations of developmental dyscalculia.

Through an in-depth analysis of DCD children in terms of numerical processing, basic arithmetic skills, and mathematical problem-solving abilities, we can more comprehensively and accurately understand the mathematical learning characteristics and difficulties of this group. This is crucial for educators and mental health professionals to provide more precise and personalized support during diagnosis and intervention. Moreover, it helps further explore and understand the causes and mechanisms of mathematical learning impairments, providing a richer and more profound theoretical foundation for future research and practice.

4.3.2. Acquired calculation disorder (ACD)

ACD, as a unique form of mathematical cognitive impairment, is typically due to brain injury or neurological disorders ( Siemann and Petermann, 2018 ). Unlike DCD, individuals with ACD might have successfully acquired certain mathematical skills earlier, but due to subsequent factors such as brain injury or disease, these skills may be lost or deteriorated ( Miundy et al., 2019 ). The main manifestations and characteristics of ACD, such as loss of mathematical skills, calculation difficulties, and mathematical thinking impairments, will be deeply explored.

Loss of Mathematical Skills:Individuals with ACD may lose some basic mathematical skills that they had previously mastered, including basic arithmetic abilities, understanding, and application of mathematical concepts ( González et al., 2019 ). They might find simple arithmetic operations challenging or feel confused when understanding and applying basic mathematical concepts and formulas. This loss may affect their mathematical application abilities in daily life and learning, such as calculating shopping expenses and measuring object lengths and areas ( Benavides-Varela et al., 2017 ).

Calculation Difficulties:Due to brain injuries, individuals with ACD may face significant challenges in mathematical calculations ( Cohen et al., 2018 ). This includes not only complex mathematical calculations, such as solving algebraic and geometric problems, but also simpler tasks like basic arithmetic operations ( Kaufmann, 2008 ; Kunwar, 2021 ). They might make mistakes easily during calculations or take longer to complete tasks that should be simple and quick.

Mathematical Thinking Impairments:Individuals with ACD may also have certain limitations in mathematical thinking and reasoning ( Siemann and Petermann, 2018 ). They might display rigidity, lack of creativity, and flexibility when facing mathematical problems ( Claros-Salinas et al., 2014 ; Hobri et al., 2021 ). They might find it difficult to understand and master new mathematical concepts and methods or lack effective problem-solving strategies and methods. Additionally, they might struggle with abstract thinking and logical reasoning, such as understanding and applying abstract mathematical concepts and theorems.

In conclusion, ACD, as a special form of mathematical cognitive impairment, is mainly characterized by the loss of mathematical skills, calculation difficulties, and mathematical thinking impairments. These impairments may severely affect the mathematical application abilities and performance of individuals with ACD in learning, work, and daily life ( Kaufmann et al., 2013 ). Therefore, more attention and support are needed for individuals with ACD, helping them overcome obstacles and improve mathematical cognition and application abilities through effective educational and rehabilitative interventions.

By analyzing developmental and acquired calculation disorders, it is evident that although both are related to deficiencies in mathematical cognitive abilities, their causes, manifestations, and impacts are distinct. Understanding and distinguishing the characteristics of these impairments are essential for better understanding the potential issues in mathematical cognitive processes and providing more precise and effective assistance to individuals facing difficulties.

4.4. Neural basis of cognitive impairments

In the profound exploration of cognitive impairments, scholars have utilized sophisticated neuroimaging technologies such as Diffusion Tensor Imaging (DTI), conducting a series of exhaustive investigations into the neural foundations of cognitive impairments ( Kucian et al., 2013 ). These explorations aim to unveil the neural structural and functional abnormalities underlying the difficulties encountered by individuals with cognitive impairments during mathematical tasks ( Murphy et al., 2007 ).

Initially, research has revealed that individuals with cognitive impairments may exhibit structural and functional anomalies in critical brain regions such as the parietal and frontal lobes ( Kaufmann et al., 2011 ; Bulthé et al., 2019 ). These lobes, essential components of the brain, play central roles in cognitive processes such as spatial representation, attention, memory, and executive functions ( Kaufmann et al., 2011 ). In the parietal lobe, abnormalities may be related to difficulties in spatial representation and visual–spatial processing, crucial for understanding and solving geometric and spatially related mathematical problems ( Bulthé et al., 2019 ). In the frontal lobe, anomalies might predominantly affect executive functions, including planning, organization, and self-monitoring, essential elements in mathematical problem-solving ( Kaufmann et al., 2011 ).

Furthermore, utilizing neuroimaging technologies like DTI, researchers have discovered potential abnormalities in the white matter pathways of individuals with cognitive impairments ( Jolles et al., 2015 ). White matter pathways, the “highways” for neural information transmission between various brain regions, are vital for ensuring coordination and integration among different functional networks of the brain ( Kucian et al., 2013 ). In individuals with cognitive impairments, abnormalities in these pathways might lead to reduced efficiency in information transmission, thereby affecting the processing and integration of mathematical information. Additionally, these neural foundation abnormalities might be directly linked to the specific manifestations of individuals with cognitive impairments in mathematical tasks ( Davidse et al., 2014 ). For instance, anomalies in the parietal and frontal lobes might make it challenging for individuals to effectively organize and utilize relevant strategies and knowledge during mathematical calculations and problem-solving. Abnormalities in the white matter pathways might affect the mobilization and utilization of various cognitive resources when handling complex mathematical tasks ( Grant et al., 2020 ).

These research findings offer invaluable perspectives, aiding in a more comprehensive and profound understanding of the neural mechanisms of cognitive impairments in mathematical learning. This not only facilitates the enhancement of precision in the diagnosis and assessment of cognitive impairments but also provides robust theoretical support for devising effective educational intervention measures and strategies. A deeper understanding of the neural basis of cognitive impairments allows for the development of more targeted teaching methods and strategies that align with the characteristics of individuals with cognitive impairments, aiming to better support their development and progress in mathematical learning.

4.5. Research on intervention strategies

In exploring intervention strategies for mathematical anxiety and cognitive impairments, researchers have employed various methods, aiming to find effective ways to alleviate individuals’ mathematical anxiety and improve their mathematical cognitive abilities.

