essay about distance learning modality

Distance-Learning Modalities in Education Essay

Introduction.

Distance education relates to an instruction delivery modality where learning occurs between the educator and students who are geographically isolated from each other during the learning process. Distance learning modalities include off-site satellite classes, video conferencing and teleconferencing, web-based instruction, and faculty-supervised clinical experiences, including internships. This study focuses on satellite campuses and videoconferencing and teleconferencing modalities. It provides a comprehensive listing of these modalities’ benefits and drawbacks, as well as a detailed comparison between them, and describes outcomes related to distance learning’s efficacy.

Pros and Cons of Selected Modalities

Satellite campuses.

Satellite campuses relate to programs that provide the curriculum in part or whole on off-campus locations from the parent organization. Studies associate satellite campuses with several advantages; first, it promotes interpersonal communication between students and faculty and improves cultural diversity. Second, it enhances learners’ experiences and opportunities, including campus choices, small class sizes at off-site campuses, and flexibility in viewing lecturers (Keating, 2014). Third, it allows dual-degree options and the opportunity to utilize and adopt distance education technologies. Fourth, off-site campuses can help increase student enrolment in the program, especially those from underrepresented regions. Ultimately, this modality has been associated with cost-related efficiencies, including the need for fewer faculty members and resource sharing.

Researchers also associate this modality with several cons; first, locating qualified off-site faculty to teach a specific subject content since they should be oriented to the curricula to ensure integrity. Finding faculty with the necessary expertise is difficult; therefore, parent campuses need an additional course coordinator to ensure the course’s integrity. Second, additional resources are required to implement or develop distant satellite campuses. Third, satellite campuses frequently lose students to the parent campus (Keating, 2014). Fourth, this modality has been linked with probable incongruence between the courses’ implementation in the curriculum due to the lack of interaction amid faculty and staff within the home campus. Fifth, since it depends on part-time faculty, institutional commitment is low. Lastly, satellite campuses must limit program offerings to reduce the risks of the program’s cost becoming unsustainable.

Videoconferencing and Teleconferencing

Teleconferencing and videoconferencing demand the installation of dedicated classrooms capable of sending and receiving digital information both off-site and on-site. This modality has been linked with several benefits; first, it allows real-time interactions between faculty and students. Next, faculty members can introduce guest speakers and live events in class without taking them to those events (Keating, 2014). The curriculum can be delivered to a large audience, increasing the chances of the curriculum’s integrity is maintained. It also reduces travel expenses and the costs for program mounting and maintenance. Additionally, success rates are high for institutions with sufficient institutional capabilities and support (Keating, 2014). Lastly, partnerships are mutually beneficial for educational institutions and healthcare organizations aiming to increase professional development or continuing education.

There several drawbacks linked to videoconferencing and teleconferencing; first, it requires specialized equipment and tools for class delivery, making education expensive. Second, institutions with limited reception and connectivity may miss out on live interactions. Third, it requires close monitoring from an expert to enhance implementation and compliance to sponsoring agency’s priority rights. Ultimately, this modality requires faculty with specialized expertise in different teaching media to ensure its success as an instructional delivery methodology.

Analysis and Comparison

Satellite Campuses incorporate face-face interactions and videoconferencing, and web-based instruction to instruct students. However, videoconferencing and teleconferencing involve delivering curriculum content via classrooms that can receive and send digital data. Both modalities allow face-to-face interaction, although satellite campus interactions are physical while that videoconferencing is online. They emphasize interaction and didactic relationships between faculty and students (Keating, 2014). The parent campus plays an integral role in the seamless implementation of the program in both modalities. Moreover, in satellite campuses, part of the classes is conducted on-site at the parent campus. Similarly, videoconferencing can also be delivered in on-site classes. Both modalities’ parent campus is responsible for ensuring course integrity and that the curriculum satisfies their educational goals.

Outcome Description Related to Teaching and Learning Effectiveness and Student and Faculty Satisfaction

Distance learning (DL) is associated with positive learning outcomes and student and faculty satisfaction. Berndt et al. (2017) showed that rural health practitioners and class facilitators were highly satisfied with distance learning strategies in professional development. Al-Balas et al. (2020) also supported this notion that distance learning improves learning outcomes. The study showed the approach provides students with educational autonomy and facilitates access to a broader range of learning resources. Another survey by Tomaino et al. (2021) demonstrated that DL produced positive educational outcomes in students with severe developmental and behavioral needs. The study participants showed positive progress in achieving their academic goals when they were engaged in DL. Tomaino et al. (2021) revealed that using audio, video, computers, and the internet increased parental engagement in students’ education. The DL helped the parents assess their children’s academic ability and learn how to effectively manage their disabilities and challenging behaviors with the instructor’s help.

DL is also cost-effective and can lead to cost-savings related to travel time. Rotimi et al. (2017) report that web-based programs make learning cheaper and are viable alternatives to face-to-face classes. Berndt et al. (2017) revealed that DL reduced healthcare providers’ travel time and costs (learners). Therefore, it can be surmised that DL can reduce travel costs for learners. Another aspect that stood out in Berndt et al. (2017) study is that face-to-face learning does not result in better outcomes than DL. The study showed that distance education produces better learning outcomes irrespective of delivery modes (Berndt et al., 2018). Learning outcomes were measured using learners’ knowledge and satisfaction scores.

However, it is essential to note that distance learning does not always result in high satisfaction levels unless certain conditions are fulfilled. Al-Balas et al. (2020) showed that medical students have negative perceptions of distance learning and were unsatisfied with it. The participants’ negative perception and dissatisfaction with distance learning emanated from their past experiences with the program. The students revealed that most distance-learning faculty were uncooperative and generally preferred the traditional approach. This revelation confirms Keating’s (2017) assertion that a critical weakness of distance learning is the lack of faculty commitment. Approximately 55.2% of the students also believed that other medical students would not commit to DL programs (Al-Balas et al., 2020). Satisfaction with DL was strongly connected to student’s experiences and interactions with the program’s faculty. Therefore, institutions should implement strategies to enhance both faculty and students’ reception of the program. On a positive note, 75.5% of the study participants preferred the blended education approach (mix of distance learning and on-site classes) (Al-Balas et al., 2020). This preference should encourage the integration of distance learning modalities with the traditional learning style.