4.5.1. Cognitive training interventions

Recently, cognitive training has emerged as a crucial intervention strategy, extensively researched and applied to alleviate mathematical anxiety and improve cognitive impairments. To enhance individuals’ cognitive abilities and information processing efficiency in mathematical learning, researchers have meticulously designed a series of systematic cognitive tasks. Among them, working memory training is a core component of cognitive training, involving the cognitive system where people temporarily store and manipulate information, directly affecting mathematical learning outcomes ( De Vreeze-Westgeest and Vogelaar, 2022 ). Researchers, through designing tasks of various forms and difficulties such as n-back tasks and complex span tasks, intentionally enhance individuals’ working memory capabilities ( Schmidt et al., 2009 ; Lucidi et al., 2014 ). With continuous and regular training, individuals can process and manipulate information more effectively in mathematical tasks, reducing the cognitive load caused by mathematical anxiety.

Cognitive training also includes attention control training, helping individuals selectively focus on and process task-related information while ignoring irrelevant distractions ( Bishara and Kaplan, 2021 ). Through specialized training such as the Flanker task and Stroop task, individuals learn to concentrate better, reducing attention dispersion and cognitive resource consumption caused by anxiety ( Van Der Ven et al., 2011 ; Van Nes, 2011 ; Peralbo et al., 2020 ). Additionally, cognitive training emphasizes enhancing other cognitive functions such as executive functions and spatial abilities ( Wu et al., 2019 ; Chatzivasileiou and Drigas, 2022 ). These trainings assist individuals in more flexibly and accurately applying various strategies and methods during the mathematical learning process, improving problem-solving abilities. During the implementation of cognitive training, researchers particularly emphasize the individualization and adaptability of training, adjusting the difficulty and content of training tasks timely based on each individual’s baseline abilities and progress, ensuring the effectiveness and efficiency of the training ( Chipman, 2010 ).

In conclusion, cognitive training, as an intervention strategy based on cognitive psychology principles, shows immense potential in alleviating mathematical anxiety and improving cognitive impairments by specifically training cognitive functions such as working memory and attention control. Looking forward, research can further explore the optimal implementation methods and effectiveness evaluation approaches of cognitive training, providing more scientific and effective guidance and strategies for cognitive training-based interventions.

4.5.2. Application of psychological interventions

The significance of psychological interventions in alleviating mathematical anxiety has been widely affirmed by extensive research. As an effective strategy to mitigate mathematical anxiety, psychological interventions not only assist individuals in altering their thought processes and emotional responses, thereby reducing the levels of mathematical anxiety, but also enhance individuals’ confidence and efficiency in mathematical learning. Cognitive-behavioral therapy, psychoeducation, and relaxation training are currently the three mainstream psychological intervention methods ( Henschel and Roick, 2020 ; Moustafa et al., 2021 ; Ng et al., 2022 ).

Research indicates that cognitive-behavioral therapy (CBT) is a promising strategy. This approach focuses on helping individuals identify and challenge their negative and irrational thinking patterns, encouraging students to assess mathematical tasks and challenges more objectively, thus alleviating anxiety caused by excessive worry and fear ( Moustafa et al., 2021 ). In the long term, this method can enable students to maintain calmness and rationality when facing mathematical challenges, thereby improving mathematical abilities ( Ramirez et al., 2018 ). Psychoeducation is also an essential intervention measure. It aims to enhance students’ understanding of mathematical anxiety, allowing them to better comprehend the causes, characteristics, and impacts of anxiety ( Casad et al., 2015 ). Studies have shown that psychoeducation can help students develop a positive and healthy learning attitude, thus reducing the fear of mathematics ( Cheng et al., 2022 ). Through psychoeducation, students can address mathematical anxiety specifically, establishing a positive emotional connection with mathematics. Relaxation training, on the other hand, starts from a physical perspective, assisting individuals in alleviating the physical and psychological stress generated by mathematics. For instance, techniques such as deep breathing and progressive muscle relaxation have been found to effectively help students maintain psychological balance and reduce tension and anxiety during mathematical learning ( Ng et al., 2022 ).

In conclusion, by integrating the above three strategies, we can assist students in reducing the levels of mathematical anxiety from multiple dimensions. Continuous and systematic psychological interventions are key, enabling individuals to gradually overcome mathematical anxiety and engage in mathematical learning and practice with more confidence and efficiency.

4.5.3. Personalized intervention strategies

In exploring intervention methods for mathematical anxiety and cognitive impairments, personalized intervention strategies have emerged as a focal point receiving significant attention. This strategy emphasizes developing and implementing intervention plans based on each learner’s unique characteristics and needs, aiming to provide more precise and targeted assistance.

Firstly, this strategy is based on a clear premise: each learner exhibits variations in cognitive abilities and mathematical anxiety ( Li et al., 2021 ). Research suggests that to effectively address specific problems encountered by learners in mathematical learning, intervention plans must consider the type and degree of learners’ cognitive impairments and the manifestation and severity of mathematical anxiety ( Luneta and Sunzuma, 2022 ). Therefore, intervention plans should be meticulously tailored to learners’ specific situations. Secondly, personalized intervention strategies are not merely preliminary planning but represent a dynamic, continuously adjusting process. Based on learners’ feedback and progress during the intervention, strategies and methods will be timely adjusted to ensure they consistently meet learners’ actual needs ( Johnson et al., 2020 ). The flexibility of this strategy not only ensures that interventions always align with learners’ needs but also enhances learners’ participation and acceptance. Additionally, intervening solely from mathematical anxiety and cognitive perspectives is insufficient. Personalized intervention strategies emphasize a comprehensive focus on learners, providing help not only from a cognitive perspective but also considering learners’ psychological, social, and emotional factors, offering holistic support ( Reyes, 2019 ; Shafiq et al., 2021 ). For example, enhancing learners’ self-efficacy and learning motivation, optimizing the learning environment, and strengthening social support are all considerations within this strategy.

In general, personalized intervention strategies emphasize respect for individual differences and support for holistic development, offering effective assistance to learners encountering anxiety and cognitive impairments in mathematical learning. Through the implementation of this strategy, we hope to assist learners in achieving greater progress and development in mathematical learning.

4.5.4. Integrated intervention methods

Integrated intervention methods have increasingly attracted attention as an innovative strategy in the research field of mathematical anxiety and cognitive impairments. Research indicates that this method, by combining cognitive training with psychological interventions, forms a diversified intervention framework aimed at comprehensively improving individuals’ mathematical anxiety and cognitive impairments ( Soares et al., 2018 ). Cognitive training focuses on enhancing the efficiency and accuracy of information processing, including training in working memory, executive functions, and attention control, which helps improve performance in mathematical tasks and reduce the cognitive load brought about by mathematical anxiety ( Schmidt et al., 2009 ; Wu et al., 2019 ; Chatzivasileiou and Drigas, 2022 ). Psychological interventions focus on emotional management and regulation, helping individuals identify, understand, and regulate negative emotions and thoughts related to mathematical anxiety through methods such as cognitive-behavioral therapy and relaxation training ( Henschel and Roick, 2020 ; Moustafa et al., 2021 ; Ng et al., 2022 ). The advantage of integrated intervention methods lies in their comprehensiveness, enabling interventions from both cognitive and emotional dimensions. This strategy helps address the issues of mathematical anxiety and cognitive impairments more effectively. Through integrated interventions, individuals can not only enhance their cognitive abilities but also achieve better management and regulation at the emotional level. Moreover, this method emphasizes individualization and flexibility, allowing for the adjustment and combination of different intervention strategies based on individuals’ specific needs, aiming to achieve optimal intervention outcomes. In conclusion, integrated intervention methods provide a new, diversified strategy and approach for the intervention of mathematical anxiety and cognitive impairments. Future research is expected to further explore and optimize this intervention method, hoping to achieve better intervention outcomes in practical applications.

4.5.5. Evaluation of intervention effects

In psychological and educational intervention research, there are various essential methods to evaluate intervention effects. Research indicates that a direct comparison of performance before and after intervention is one of the fundamental methods to evaluate intervention effects, helping to measure changes in relevant indicators such as the level of mathematical anxiety and cognitive abilities ( Casad et al., 2015 ). Meanwhile, comparison with a control group is considered a more precise evaluation method. Through this comparison, the effect of the intervention can be judged more accurately, eliminating other possible interfering factors. Additionally, the importance of long-term tracking should not be overlooked ( Bishara and Kaplan, 2021 ). Long-term tracking can help researchers explore the sustainability and stability of intervention effects, providing a more accurate evaluation of the long-term effects of intervention strategies ( Ng et al., 2022 ). Research should also consider the multidimensionality of evaluation. Multidimensional evaluations, including assessments of individuals’ self-efficacy, motivation, and emotional regulation abilities, can provide a more comprehensive and profound understanding of the effects of intervention strategies ( Chipman, 2010 ). By integrating various evaluation methods, a more comprehensive and accurate conclusion can be drawn. In summary, evaluating intervention effects is a complex and multi-level process. Through meticulous and comprehensive evaluation, we can better understand and validate the effectiveness of intervention strategies, providing robust support and valuable references for future research and practice.

In conclusion, intervention strategies for mathematical anxiety and cognitive impairments are diversified and comprehensive. Through continuous research and exploration, we can continually refine and optimize intervention methods, providing more effective support for alleviating mathematical anxiety and improving cognitive impairments.

4.6. Comparison of research methods and results

In the in-depth exploration of the interactive relationship between mathematical anxiety and cognitive impairments, we must confront a reality: significant disparities exist in the research methods and results across various studies. These differences offer us a valuable opportunity to understand this complex phenomenon from multiple angles and dimensions. Below is a detailed comparison and analysis of the research methods and results from various studies.

4.6.1. Diversity of research focus

In exploring the intertwined relationship between mathematical anxiety and cognitive impairments, research exhibits a rich diversity of focuses. Firstly, some studies predominantly concentrate on the neural mechanisms of mathematical anxiety and cognitive impairments, often employing advanced neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) ( Newman et al., 2011 ; Jolles et al., 2015 ). These studies reveal how mathematical anxiety affects individuals’ cognitive processing at the neural level, providing neurobiological evidence for a deeper understanding of the nature of mathematical anxiety and its impact on individuals’ mathematical learning abilities. For instance, studies have found that individuals with mathematical anxiety exhibit increased activity in brain regions associated with emotional regulation and decreased activity in regions related to mathematical information processing when handling mathematical tasks ( Young et al., 2012 ). Exploring these neural mechanisms contributes to a deeper understanding of the intrinsic connections between mathematical anxiety and cognitive impairments. On the other hand, some studies focus more on the design and evaluation of intervention strategies, seeking and validating methods to alleviate mathematical anxiety and improve cognitive impairments ( Schmidt et al., 2009 ; Wu et al., 2019 ; Li et al., 2021 ; Chatzivasileiou and Drigas, 2022 ; Ng et al., 2022 ). For example, by implementing cognitive-behavioral therapy or psychoeducational interventions, these studies aim to reduce students’ levels of mathematical anxiety and help improve their mathematical learning outcomes ( Bishara and Kaplan, 2021 ; Moustafa et al., 2021 ). Research focused on interventions holds practical value and guidance, offering us methods to practically address and improve mathematical anxiety and cognitive impairments.

In consideration of the above, the diversity of research focuses enriches our understanding of the relationship between mathematical anxiety and cognitive impairments. It provides approaches and methods to address and study this issue from different angles and dimensions, contributing to a more comprehensive and profound exploration of the complex relationship between mathematical anxiety and cognitive impairments.

4.6.2. Variability in sample selection

In discussing the relationship between mathematical anxiety and cognitive impairments, the strategies and methods of sample selection hold decisive importance. Through a comprehensive analysis of multiple studies, we find significant variability in sample selection, directly affecting the depth, breadth, and representativeness of the research conclusions.