Technological advancements in the education sector allow both learners and faculty members to easily access, collect, evaluate, and disseminate knowledge and relevant data. Learning communities have currently evolved from conventional classrooms to e-learning settings. Students converge or connect in a virtual surrounding to solve issues, exchange ideas, develop new meanings, and explore alternatives. There are various distance learning modalities, including teleconferencing and videoconferencing and satellite campuses. These methodologies have been associated with significant benefits and drawbacks.

Al-Balas, M., Al-Balas, H. I., Jaber, H. M., Obeidat, K., Al-Balas, H., Aborajooh, E. A., Al-Taher, R., & Al-Balas, B. (2020). Distance learning in clinical medical education amid COVID-19 pandemic in Jordan: Current situation, challenges, and perspectives . BMC Medical Education , 20 , 1–7. Web.

Berndt, A., Murray, C. M., Kennedy, K., Stanley, M. J., & Gilbert-Hunt, S. (2017). Effectiveness of distance learning strategies for continuing professional development (CPD) for rural allied health practitioners: A systematic review. BMC Medical Education , 17 , 1–13. Web.

Keating, S. B. (2014). Curriculum development and evaluation in nursing (3 rd ed.). Springer Publishing.

Rotimi, O., Orah, N., Shaaban, A., Daramola, A. O., & Abdulkareem, F. B. (2017). Remote teaching of histopathology using scanned slides via skype between the United Kingdom and Nigeria . Archives of Pathology & Laboratory Medicine , 141 (2), 298–300. Web.

Tomaino, M. A. E., Greenberg, A. L., Kagawa-Purohit, S. A., Doering, S. A., & Miguel, E. S. (2021). An assessment of the feasibility and effectiveness of distance learning for students with severe developmental disabilities and high behavioral needs. Behavior Analysis in Practice , 1 (1), 1–16. Web.

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Distance learning, as it is known to many students, is the online learning and teaching programs offered by world class institutions of learning. Unlike traditional classroom education, students are virtually enrolled in their programs and respective classes online. Statistical data taken from the leading institutions of higher learning in the United States and United Kingdom show that the number of students registering for distance learning programs is increasing day and night. In the United States alone, the number of students taking courses through distance education has since risen from 3.9 million in 2010 to approximately 8.9 million students in 2013. Whereas distance learning is applauded for its inherent ability to reduce illiteracy amongst the Americans through promotion of cheaper internet enabled computer programs, the mode of education has been found out to compromise the quality of learning outcomes.

Although traditional classroom education remains the mode of learning which is widely practiced and offered by most of the institutions such as colleges and universities across the world, distance learning is increasingly becoming more popular in the age of information technology. Distance learning is no longer an alternative mode of learning to traditional education but a preferred mode of learning across the world. The most recent survey conducted among college students revealed that 80% of the college and university students are in favor of distance learning because of its flexibility. The subsequent popularity of distance learning is attributed to fact it is the only mode of education that gives students freedom to choose the convenient time of the night or day to take classes. Unlike the subjective traditional face-to-face education with its fixed teaching and learning schedule, the highly individualized distance learning gives students full freedom on when and what they want to learn.

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Secondly, distance learning reaches the highest number of students within the shortest period of time as opposed to the traditional face-to-face learning. The number of students graduating from various institutions of education after undertaking distance learning programs is increasing every year. Statistics show that about there are about 9 million students registered for various distance learning programs in the United States last year and the figures are on an upward trend. The flaring number of students opting for the distance learning implies that larger segments of illiterate populations are effectively reached. Consequently, the mode of learning has proven to the most effective and convenient method of combating higher rates of illiteracy across the continents. Apart from its accessibility, multitudes of learners successfully complete their courses because distance learning programs are far cheaper than compared to traditional learning programs.

Suffice it to say, there is substantial evidence that distance learning has proven to be more effective tool in promoting literacy amongst the adult populations. It is more suitable for the adult learners who are either in full time employment or committed in their domestic duties thus, cannot manage to fit in traditional mode of education with fixed schedule. With the full knowledge that the internet-enabled mode of learning takes place in the comfort of living rooms, many mature learners find distance learning more palatable because it upholds their confidentiality and privacy. In this regard, the electronic mode of learning renders education a private affair compared to traditional education that makes education a public affair. It therefore goes without saying that distance learning has adequately counteracted shame that most adult students face in their efforts to access education programs in traditional institutions.

Most importantly, distance learning programs are designed to meet the diverse needs of learners like no other. For instance, the programs are scheduled to ensure that learners who are in active job with tight work schedule, parents taking care of their children, and persons living outside the catchment areas of the learning institutions can create time and study at their own convenient time. Both the young and old; men and women; the rich and poor are satisfactorily accommodated by the distance learning education programs. In addition to this, distance learning educational programs are designed in a way that individual learners can study at their pace; students are at liberty to start, break and resume personalized studies at their own discretion. This rare phenomenon gives distance learning an upper hand above traditional classroom face-to-face learning.

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Despite the numerous gains and advantages that come with the distance education on the students' side, it has been established that learning at home behind an internet-enabled computer cannot replace face to face education existing in institutions of higher learning such as universities and colleges. On many occasions, educational experts have raised their concern on the effectiveness of distance learning on pedagogical delivery of complex concepts especially in science-oriented subjects such as chemistry and mathematics. According to the latest research finding, distance learning is limited to the kind of courses they offer to students. For instance, technical courses such as engineering, applied technology and mechanics that require the instructors to impart psychomotor and manipulative skills to learners could not be delivered via distance learning programs. The much desired delivery of technical courses of this nature is therefore an exclusive reserve of the traditional face-to-face education. At the end of it all, It emerges that traditional face-to-face education produces better results in technical subjects that requires practical skills.

It has been proven over and over again that there are a lot of difficulties in self-directed learning which is demanded by the online education. Many a times, students undertaking online courses do not have set schedule for their studies thus, leaving much room for distracters that altogether work to the detriment of students' academic performance. Taking into consideration that students are left to study on their own while at the same time being least supervised by their course instructors, most of the students do not see the need to delve into their studies before the examination period. The reduced contact hours between instructors and students due to exclusive use of virtual interactive platform, instructors will not be able to constantly monitor students' learning progress. In this case, the outcome of the learning process in learners is compromised because instructors often fail to identify students' weaknesses in distance learning. On the other hand, instructors quickly identify individual learner's areas of weaknesses and fix them in time to bring about desirable learning outcome in learners.