Firstly, some studies show that researchers have a clear focus on sample selection, specifically choosing participants from certain age groups or backgrounds, such as elementary and high school students, or university students. This targeted selection provides researchers with a more focused perspective, enabling them to delve deeply into the manifestations of mathematical anxiety in specific groups ( Zhou et al., 2007 ; Prado et al., 2014 ; Jolles et al., 2015 ). For example, some studies have found that middle school students have higher levels of mathematical anxiety compared to university students, possibly related to their academic pressures and external expectations ( Klados et al., 2015 ). This research method helps accurately reveal the characteristics of mathematical anxiety at different educational stages. However, other studies have adopted a broader sample selection, encompassing various ages, genders, cultures, and educational backgrounds, thus making the research results more universal and representative ( Yi-Rong et al., 2011 ; Jolles et al., 2015 ). Such a method can help us understand the manifestations of mathematical anxiety in various populations more comprehensively, thereby revealing its general connections with cognitive impairments. More importantly, this broad sample selection provides an opportunity to explore the potential influences of background factors such as culture and education on mathematical anxiety, offering robust guidance for the formulation of effective intervention measures in the future.

In conclusion, the variability in sample selection not only affects the focus and depth of the research but also determines the application and interpretation of the research results. Therefore, future research should pay more attention to sample selection strategies to reveal the relationship between mathematical anxiety and cognitive impairments more precisely, providing more practical references for educational and psychological interventions.

4.6.3. Diversity of data analysis methods

In the relevant research exploring the interactive relationship between mathematical anxiety and cognitive impairments, the diversity of data analysis methods has emerged as a significant characteristic. Many scholars have adopted quantitative analysis methods, utilizing rigorous statistical tests such as t-tests, analysis of variance, and regression analysis to validate research hypotheses, and quantitatively assess the relationship between mathematical anxiety and cognitive impairments based on large sample sizes ( Rosenberg-Lee et al., 2011 ; Soltész et al., 2011 ; Klados et al., 2017 ; Choi-Koh and Ryoo, 2019 ). These studies present results in the form of data and charts, providing intuitive evidence for unveiling the objective relationship between mathematical anxiety and cognitive impairments. Simultaneously, some studies have chosen qualitative analysis methods, collecting data through open-ended questionnaires, in-depth interviews, and focus group discussions, to explore and describe individuals’ intrinsic experiences and feelings when facing mathematical learning more profoundly ( Young et al., 2012 ; Batashvili et al., 2019 ; Peña et al., 2019 ). This approach focuses on revealing students’ genuine reactions and feelings in mathematical learning, providing rich background information for understanding how mathematical anxiety leads to cognitive impairments. Some studies have adopted mixed methods, integrating both quantitative and qualitative analyses, aiming to reveal the interactive relationship between mathematical anxiety and cognitive impairments more comprehensively ( Rosenberg-Lee et al., 2011 ; Young et al., 2012 ; Peña et al., 2019 ). This strategy helps obtain a rich diversity of data, allowing for a more comprehensive and profound understanding and interpretation of how mathematical anxiety affects individuals’ cognitive processes.

Considering the diversity of these research methods reflects that scholars are exploring the issue of mathematical anxiety and cognitive impairments from various angles and dimensions. This methodological diversity enriches the research content and helps us understand the complex relationship between mathematical anxiety and cognitive impairments more comprehensively and profoundly, allowing for a better grasp of its essence and patterns.

4.6.4. Interpretation and understanding of research results

Through a comprehensive analysis of numerous literature, we find significant disparities in the interpretation and understanding of research on mathematical anxiety and cognitive impairments, mainly reflected in the emphasis on individual differences and the exploration of general rules and mechanisms.

Some studies indicate that individuals, when facing mathematical tasks, exhibit varying degrees of mathematical anxiety and cognitive impairments due to differences in personal experiences and cognitive processing methods ( Bulthé et al., 2019 ). These studies emphasize proposing targeted intervention measures based on individuals’ specific situations. For example, providing psychological counseling for individuals who have mathematical anxiety due to past failures, and offering learning guidance and strategy training for those lacking effective learning strategies. On the other hand, some studies focus more on exploring the general rules and mechanisms of mathematical anxiety and cognitive impairments, trying to find universal solutions to guide teaching and intervention ( Li et al., 2013 ; Foley et al., 2017 ; Klados et al., 2019 ). For instance, enhancing students’ self-efficacy and autonomous learning abilities is considered an effective way to alleviate mathematical anxiety and improve cognitive impairments.

To achieve more comprehensive and effective teaching and intervention, future research should find a balance between individual differences and general rules. We should consider each student’s uniqueness, providing personalized support that meets their needs, while also exploring the common rules of mathematical anxiety and cognitive impairments, forming systematic and scientific teaching and intervention strategies. This integrated approach helps students overcome mathematical anxiety and cognitive impairments more effectively, thereby improving the effectiveness and quality of mathematical learning.

In conclusion, the differences in research methods and results across various literature provide us with a valuable perspective to understand the interactive relationship between mathematical anxiety and cognitive impairments from multiple angles and dimensions. This diversity and complexity require us to consider various factors comprehensively when analyzing and comparing, allowing for a more comprehensive and profound understanding of the current research status and development trends in this field.

4.7. Limitations

In the current academic environment, significant progress has been made in the research on mathematical cognitive impairments and math anxiety. However, as with many academic studies, research in this field also has its inherent limitations. Below is a detailed discussion of these limitations:

4.7.1. Issue of sample size

For studies in the neuroscience field exploring the relationship between math anxiety and cognitive processes, the size and representativeness of the sample constitute significant limitations.

Firstly, a smaller sample might lead to Type II errors, where an actual effect is not detected due to insufficient sample size. Such limitation could cause researchers to erroneously conclude that there is no association between math anxiety and a particular cognitive process when, in fact, such an association might exist. Additionally, another notable limitation of a small sample is its impact on the replicability of research results. Specific samples might make the results too dependent on that sample, questioning the applicability in a broader population. Secondly, the representativeness of the research sample is another critical limitation. If the research sample lacks diversity, such as being limited to specific age groups, genders, or cultural backgrounds, it might make the research results less universally applicable. For instance, studies involving only university students might not be generalizable to other age groups, constituting its limitation. Practical research conditions and constraints, such as funding, time, and technology, often further exacerbate these limitations. High costs and technical difficulties might limit the number and diversity of recruitable samples, especially in studies involving high-cost technologies such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) ( Artemenko et al., 2019 ; Shi et al., 2023 ).