Lack of the physical interaction between students and course instructors in the distance learning programs leads to gross instructional misunderstanding. This could have unbearable detrimental effects on the accuracy and effectiveness with which learning objectives are met. Contrary to the traditional face-to-face form of education, distance learning deprives students of the adequate opportunity to be in constant contact with their course instructors. Therefore, they are bound to experience instruction breakdown from the internet learning interface. It is imperative to note, however, that distance education leads to increased incidences of cheating alongside other host of irregularities in online examinations.

In conclusion, distance learning has proven to be more convenient, cheaper and confidential learner-friendly mode of learning. The global enrolment rates in the institutions of higher learning have shot up tremendously since the rolling out of distance learning educational programs. Judging from the ongoing trends, it is evident that distance learning will continue to gain prominence over the traditional face-to-face education.

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• Review Article
• Published: 27 September 2021

Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap

• Sébastien Goudeau   ORCID: orcid.org/0000-0001-7293-0977 1 ,
• Camille Sanrey   ORCID: orcid.org/0000-0003-3158-1306 1 ,
• Arnaud Stanczak   ORCID: orcid.org/0000-0002-2596-1516 2 ,
• Antony Manstead   ORCID: orcid.org/0000-0001-7540-2096 3 &
• Céline Darnon   ORCID: orcid.org/0000-0003-2613-689X 2  

Nature Human Behaviour volume  5 ,  pages 1273–1281 ( 2021 ) Cite this article

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The COVID-19 pandemic has forced teachers and parents to quickly adapt to a new educational context: distance learning. Teachers developed online academic material while parents taught the exercises and lessons provided by teachers to their children at home. Considering that the use of digital tools in education has dramatically increased during this crisis, and it is set to continue, there is a pressing need to understand the impact of distance learning. Taking a multidisciplinary view, we argue that by making the learning process rely more than ever on families, rather than on teachers, and by getting students to work predominantly via digital resources, school closures exacerbate social class academic disparities. To address this burning issue, we propose an agenda for future research and outline recommendations to help parents, teachers and policymakers to limit the impact of the lockdown on social-class-based academic inequality.

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The widespread effects of the COVID-19 pandemic that emerged in 2019–2020 have drastically increased health, social and economic inequalities 1 , 2 . For more than 900 million learners around the world, the pandemic led to the closure of schools and universities 3 . This exceptional situation forced teachers, parents and students to quickly adapt to a new educational context: distance learning. Teachers had to develop online academic materials that could be used at home to ensure educational continuity while ensuring the necessary physical distancing. Primary and secondary school students suddenly had to work with various kinds of support, which were usually provided online by their teachers. For college students, lockdown often entailed returning to their hometowns while staying connected with their teachers and classmates via video conferences, email and other digital tools. Despite the best efforts of educational institutions, parents and teachers to keep all children and students engaged in learning activities, ensuring educational continuity during school closure—something that is difficult for everyone—may pose unique material and psychological challenges for working-class families and students.

Not only did the pandemic lead to the closure of schools in many countries, often for several weeks, it also accelerated the digitalization of education and amplified the role of parental involvement in supporting the schoolwork of their children. Thus, beyond the specific circumstances of the COVID-19 lockdown, we believe that studying the effects of the pandemic on academic inequalities provides a way to more broadly examine the consequences of school closure and related effects (for example, digitalization of education) on social class inequalities. Indeed, bearing in mind that (1) the risk of further pandemics is higher than ever (that is, we are in a ‘pandemic era’ 4 , 5 ) and (2) beyond pandemics, the use of digital tools in education (and therefore the influence of parental involvement) has dramatically increased during this crisis, and is set to continue, there is a pressing need for an integrative and comprehensive model that examines the consequences of distance learning. Here, we propose such an integrative model that helps us to understand the extent to which the school closures associated with the pandemic amplify economic, digital and cultural divides that in turn affect the psychological functioning of parents, students and teachers in a way that amplifies academic inequalities. Bringing together research in social sciences, ranging from economics and sociology to social, cultural, cognitive and educational psychology, we argue that by getting students to work predominantly via digital resources rather than direct interactions with their teachers, and by making the learning process rely more than ever on families rather than teachers, school closures exacerbate social class academic disparities.

First, we review research showing that social class is associated with unequal access to digital tools, unequal familiarity with digital skills and unequal uses of such tools for learning purposes 6 , 7 . We then review research documenting how unequal familiarity with school culture, knowledge and skills can also contribute to the accentuation of academic inequalities 8 , 9 . Next, we present the results of surveys conducted during the 2020 lockdown showing that the quality and quantity of pedagogical support received from schools varied according to the social class of families (for examples, see refs. 10 , 11 , 12 ). We then argue that these digital, cultural and structural divides represent barriers to the ability of parents to provide appropriate support for children during distance learning (Fig. 1 ). These divides also alter the levels of self-efficacy of parents and children, thereby affecting their engagement in learning activities 13 , 14 . In the final section, we review preliminary evidence for the hypothesis that distance learning widens the social class achievement gap and we propose an agenda for future research. In addition, we outline recommendations that should help parents, teachers and policymakers to use social science research to limit the impact of school closure and distance learning on the social class achievement gap.

Economic, structural, digital and cultural divides influence the psychological functioning of parents and students in a way that amplify inequalities.

The digital divide

Unequal access to digital resources.

Although the use of digital technologies is almost ubiquitous in developed nations, there is a digital divide such that some people are more likely than others to be numerically excluded 15 (Fig. 1 ). Social class is a strong predictor of digital disparities, including the quality of hardware, software and Internet access 16 , 17 , 18 . For example, in 2019, in France, around 1 in 5 working-class families did not have personal access to the Internet compared with less than 1 in 20 of the most privileged families 19 . Similarly, in 2020, in the United Kingdom, 20% of children who were eligible for free school meals did not have access to a computer at home compared with 7% of other children 20 . In 2021, in the United States, 41% of working-class families do not own a laptop or desktop computer and 43% do not have broadband compared with 8% and 7%, respectively, of upper/middle-class Americans 21 . A similar digital gap is also evident between lower-income and higher-income countries 22 .