In conclusion, despite the unique value of neuroscience research, the limitations in sample size and representativeness need full recognition and attention. Researchers should consider these limitations when assessing the reliability and universality of their research results and seek methods to address or mitigate these issues where possible.

4.7.2. Diversity of research methods

In the research on math anxiety and cognitive impairments, a variety of research methods have been employed, including functional magnetic resonance imaging (fMRI), event-related potentials (ERP), behavioral assessment tools, and functional near-infrared spectroscopy (fNIRS). Each of these methods has its unique advantages and limitations, collectively constituting a rich research toolbox, allowing for an in-depth exploration of such complex phenomena from multiple angles.

Firstly, fMRI, with its high spatial resolution, can capture real-time brain activity during mathematical tasks, but its main limitation lies in the inability to directly measure neuronal activity, and it is relatively costly ( Holloway et al., 2013 ). ERP, with its high temporal resolution, can measure the brain’s immediate response to specific stimuli or events, but its spatial resolution is lower, and the source of signals might be challenging to determine ( Klados et al., 2015 ). Behavioral assessment tools are relatively simple, low-cost, and suitable for large-sample research but might not fully reveal individuals’ intrinsic cognitive and emotional processes.

However, the diversity of these methods also brings challenges to research. Different research methods might lead to different interpretations and conclusions regarding the same phenomenon. For example, an fMRI-based study might reveal that a particular brain area is more active in individuals with math anxiety ( Newman et al., 2011 ), while an ERP-based study might find that this activity is related to a specific cognitive process ( Peña and Suárez-Pellicioni, 2014 ). Moreover, since each method has its inherent limitations, such as dependency on technology and equipment, influence of experimental conditions and analysis methods, and interference of cultural, educational backgrounds, and individual differences, researchers need to apply multiple methods comprehensively to understand math anxiety and cognitive impairments more accurately and thoroughly. Through cross-validation and integrated analysis, more in-depth and comprehensive research results are expected to be obtained.

4.7.3. Limitations in research focus

In the field of research on mathematical cognitive impairments and math anxiety, the current focus predominantly lies on fundamental mathematical skills, such as basic computational abilities and numerical representation ( Rosenberg-Lee et al., 2011 ; Prado et al., 2014 ; Jolles et al., 2015 ). The limitation of this focus manifests as a relative neglect of more advanced and complex mathematical cognitive processes, such as abstract thinking, problem-solving, and logical reasoning. This not only may constrain a more comprehensive and profound understanding of math anxiety and mathematical cognitive impairments but also may adversely impact the design and implementation of educational practices and intervention strategies.

Firstly, while fundamental mathematical skills are the basis of mathematical learning, focusing solely on them might overlook the diversity and complexity of mathematical cognition. For instance, abilities like abstract thinking and problem-solving are essential for understanding and mastering mathematical concepts, conducting mathematical reasoning, and solving complex problems. Secondly, the current research focus might also limit a more comprehensive exploration of math anxiety and mathematical cognitive impairments, as these issues are related not only to basic skills but also to more advanced mathematical cognitive processes.

Moreover, an excessive focus on fundamental skills might lead to a monotonous and one-sided orientation in educational and intervention strategies, affecting their comprehensive effectiveness. For more effective promotion of mathematical learning and alleviation of math anxiety, research and practice need to consider the comprehensiveness and diversity of mathematical cognition.

In conclusion, research should pay balanced attention to both fundamental skills and advanced mathematical cognitive processes, to promote comprehensive theoretical and practical development in the field of mathematical cognitive impairments and math anxiety. Future research needs to explore more deeply various aspects of mathematical cognition, including complex and advanced cognitive processes beyond basic skills. This way, we can understand math anxiety and mathematical cognitive impairments more comprehensively and provide richer and more comprehensive guidance for educational practice and intervention strategies.

4.7.4. Issues in interdisciplinary integration

Researching the complex and multifaceted topic of mathematical cognitive impairments and math anxiety involves the intersection of numerous disciplines such as psychology, education, and neuroscience. Although each discipline provides unique and rich perspectives, efficiently integrating these diverse resources has become a core challenge. The primary issue lies in the independent characteristics and focuses of the research methods and theoretical frameworks of each discipline. Psychology mainly studies individuals’ psychological and behavioral responses, neuroscience focuses on exploring biological mechanisms and neural networks, and education emphasizes the effects of teaching methods and educational environments on individuals. This requires researchers to meticulously adjust and integrate the features of each discipline when designing and implementing research. Secondly, successful interdisciplinary research requires researchers to possess high levels of collaboration and communication skills. This not only demands them to understand and master the core knowledge and methods of each discipline but also to establish effective communication and cooperation with peers from different disciplinary backgrounds, undoubtedly setting higher requirements for researchers’ professional competence and interdisciplinary collaboration abilities. Moreover, finding a balance to maintain the uniqueness of each discipline while achieving effective integration is also crucial. Excessive integration might weaken the core advantages of a discipline, while insufficient integration might lead to one-sided or limited research results.

In conclusion, we recognize that interdisciplinary integration in the research of mathematical cognitive impairments and math anxiety is both an essential direction and a challenging task. To obtain more comprehensive and profound research results in this field, we must innovate in theory and methods and strengthen communication and cooperation between disciplines.

4.7.5. Lack of intervention strategies

In the field of research on math anxiety and cognitive impairments, despite the accumulation of rich theoretical knowledge and empirical data, the effective transformation of this knowledge into practical intervention strategies remains a challenge. Currently, although there is a deeper description and explanation of math anxiety and cognitive impairments, practical intervention strategies, whether at the educational, psychological, or neurobiological levels, still appear relatively weak. This poses significant constraints on the application in actual education and psychological health fields.

Firstly, most studies focus on the manifestations and causes of math anxiety and cognitive impairments, with less involvement in specific intervention strategies and methods ( Li et al., 2013 ; Foley et al., 2017 ; Klados et al., 2019 ). This necessitates further empirical research to explore and confirm the actual effects of various intervention strategies. Secondly, even if some strategies achieve preliminary effects in the short term, their sustainability and stability remain unknown due to the lack of long-term follow-up evaluations. Future research must pay more attention to evaluating the long-term effects of intervention strategies and exploring how these strategies can be sustainably applied in daily environments. Moreover, given the significant variations in math anxiety and cognitive impairments among individuals, intervention strategies also need to have a certain level of individualization and customization. How to adjust and optimize strategies based on individual differences will be key in future research. Additionally, the implementation of intervention strategies also faces various practical challenges, such as resource allocation, professional training, and integration with current education and mental health services. Solving these challenges requires interdisciplinary efforts and cooperation.