Second, simply having access to a computer and an Internet connection does not ensure effective distance learning. For example, many of the educational resources sent by teachers need to be printed, thereby requiring access to printers. Moreover, distance learning is more difficult in households with only one shared computer compared with those where each family member has their own 23 . Furthermore, upper/middle-class families are more likely to be able to guarantee a suitable workspace for each child than their working-class counterparts 24 .

In the context of school closures, such disparities are likely to have important consequences for educational continuity. In line with this idea, a survey of approximately 4,000 parents in the United Kingdom confirmed that during lockdown, more than half of primary school children from the poorest families did not have access to their own study space and were less well equipped for distance learning than higher-income families 10 . Similarly, a survey of around 1,300 parents in the Netherlands found that during lockdown, children from working-class families had fewer computers at home and less room to study than upper/middle-class children 11 .

Data from non-Western countries highlight a more general digital divide, showing that developing countries have poorer access to digital equipment. For example, in India in 2018, only 10.7% of households possessed a digital device 25 , while in Pakistan in 2020, 31% of higher-education teachers did not have Internet access and 68.4% did not have a laptop 26 . In general, developing countries lack access to digital technologies 27 , 28 , and these difficulties of access are even greater in rural areas (for example, see ref. 29 ). Consequently, school closures have huge repercussions for the continuity of learning in these countries. For example, in India in 2018, only 11% of the rural and 40% of the urban population above 14 years old could use a computer and access the Internet 25 . Time spent on education during school closure decreased by 80% in Bangladesh 30 . A similar trend was observed in other countries 31 , with only 22% of children engaging in remote learning in Kenya 32 and 50% in Burkina Faso 33 . In Ghana, 26–32% of children spent no time at all on learning during the pandemic 34 . Beyond the overall digital divide, social class disparities are also evident in developing countries, with lower access to digital resources among households in which parental educational levels were low (versus households in which parental educational levels were high; for example, see ref. 35 for Nigeria and ref. 31 for Ecuador).

Unequal digital skills

In addition to unequal access to digital tools, there are also systematic variations in digital skills 36 , 37 (Fig. 1 ). Upper/middle-class families are more familiar with digital tools and resources and are therefore more likely to have the digital skills needed for distance learning 38 , 39 , 40 . These digital skills are particularly useful during school closures, both for students and for parents, for organizing, retrieving and correctly using the resources provided by the teachers (for example, sending or receiving documents by email, printing documents or using word processors).

Social class disparities in digital skills can be explained in part by the fact that children from upper/middle-class families have the opportunity to develop digital skills earlier than working-class families 41 . In member countries of the OECD (Organisation for Economic Co-operation and Development), only 23% of working-class children had started using a computer at the age of 6 years or earlier compared with 43% of upper/middle-class children 42 . Moreover, because working-class people tend to persist less than upper/middle-class people when confronted with digital difficulties 23 , the use of digital tools and resources for distance learning may interfere with the ability of parents to help children with their schoolwork.

Unequal use of digital tools

A third level of digital divide concerns variations in digital tool use 18 , 43 (Fig. 1 ). Upper/middle-class families are more likely to use digital resources for work and education 6 , 41 , 44 , whereas working-class families are more likely to use these resources for entertainment, such as electronic games or social media 6 , 45 . This divide is also observed among students, whereby working-class students tend to use digital technologies for leisure activities, whereas their upper/middle-class peers are more likely to use them for academic activities 46 and to consider that computers and the Internet provide an opportunity for education and training 23 . Furthermore, working-class families appear to regulate the digital practices of their children less 47 and are more likely to allow screens in the bedrooms of children and teenagers without setting limits on times or practices 48 .

In sum, inequalities in terms of digital resources, skills and use have strong implications for distance learning. This is because they make working-class students and parents particularly vulnerable when learning relies on extensive use of digital devices rather than on face-to-face interaction with teachers.

The cultural divide

Even if all three levels of digital divide were closed, upper/middle-class families would still be better prepared than working-class families to ensure educational continuity for their children. Upper/middle-class families are more familiar with the academic knowledge and skills that are expected and valued in educational settings, as well as with the independent, autonomous way of learning that is valued in the school culture and becomes even more important during school closure (Fig. 1 ).

Unequal familiarity with academic knowledge and skills

According to classical social reproduction theory 8 , 49 , school is not a neutral place in which all forms of language and knowledge are equally valued. Academic contexts expect and value culture-specific and taken-for-granted forms of knowledge, skills and ways of being, thinking and speaking that are more in tune with those developed through upper/middle-class socialization (that is, ‘cultural capital’ 8 , 50 , 51 , 52 , 53 ). For instance, academic contexts value interest in the arts, museums and literature 54 , 55 , a type of interest that is more likely to develop through socialization in upper/middle-class families than in working-class socialization 54 , 56 . Indeed, upper/middle-class parents are more likely than working-class parents to engage in activities that develop this cultural capital. For example, they possess more books and cultural objects at home, read more stories to their children and visit museums and libraries more often (for examples, see refs. 51 , 54 , 55 ). Upper/middle-class children are also more involved in extra-curricular activities (for example, playing a musical instrument) than working-class children 55 , 56 , 57 .

Beyond this implicit familiarization with the school curriculum, upper/middle-class parents more often organize educational activities that are explicitly designed to develop academic skills of their children 57 , 58 , 59 . For example, they are more likely to monitor and re-explain lessons or use games and textbooks to develop and reinforce academic skills (for example, labelling numbers, letters or colours 57 , 60 ). Upper/middle-class parents also provide higher levels of support and spend more time helping children with homework than working-class parents (for examples, see refs. 61 , 62 ). Thus, even if all parents are committed to the academic success of their children, working-class parents have fewer chances to provide the help that children need to complete homework 63 , and homework is more beneficial for children from upper-middle class families than for children from working-class families 64 , 65 .

School closures amplify the impact of cultural inequalities

The trends described above have been observed in ‘normal’ times when schools are open. School closures, by making learning rely more strongly on practices implemented at home (rather than at school), are likely to amplify the impact of these disparities. Consistent with this idea, research has shown that the social class achievement gap usually greatly widens during school breaks—a phenomenon described as ‘summer learning loss’ or ‘summer setback’ 66 , 67 , 68 . During holidays, the learning by children tends to decline, and this is particularly pronounced in children from working-class families. Consequently, the social class achievement gap grows more rapidly during the summer months than it does in the rest of the year. This phenomenon is partly explained by the fact that during the break from school, social class disparities in investment in activities that are beneficial for academic achievement (for example, reading, travelling to a foreign country or museum visits) are more pronounced.

Therefore, when they are out of school, children from upper/middle-class backgrounds may continue to develop academic skills unlike their working-class counterparts, who may stagnate or even regress. Research also indicates that learning loss during school breaks tends to be cumulative 66 . Thus, repeated episodes of school closure are likely to have profound consequences for the social class achievement gap. Consistent with the idea that school closures could lead to similar processes as those identified during summer breaks, a recent survey indicated that during the COVID-19 lockdown in the United Kingdom, children from upper/middle-class families spent more time on educational activities (5.8 h per day) than those from working-class families (4.5 h per day) 7 , 69 .

Unequal dispositions for autonomy and self-regulation

School closures have encouraged autonomous work among students. This ‘independent’ way of studying is compatible with the family socialization of upper/middle-class students, but does not match the interdependent norms more commonly associated with working-class contexts 9 . Upper/middle-class contexts tend to promote cultural norms of independence whereby individuals perceive themselves as autonomous actors, independent of other individuals and of the social context, able to pursue their own goals 70 . For example, upper/middle-class parents tend to invite children to express their interests, preferences and opinions during the various activities of everyday life 54 , 55 . Conversely, in working-class contexts characterized by low economic resources and where life is more uncertain, individuals tend to perceive themselves as interdependent, connected to others and members of social groups 53 , 70 , 71 . This interdependent self-construal fits less well with the independent culture of academic contexts. This cultural mismatch between interdependent self-construal common in working-class students and the independent norms of the educational institution has negative consequences for academic performance 9 .

Once again, the impact of these differences is likely to be amplified during school closures, when being able to work alone and autonomously is especially useful. The requirement to work alone is more likely to match the independent self-construal of upper/middle-class students than the interdependent self-construal of working-class students. In the case of working-class students, this mismatch is likely to increase their difficulties in working alone at home. Supporting our argument, recent research has shown that working-class students tend to underachieve in contexts where students work individually compared with contexts where students work with others 72 . Similarly, during school closures, high self-regulation skills (for example, setting goals, selecting appropriate learning strategies and maintaining motivation 73 ) are required to maintain study activities and are likely to be especially useful for using digital resources efficiently. Research has shown that students from working-class backgrounds typically develop their self-regulation skills to a lesser extent than those from upper/middle-class backgrounds 74 , 75 , 76 .

Interestingly, some authors have suggested that independent (versus interdependent) self-construal may also affect communication with teachers 77 . Indeed, in the context of distance learning, working-class families are less likely to respond to the communication of teachers because their ‘interdependent’ self leads them to respect hierarchies, and thus perceive teachers as an expert who ‘can be trusted to make the right decisions for learning’. Upper/middle class families, relying on ‘independent’ self-construal, are more inclined to seek individualized feedback, and therefore tend to participate to a greater extent in exchanges with teachers. Such cultural differences are important because they can also contribute to the difficulties encountered by working-class families.

The structural divide: unequal support from schools

The issues reviewed thus far all increase the vulnerability of children and students from underprivileged backgrounds when schools are closed. To offset these disadvantages, it might be expected that the school should increase its support by providing additional resources for working-class students. However, recent data suggest that differences in the material and human resources invested in providing educational support for children during periods of school closure were—paradoxically—in favour of upper/middle-class students (Fig. 1 ). In England, for example, upper/middle-class parents reported benefiting from online classes and video-conferencing with teachers more often than working-class parents 10 . Furthermore, active help from school (for example, online teaching, private tutoring or chats with teachers) occurred more frequently in the richest households (64% of the richest households declared having received help from school) than in the poorest households (47%). Another survey found that in the United Kingdom, upper/middle-class children were more likely to take online lessons every day (30%) than working-class students (16%) 12 . This substantial difference might be due, at least in part, to the fact that private schools are better equipped in terms of online platforms (60% of schools have at least one online platform) than state schools (37%, and 23% in the most deprived schools) and were more likely to organize daily online lessons. Similarly, in the United Kingdom, in schools with a high proportion of students eligible for free school meals, teachers were less inclined to broadcast an online lesson for their pupils 78 . Interestingly, 58% of teachers in the wealthiest areas reported having messaged their students or their students’ parents during lockdown compared with 47% in the most deprived schools. In addition, the probability of children receiving technical support from the school (for example, by providing pupils with laptops or other devices) is, surprisingly, higher in the most advantaged schools than in the most deprived 78 .

In addition to social class disparities, there has been less support from schools for African-American and Latinx students. During school closures in the United States, 40% of African-American students and 30% of Latinx students received no online teaching compared with 10% of white students 79 . Another source of inequality is that the probability of school closure was correlated with social class and race. In the United States, for example, school closures from September to December 2020 were more common in schools with a high proportion of racial/ethnic minority students, who experience homelessness and are eligible for free/discounted school meals 80 .

Similarly, access to educational resources and support was lower in poorer (compared with richer) countries 81 . In sub-Saharan Africa, during lockdown, 45% of children had no exposure at all to any type of remote learning. Of those who did, the medium was mostly radio, television or paper rather than digital. In African countries, at most 10% of children received some material through the Internet. In Latin America, 90% of children received some remote learning, but less than half of that was through the internet—the remainder being via radio and television 81 . In Ecuador, high-school students from the lowest wealth quartile had fewer remote-learning opportunities, such as Google class/Zoom, than students from the highest wealth quartile 31 .

Thus, the achievement gap and its accentuation during lockdown are due not only to the cultural and digital disadvantages of working-class families but also to unequal support from schools. This inequality in school support is not due to teachers being indifferent to or even supportive of social stratification. Rather, we believe that these effects are fundamentally structural. In many countries, schools located in upper/middle-class neighbourhoods have more money than those in the poorest neighbourhoods. Moreover, upper/middle-class parents invest more in the schools of their children than working-class parents (for example, see ref. 82 ), and schools have an interest in catering more for upper/middle-class families than for working-class families 83 . Additionally, the expectation of teachers may be lower for working-class children 84 . For example, they tend to estimate that working-class students invest less effort in learning than their upper/middle-class counterparts 85 . These differences in perception may have influenced the behaviour of teachers during school closure, such that teachers in privileged neighbourhoods provided more information to students because they expected more from them in term of effort and achievement. The fact that upper/middle-class parents are better able than working-class parents to comply with the expectations of teachers (for examples, see refs. 55 , 86 ) may have reinforced this phenomenon. These discrepancies echo data showing that working-class students tend to request less help in their schoolwork than upper/middle-class ones 87 , and they may even avoid asking for help because they believe that such requests could lead to reprimands 88 . During school closures, these students (and their families) may in consequence have been less likely to ask for help and resources. Jointly, these phenomena have resulted in upper/middle-class families receiving more support from schools during lockdown than their working-class counterparts.