In conclusion, despite a deeper understanding of math anxiety and cognitive impairments, there are still many difficulties to overcome in the research and application of intervention strategies. It is hoped that future research can provide more guiding and practical strategies and methods for this field.

4.7.6. Influence of cultural and social background

When exploring math anxiety and mathematical cognitive impairments, the role of cultural and social backgrounds is indispensable. Both play a key role in determining individuals’ cognitive development and emotional experiences in the mathematical learning process. However, current research mainly focuses on specific cultural and social environments, providing us with limited perspectives ( Yi-Rong et al., 2011 ; Jolles et al., 2015 ).

Cultural backgrounds profoundly influence the shaping of mathematical cognition, reflected in different cultures’ educational methods, educational emphases, and resource allocations. For instance, some cultures might value basic mathematical skills more, while others might emphasize problem-solving and critical thinking abilities. This implies that the formation of mathematical cognition might vary across different cultures. Social backgrounds also significantly impact math anxiety and mathematical cognitive impairments. They determine societal expectations of mathematical abilities, the social value of mathematical learning, and the general acceptance of math anxiety. For example, some societies might view mathematics as a key competence, while others might not. Furthermore, cultural and social backgrounds also affect how individuals perceive and cope with math anxiety and mathematical cognitive impairments. In some cultures, people might be more willing to openly discuss their difficulties, while in others, they might choose to remain silent about these issues.

In summary, to deeply understand math anxiety and mathematical cognitive impairments, we need to consider a broader cultural and social background. Future research should pay more attention to the diversity of these factors, which can not only provide us with a deeper understanding but also help in formulating more effective and targeted intervention strategies.

In conclusion, although some progress has been made in the research of mathematical cognitive impairments and math anxiety, there are still many unresolved issues and challenges. These limitations remind us to be cautious in interpreting and applying research results and provide direction for future research.

4.8. Future research directions

Based on the review and analysis of existing literature, it is evident that math anxiety and cognitive impairments are two important and interconnected research focuses in the field of mathematical cognition. Future research can further expand and deepen in the following directions:

4.8.1. In-depth study of math anxiety

(1) Exploration of neural mechanisms

To deeply understand the neural mechanisms behind math anxiety, future research should focus on revealing how neurotransmitter changes and synaptic plasticity influence the onset and maintenance of math anxiety. Current research has somewhat demonstrated the association between math anxiety and brain activity patterns through functional brain imaging technologies, but a comprehensive understanding of the neurobiological foundations is still pending. Neurotransmitters such as serotonin, dopamine, and cortisol may play crucial roles in math anxiety. Therefore, there should be a profound exploration of how these neurotransmitters influence the levels of math anxiety and whether they can serve as potential targets for alleviating math anxiety. Additionally, research should assess whether there are abnormalities in the synaptic plasticity of individuals with math anxiety and explore whether these abnormalities are related to the clinical manifestations of math anxiety, thereby providing a more comprehensive and refined theoretical basis for future interventions against math anxiety.

(2) Optimization of intervention strategies

For math anxiety, future research should delve deeper into the optimization and sustainability of intervention strategies. Firstly, the range of intervention methods should be broadened. In addition to cognitive-behavioral therapy, psychoeducation, and relaxation training, considerations could be given to introducing psychodynamic therapy and acceptance and commitment therapy, enriching the diversity of interventions. Simultaneously, research should consider the combined use of different intervention methods to enhance the overall effect of the interventions. Secondly, the focus of research needs to extend from the short-term effects of interventions to long-term effects. Research should conduct long-term follow-ups to comprehensively assess the sustainability and stability of intervention strategies, ensuring their long-term application. Lastly, future research should also deeply understand the mechanisms of intervention actions, clarifying the precise pathways of their effects, and further optimizing the targeting and selection of intervention methods. Through these in-depth studies, it is expected to find more precise and effective strategies, providing continuous and comprehensive support for individuals troubled by math anxiety.

4.8.2. Multidimensional exploration of cognitive impairments

(1) Improvement of diagnostic and assessment tools

Future research on cognitive impairments should focus on the multidimensional improvement and innovation of diagnostic and assessment tools. Firstly, research should focus on developing more accurate and sensitive diagnostic assessment tools. This involves not only the development of new psychological measurement tools but also the revision and optimization of existing tools to assess individuals’ cognitive impairments more comprehensively and accurately. Secondly, more advanced neuroimaging technologies should be utilized to deeply explore and assess cognitive impairments from a neurobiological perspective, aiming to reveal their deeper neural mechanisms. Lastly, by introducing machine learning and artificial intelligence technologies, significant information for diagnosing and assessing cognitive impairments can be identified in big data. By building and training more accurate predictive models, the risk and severity of cognitive impairments can be identified and assessed more effectively. The comprehensive application of these methods and technologies will help improve the accuracy and sensitivity of the diagnosis and assessment of cognitive impairments.

(2) Personalized intervention plans

The direction of future research should focus on the multidimensional exploration of cognitive impairments, especially in formulating personalized intervention plans. For different types and degrees of cognitive impairments, research should design more specific and targeted intervention strategies. For example, interventions for mild cognitive impairments can adopt more gentle and heuristic methods, while severe cognitive impairments require more intensive and systematic strategies. Additionally, the formulation of intervention plans should also fully consider individual differences, such as age, gender, educational background, and psychological characteristics. Thus, by integrating various factors, intervention plans can be more refined and personalized, thereby effectively improving the effectiveness and efficiency of interventions, promoting the improvement and recovery of individuals with cognitive impairments.