Psychological effects of digital, cultural and structural divides

Despite being strongly influenced by social class, differences in academic achievement are often interpreted by parents, teachers and students as reflecting differences in ability 89 . As a result, upper/middle-class students are usually perceived—and perceive themselves—as smarter than working-class students, who are perceived—and perceive themselves—as less intelligent 90 , 91 , 92 or less able to succeed 93 . Working-class students also worry more about the fact that they might perform more poorly than upper/middle-class students 94 , 95 . These fears influence academic learning in important ways. In particular, they can consume cognitive resources when children and students work on academic tasks 96 , 97 . Self-efficacy also plays a key role in engaging in learning and perseverance in the face of difficulties 13 , 98 . In addition, working-class students are those for whom the fear of being outperformed by others is the most negatively related to academic performance 99 .

The fact that working-class children and students are less familiar with the tasks set by teachers, and less well equipped and supported, makes them more likely to experience feelings of incompetence (Fig. 1 ). Working-class parents are also more likely than their upper/middle-class counterparts to feel unable to help their children with schoolwork. Consistent with this, research has shown that both working-class students and parents have lower feelings of academic self-efficacy than their upper/middle-class counterparts 100 , 101 . These differences have been documented under ‘normal’ conditions but are likely to be exacerbated during distance learning. Recent surveys conducted during the school closures have confirmed that upper/middle-class families felt better able to support their children in distance learning than did working-class families 10 and that upper/middle-class parents helped their children more and felt more capable to do so 11 , 12 .

Pandemic disparity, future directions and recommendations

The research reviewed thus far suggests that children and their families are highly unequal with respect to digital access, skills and use. It also shows that upper/middle-class students are more likely to be supported in their homework (by their parents and teachers) than working-class students, and that upper/middle-class students and parents will probably feel better able than working-class ones to adapt to the context of distance learning. For all these reasons, we anticipate that as a result of school closures, the COVID-19 pandemic will substantially increase the social class achievement gap. Because school closures are a recent occurrence, it is too early to measure with precision their effects on the widening of the achievement gap. However, some recent data are consistent with this idea.

Evidence for a widening gap during the pandemic

Comparing academic achievement in 2020 with previous years provides an early indication of the effects of school closures during the pandemic. In France, for example, first and second graders take national evaluations at the beginning of the school year. Initial comparisons of the results for 2020 with those from previous years revealed that the gap between schools classified as ‘priority schools’ (those in low-income urban areas) and schools in higher-income neighbourhoods—a gap observed every year—was particularly pronounced in 2020 in both French and mathematics 102 .

Similarly, in the Netherlands, national assessments take place twice a year. In 2020, they took place both before and after school closures. A recent analysis compared progress during this period in 2020 in mathematics/arithmetic, spelling and reading comprehension for 7–11-year-old students within the same period in the three previous years 103 . Results indicated a general learning loss in 2020. More importantly, for the 8% of working-class children, the losses were 40% greater than they were for upper/middle-class children.

Similar results were observed in Belgium among students attending the final year of primary school. Compared with students from previous cohorts, students affected by school closures experienced a substantial decrease in their mathematics and language scores, with children from more disadvantaged backgrounds experiencing greater learning losses 104 . Likewise, oral reading assessments in more than 100 school districts in the United States showed that the development of this skill among children in second and third grade significantly slowed between Spring and Autumn 2020, but this slowdown was more pronounced in schools from lower-achieving districts 105 .

It is likely that school closures have also amplified racial disparities in learning and achievement. For example, in the United States, after the first lockdown, students of colour lost the equivalent of 3–5 months of learning, whereas white students were about 1–3 months behind. Moreover, in the Autumn, when some students started to return to classrooms, African-American and Latinx students were more likely to continue distance learning, despite being less likely to have access to the digital tools, Internet access and live contact with teachers 106 .

In some African countries (for example, Ethiopia, Kenya, Liberia, Tanzania and Uganda), the COVID-19 crisis has resulted in learning loss ranging from 6 months to more 1 year 107 , and this learning loss appears to be greater for working-class children (that is, those attending no-fee schools) than for upper/middle-class children 108 .

These findings show that school closures have exacerbated achievement gaps linked to social class and ethnicity. However, more research is needed to address the question of whether school closures differentially affect the learning of students from working- and upper/middle-class families.

Future directions

First, to assess the specific and unique impact of school closures on student learning, longitudinal research should compare student achievement at different times of the year, before, during and after school closures, as has been done to document the summer learning loss 66 , 109 . In the coming months, alternating periods of school closure and opening may occur, thereby presenting opportunities to do such research. This would also make it possible to examine whether the gap diminishes a few weeks after children return to in-school learning or whether, conversely, it increases with time because the foundations have not been sufficiently acquired to facilitate further learning 110 .

Second, the mechanisms underlying the increase in social class disparities during school closures should be examined. As discussed above, school closures result in situations for which students are unevenly prepared and supported. It would be appropriate to seek to quantify the contribution of each of the factors that might be responsible for accentuating the social class achievement gap. In particular, distinguishing between factors that are relatively ‘controllable’ (for example, resources made available to pupils) and those that are more difficult to control (for example, the self-efficacy of parents in supporting the schoolwork of their children) is essential to inform public policy and teaching practices.

Third, existing studies are based on general comparisons and very few provide insights into the actual practices that took place in families during school closure and how these practices affected the achievement gap. For example, research has documented that parents from working-class backgrounds are likely to find it more difficult to help their children to complete homework and to provide constructive feedback 63 , 111 , something that could in turn have a negative impact on the continuity of learning of their children. In addition, it seems reasonable to assume that during lockdown, parents from upper/middle-class backgrounds encouraged their children to engage in practices that, even if not explicitly requested by teachers, would be beneficial to learning (for example, creative activities or reading). Identifying the practices that best predict the maintenance or decline of educational achievement during school closures would help identify levers for intervention.

Finally, it would be interesting to investigate teaching practices during school closures. The lockdown in the spring of 2020 was sudden and unexpected. Within a few days, teachers had to find a way to compensate for the school closure, which led to highly variable practices. Some teachers posted schoolwork on platforms, others sent it by email, some set work on a weekly basis while others set it day by day. Some teachers also set up live sessions in large or small groups, providing remote meetings for questions and support. There have also been variations in the type of feedback given to students, notably through the monitoring and correcting of work. Future studies should examine in more detail what practices schools and teachers used to compensate for the school closures and their effects on widening, maintaining or even reducing the gap, as has been done for certain specific literacy programmes 112 as well as specific instruction topics (for example, ecology and evolution 113 ).

Practical recommendations

We are aware of the debate about whether social science research on COVID-19 is suitable for making policy decisions 114 , and we draw attention to the fact that some of our recommendations (Table 1 ) are based on evidence from experiments or interventions carried out pre-COVID while others are more speculative. In any case, we emphasize that these suggestions should be viewed with caution and be tested in future research. Some of our recommendations could be implemented in the event of new school closures, others only when schools re-open. We also acknowledge that while these recommendations are intended for parents and teachers, their implementation largely depends on the adoption of structural policies. Importantly, given all the issues discussed above, we emphasize the importance of prioritizing, wherever possible, in-person learning over remote learning 115 and where this is not possible, of implementing strong policies to support distance learning, especially for disadvantaged families.

Where face-to face teaching is not possible and teachers are responsible for implementing distance learning, it will be important to make them aware of the factors that can exacerbate inequalities during lockdown and to provide them with guidance about practices that would reduce these inequalities. Thus, there is an urgent need for interventions aimed at making teachers aware of the impact of the social class of children and families on the following factors: (1) access to, familiarity with and use of digital devices; (2) familiarity with academic knowledge and skills; and (3) preparedness to work autonomously. Increasing awareness of the material, cultural and psychological barriers that working-class children and families face during lockdown should increase the quality and quantity of the support provided by teachers and thereby positively affect the achievements of working-class students.

In addition to increasing the awareness of teachers of these barriers, teachers should be encouraged to adjust the way they communicate with working-class families due to differences in self-construal compared with upper/middle-class families 77 . For example, questions about family (rather than personal) well-being would be congruent with interdependent self-construals. This should contribute to better communication and help keep a better track of the progress of students during distance learning.

It is also necessary to help teachers to engage in practices that have a chance of reducing inequalities 53 , 116 . Particularly important is that teachers and schools ensure that homework can be done by all children, for example, by setting up organizations that would help children whose parents are not in a position to monitor or assist with the homework of their children. Options include homework help groups and tutoring by teachers after class. When schools are open, the growing tendency to set homework through digital media should be resisted as far as possible given the evidence we have reviewed above. Moreover, previous research has underscored the importance of homework feedback provided by teachers, which is positively related to the amount of homework completed and predictive of academic performance 117 . Where homework is web-based, it has also been shown that feedback on web-based homework enhances the learning of students 118 . It therefore seems reasonable to predict that the social class achievement gap will increase more slowly (or even remain constant or be reversed) in schools that establish individualized monitoring of students, by means of regular calls and feedback on homework, compared with schools where the support provided to pupils is more generic.

Given that learning during lockdown has increasingly taken place in family settings, we believe that interventions involving the family are also likely to be effective 119 , 120 , 121 . Simply providing families with suitable material equipment may be insufficient. Families should be given training in the efficient use of digital technology and pedagogical support. This would increase the self-efficacy of parents and students, with positive consequences for achievement. Ideally, such training would be delivered in person to avoid problems arising from the digital divide. Where this is not possible, individualized online tutoring should be provided. For example, studies conducted during the lockdown in Botswana and Italy have shown that individual online tutoring directly targeting either parents or students in middle school has a positive impact on the achievement of students, particularly for working-class students 122 , 123 .

Interventions targeting families should also address the psychological barriers faced by working-class families and children. Some interventions have already been designed and been shown to be effective in reducing the social class achievement gap, particularly in mathematics and language 124 , 125 , 126 . For example, research showed that an intervention designed to train low-income parents in how to support the mathematical development of their pre-kindergarten children (including classes and access to a library of kits to use at home) increased the quality of support provided by the parents, with a corresponding impact on the development of mathematical knowledge of their children. Such interventions should be particularly beneficial in the context of school closure.

Beyond its impact on academic performance and inequalities, the COVID-19 crisis has shaken the economies of countries around the world, casting millions of families around the world into poverty 127 , 128 , 129 . As noted earlier, there has been a marked increase in economic inequalities, bringing with it all the psychological and social problems that such inequalities create 130 , 131 , especially for people who live in scarcity 132 . The increase in educational inequalities is just one facet of the many difficulties that working-class families will encounter in the coming years, but it is one that could seriously limit the chances of their children escaping from poverty by reducing their opportunities for upward mobility. In this context, it should be a priority to concentrate resources on the most deprived students. A large proportion of the poorest households do not own a computer and do not have personal access to the Internet, which has important consequences for distance learning. During school closures, it is therefore imperative to provide such families with adequate equipment and Internet service, as was done in some countries in spring 2020. Even if the provision of such equipment is not in itself sufficient, it is a necessary condition for ensuring pedagogical continuity during lockdown.