4.8.3. Interaction research between math anxiety and cognitive impairments

(1) Exploration of mutual influence mechanisms

An essential direction for future research is to delve deeply into the interaction mechanisms between math anxiety and cognitive impairments. Firstly, research should reveal more precisely how math anxiety affects individuals’ performance in cognitive processes such as working memory, attention allocation, and information processing speed, and how this indirectly leads to the emergence and exacerbation of cognitive impairments. Simultaneously, attention should be given to how cognitive impairments become a source of math anxiety, considering how they enhance individuals’ math anxiety by causing learning difficulties and declining academic performance.

From a neurobiological perspective, future research could explore the common neural foundations of math anxiety and cognitive impairments. For instance, by utilizing brain imaging technologies, the similarities and differences in brain activity and connection patterns between math anxiety and cognitive impairments could be revealed. Also, considerations should be given to exploring their mutual influences on behavioral performances and learning strategies from psychological and educational perspectives. By integrating research methods and perspectives from different disciplines, a more comprehensive understanding of the interactions between math anxiety and cognitive impairments is expected, providing theoretical support for alleviating math anxiety and improving cognitive impairments.

(2) Comprehensive intervention strategies

Regarding the interactive relationship between math anxiety and cognitive impairments, the direction of future research should focus on developing comprehensive intervention strategies to address these two issues bidirectionally. Firstly, for math anxiety, psychological intervention strategies such as cognitive-behavioral therapy, psychoeducation, and relaxation training can be adopted to help individuals stabilize emotions and reduce anxiety levels. Simultaneously, to address cognitive impairments, educational interventions like metacognitive strategy training, problem-solving strategy training, and learning strategy guidance will be effective. A more innovative research direction might involve combining the aforementioned psychological and educational interventions to form a comprehensive intervention model. For example, integrating specific training for cognitive impairments into psychological interventions, or incorporating psychological support for math anxiety into educational interventions. Such comprehensive strategies can not only achieve comprehensive improvements in math anxiety and cognitive impairments but also contribute to providing a more systematic, in-depth understanding and intervention methods for these two major issues.

4.8.4. Interdisciplinary collaborative research

In today’s complex and ever-changing academic research field, interdisciplinary collaborative research has become a trend and necessity, especially on cross-disciplinary issues like math anxiety and cognitive impairments, where experts and scholars from various disciplines need to join hands for exploration and research.

(1) Interdisciplinary integration

Future research on math anxiety should emphasize interdisciplinary integration and collaboration. Math anxiety is not merely a problem in the field of psychology; it is closely related to brain structure and function, educational methods, and individual psychology and emotional responses. Neuroscientists can explore how math anxiety affects brain activity using advanced brain imaging technologies, providing a scientific basis for formulating corresponding intervention measures. Educators can delve deeply into educational methods and strategies, exploring how to reduce the incidence of math anxiety through educational interventions and applying research findings to educational practice. The contribution of psychologists lies in deeply analyzing the psychological mechanisms of math anxiety, studying its association with other psychological problems, and looking for possibilities of comprehensive interventions. This interdisciplinary collaboration helps us understand the causes of math anxiety from different dimensions and levels comprehensively and find more effective intervention methods.

(2) International collaboration

With the acceleration of globalization, research on math anxiety and cognitive impairments also shows an increasingly international trend. Faced with this global issue, the importance of interdisciplinary and international collaboration becomes more prominent. International collaboration not only promotes resource sharing, improving research efficiency and quality, but also broadens research perspectives through the exchange and collision of different cultures and educational backgrounds, leading to the exploration of new research directions and methods. More importantly, international collaboration can promote the global dissemination and application of research findings, providing global strategies for solving problems of math anxiety and cognitive impairments. In conclusion, through deepening interdisciplinary integration and international collaboration, we can expect to promote the progress of research on math anxiety and cognitive impairments more quickly and effectively, providing more scientific and practical theoretical support and strategic suggestions for related educational practices.

Through in-depth research in the above directions, we can expect richer and more profound research findings in the fields of math anxiety and cognitive impairments, providing more scientific and effective theoretical support and strategic suggestions for educational practices.

5. Conclusion

5.1. main findings.

This review systematically analyzes a wealth of related literature, aiming to deeply explore the core roles of math anxiety and cognitive impairments in mathematical cognition processes and their interconnections. In terms of cognitive impairments, the study reveals two primary forms of impediments: developmental calculation disorders and acquired calculation disorders. The former typically manifests as inherent calculation deficiencies without apparent neurophysiological damage, while the latter usually results from some form of brain injury or disease causing the loss or decline of calculation abilities. However, appropriate training and intervention strategies may help improve or alleviate the symptoms of these impediments.

Regarding math anxiety, this phenomenon plays a crucial role in mathematical cognitive processes. Math anxiety not only affects the representation of numbers and calculations but also interferes with the resolution of arithmetic problems and spatial processing abilities. Individuals suffering from math anxiety may find mathematical tasks more challenging, leading to increased psychological stress and further decline in mathematical performance.

By integrating these findings, this article helps comprehensively understand how math anxiety and cognitive impairments mutually influence mathematical cognition at multiple levels. This understanding not only deepens our exploration of the psychological and neural mechanisms of mathematical cognition but also provides theoretical support for formulating precise and effective educational intervention strategies for various types of mathematical difficulties. Future research should further explore how to help individuals affected by math anxiety and cognitive impairments more effectively through integrated intervention methods, promoting their mathematical learning and self-development.

5.2. Practical applications

The profound findings of this study offer valuable insights into the educational field, especially concerning resolving students’ math anxiety and cognitive impairment issues. Firstly, for students with cognitive impairments, educators can design targeted and personalized teaching strategies and intervention measures based on individual differences. For instance, creating a more supportive learning environment and providing abundant practical opportunities to enhance their mathematical cognitive abilities, while stimulating students’ learning enthusiasm and confidence through cooperative learning and group discussions. Secondly, to alleviate students’ math anxiety, educators should strengthen care and support, helping students gradually overcome anxiety through more guiding and encouraging methods. Introducing lively and interesting teaching methods, such as games and stories, is also an effective way to improve students’ learning interest and participation. Additionally, educators can flexibly adjust teaching strategies to meet the specific needs of different students. For example, integrating more practical applications and case analyses into teaching, allowing students to feel the practicality of mathematical learning, enhancing learning enthusiasm and motivation. Simultaneously, through regular feedback and evaluation, educators can timely grasp students’ learning conditions, make corresponding teaching adjustments, and meet students’ learning needs more precisely.

In summary, this study provides educators with theoretical support and strategic guidance for practically addressing students’ math anxiety and cognitive impairments. Educators should fully utilize these research findings, scientifically and reasonably design and implement teaching activities, aiming to improve students’ mathematical cognitive abilities and learning outcomes, helping them overcome difficulties and challenges in the learning process.

Author contributions

HY: Conceptualization, Writing – original draft, Writing – review & editing.

Funding Statement

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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How flotation therapy may help your mental health

Flotation therapy — which involves floating in a tank of warm, salt-saturated water — is a popular and often expensive form of relaxation. Now, a small but growing body of research suggests it may also reduce symptoms of a variety of mental health conditions.

Most float tank sessions last about an hour. During a typical experience, a person disrobes in a private room and enters the pod, which may resemble an oversize hot tub. The pod is filled with shallow, body-temperature water that is saturated with Epsom salts to buoy your body. You can leave the pod open or close the lid to be cocooned in an environment devoid of light and sound.

Experts say float therapy seems to work on several levels, heightening the senses, aiding relaxation and soothing the body and mind.

“It calms the mind, sharpens our sense of the body and helps us live in the moment — all of which can break the cycle of negative thoughts,” said Sahib Khalsa, principal investigator and clinical director at the Laureate Institute for Brain Research in Tulsa, a hub of float therapy research.

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Abby Michel, 27, had been in therapy for anxiety since she graduated from high school. When she moved to Boston in 2019, she was hired as a receptionist at the Indoor Oasis, a wellness center with float tanks in Newton, Mass. Michel said she began floating regularly, and it became “an important tool in the toolbox” to manage her anxiety.

“It’s become like a ritual,” she said. “You unwind, and you begin to see life differently, from a more distanced perspective.”

The science behind flotation therapy

The research on the mental health benefits of flotation therapy is mixed and limited. Some studies have shown that float therapy may reduce symptoms of a variety of conditions, including generalized anxiety disorder , as well as depression and anxiety . The therapy also appears to lower blood pressure and decrease soreness after high-intensity exercise . Preliminary research suggests it may minimize cravings in addiction-related illness.

A 2021 meta-analysis of studies on float therapy for mental health conditions found “limited evidence” from two randomized controlled trials that floating may reduce anxiety and symptoms associated with anxiety, including muscle tension, sleep difficulties and depression.

Marc Wittmann, a brain and time perception researcher based in Freiberg, Germany, compares the experience of flotation to meditation. “During meditation, you feel that you lose your body boundaries and you are more one with the environment,” said Wittmann, who uses float tanks for some of his research.

He says this dissolving sense of one’s body is correlated with lower anxiety, according to his own research that hasn’t been published yet.

One explanation for the potential mental health benefits of floating is that it can enhance a biological process known as “interoception.” Interoception is defined as the process by which the nervous system “senses, interprets and integrates” signals from the body — essentially how the brain understands the body. Dysfunction of interoception may play a role in anxiety and eating disorders, among other mental health conditions.

One problem in evaluating specific therapies, such as floating, is teasing out the various mechanisms at work, said Wen Chen, the National Institutes of Health branch chief for basic and mechanistic research in complementary and integrative health. "Is there something special about flotation, or not?” she said. “Maybe you’re just in a very relaxing environment, so how unique is it compared to other relaxation techniques we have?”

Flotation therapy for eating disorders

Some researchers are studying whether flotation therapy can help people with eating disorders. One NIH-funded study, which is currently recruiting patients, will investigate how augmenting float therapy with interoception-focused psychotherapy can improve anxiety and body image in patients with anorexia nervosa.

In another study, a randomized controlled trial that included 68 women and girls hospitalized for anorexia, researchers reported that twice-weekly float sessions improved patients’ levels of body dissatisfaction, a hallmark of the disease.

Patients were shown images of different body shapes and sizes and asked to choose the image that best matched their own bodies and their ideal body.

Right after float therapy, and again six months later, patients showed “significant” reductions in body dissatisfaction, meaning they chose images of bodies that more closely matched their own, demonstrating a less distorted body image, researchers said.

“Floating might shift attention away from how the body looks to how it feels, promoting a healthier body image,” said Khalsa, the study’s senior author.

What float therapy feels like

Consumer demand for float tanks is growing. According to one estimate , there are now close to 400 float centers in the United States, up from about 50 in 2010, with costs ranging from approximately $50 to $100 per 60- or 90-minute session. Many of the newer float centers are a far cry from the “sensory deprivation” tanks made infamous in pop culture with films like “ Altered States .” These days, people can add calming music and serene lighting to their float experiences and choose less claustrophobia-inducing pools that are more like open-style hot tubs.

Justin Feinstein, director and president of the nonprofit Float Research Collective , which is raising money to conduct more research, said many doctors still hold outdated views about the practice.

“Floating has been referred to as ‘sensory deprivation,’ which I think is a misnomer,” Feinstein said. On the contrary, he said, patients have reported “enhancement” of internal sensations during flotation, such as noticing their breath and heartbeat.

For some individuals, floating can offer profound relief.

After a slew of unsuccessful treatments for eating disorders, Emily Noren, 28, of San Diego, tried floating.

At first, she said she found the experience uncomfortable. But she made it through the first 90-minute float and returned for more.

“The float tank helped me to take a break from the real world, a break from my body for a little bit,” said Noren, who has self-published a book, “ Unsinkable ,” about the experience. “Before, I’d hear the eating disorder voice, the diet commercial voice, the influencer who lost weight voice, the dad trauma voice. In the float tank, I could hear my own voice.”

Marty Gibbons, the owner of a contracting business in Portland, Ore., used flotation to avoid taking painkillers after breaking his leg in a skydiving accident.

“I started floating three days a week for six months straight,” said Gibbons, who is sober and didn’t want to take prescription pain drugs. “I didn’t take one opiate. I floated and used ice. That was my pain regimen.”

Do you have a question about human behavior or neuroscience? Email [email protected] and we may answer it in a future column.

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