Finally, after prolonged periods of school closure, many students may not have acquired the skills needed to pursue their education. A possible consequence would be an increase in the number of students for whom teachers recommend class repetitions. Class repetitions are contentious. On the one hand, class repetition more frequently affects working-class children and is not efficient in terms of learning improvement 133 . On the other hand, accepting lower standards of academic achievement or even suspending the practice of repeating a class could lead to pupils pursuing their education without mastering the key abilities needed at higher grades. This could create difficulties in subsequent years and, in this sense, be counterproductive. We therefore believe that the most appropriate way to limit the damage of the pandemic would be to help children catch up rather than allowing them to continue without mastering the necessary skills. As is being done in some countries, systematic remedial courses (for example, summer learning programmes) should be organized and financially supported following periods of school closure, with priority given to pupils from working-class families. Such interventions have genuine potential in that research has shown that participation in remedial summer programmes is effective in reducing learning loss during the summer break 134 , 135 , 136 . For example, in one study 137 , 438 students from high-poverty schools were offered a multiyear summer school programme that included various pedagogical and enrichment activities (for example, science investigation and music) and were compared with a ‘no-treatment’ control group. Students who participated in the summer programme progressed more than students in the control group. A meta-analysis 138 of 41 summer learning programmes (that is, classroom- and home-based summer interventions) involving children from kindergarten to grade 8 showed that these programmes had significantly larger benefits for children from working-class families. Although such measures are costly, the cost is small compared to the price of failing to fulfil the academic potential of many students simply because they were not born into upper/middle-class families.

The unprecedented nature of the current pandemic means that we lack strong data on what the school closure period is likely to produce in terms of learning deficits and the reproduction of social inequalities. However, the research discussed in this article suggests that there are good reasons to predict that this period of school closures will accelerate the reproduction of social inequalities in educational achievement.

By making school learning less dependent on teachers and more dependent on families and digital tools and resources, school closures are likely to greatly amplify social class inequalities. At a time when many countries are experiencing second, third or fourth waves of the pandemic, resulting in fresh periods of local or general lockdowns, systematic efforts to test these predictions are urgently needed along with steps to reduce the impact of school closures on the social class achievement gap.

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We thank G. Reis for editing the figure. The writing of this manuscript was supported by grant ANR-19-CE28-0007–PRESCHOOL from the French National Research Agency (S.G.).

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Goudeau, S., Sanrey, C., Stanczak, A. et al. Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap. Nat Hum Behav 5 , 1273–1281 (2021). https://doi.org/10.1038/s41562-021-01212-7

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Argumentative Essay: Online Learning and Educational Access

Conventional learning is evolving with the help of computers and online technology. New ways of learning are now available, and improved access is one of the most important benefits available. People all around the world are experiencing improved mobility as a result of the freedom and potential that online learning provides, and as academic institutions and learning organisations adopt online learning technologies and remote-access learning, formal academic education is becoming increasingly legitimate. This essay argues the contemporary benefits of online learning, and that these benefits significantly outweigh the issues, challenges and disadvantages of online learning.

Online learning is giving people new choices and newfound flexibility with their personal learning and development. Whereas before, formal academic qualifications could only be gained by participating in a full time course on site, the internet has allowed institutions to expand their reach and offer recognized courses on a contact-partial, or totally virtual, basis. Institutions can do so with relatively few extra resources, and for paid courses this constitutes excellent value, and the student benefits with greater educational access and greater flexibility to learn and get qualified even when there lots of other personal commitments to deal with.

Flexibility is certainly one of the most important benefits, but just as important is educational access. On top of the internet’s widespread presence in developed countries, the internet is becoming increasingly available in newly developed and developing countries. Even without considering the general informational exposure that the internet delivers, online academic courses and learning initiatives are becoming more aware of the needs of people from disadvantaged backgrounds, and this means that people from such backgrounds are in a much better position to learn and progress than they used to be.

The biggest argument that raises doubt over online learning is the quality of online courses in comparison to conventional courses. Are such online courses good enough for employers to take notice? The second biggest argument is the current reality that faces many people from disadvantaged backgrounds, despite the improvements made in this area in recent years – they do not have the level of basic access needed to benefit from online learning. In fact, there are numerous sources of evidence that claim disadvantaged students are not receiving anywhere near the sort of benefits that online learning institutions and promoters are trying to instigate. Currently there are many organisations, campaigns and initiatives that are working to expand access to higher education. With such high participation, it can be argued that it is only a matter of time before the benefits are truly realised, but what about the global online infrastructure?

There is another argument that is very difficult to dispel, and that is the response of different types of students to the online learning paradigm. Evidence shows that there are certain groups of students that benefit from college distance learning much more than other groups. In essence, students must be highly motivated and highly disciplined if they are to learn effectively in their own private environment.

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Classroom Q&A

With larry ferlazzo.

In this EdWeek blog, an experiment in knowledge-gathering, Ferlazzo will address readers’ questions on classroom management, ELL instruction, lesson planning, and other issues facing teachers. Send your questions to [email protected]. Read more from this blog.

Students Reflect on Their Distance Learning Experiences

• Share article

(This is the second post in a multipart series. You can see Part One here .)

Here is the new question-of-the-week:

What has your online learning experience been as a student? What did you like about it? What didn’t you like about it? How does it compare with your experience as a student in a physical classroom? In the future, if you could choose, would you want to do more online learning? If so, why? If not, why not?

In Part One , five students from the high school where I teach in Sacramento, Calif., shared their reflections.

Today, the first three contributions come from students in Austin Green’s 1st grade class in Utah.

All other student commentators today work with Robert G Taylor, Ed.S., and Jon Harding at the Kansas State School for the Blind.

“I miss my teacher!”

Tristan Fitzgerald is a 1st grade student at Fremont Elementary School, age 6:

I’m doing good at online learning. I miss my friends. I spend lots of time at home. My sister distracts me!!! I miss my teacher! I’m doing the same things. It is harder because of my sister. I want to learn in the classroom because I would miss my teacher.

“School is over fast”

Sydni Buckner is a 1st grade student at Fremont Elementary School, age 7:

What I like about remote learning. First, school is over fast. Next, there’s no waiting on students and it’s quiet. Last, I like to use the computer. I like remote learning.

Carsen Gordon is a 1st grade student at Fremont Elementary School, age 7:

I have liked doing math with remote learning. I have also liked that it is shorter time than at school. My teacher is doing Zoom meetings to teach us. He has made it easy to understand the work I need to do. I would like to do more online learning.

“Learn at my own pace”

Jay Walker is a junior at Smithville High School in Smithville, Mo.:

At first, it was incredibly difficult transitioning from traditional high school to online learning, but gradually as the months go by I am slowly starting to get used to it. Though I feel like I’m not getting a good amount of social interaction from my peers, I find online learning to be much more beneficial for me as a student. Not being in the pressurized environment of a classroom gives me the opportunity to learn at my own pace, whether that be faster or slower than the original classroom, and if clarification is needed, I can simply rewind the lecture videos my teachers are putting out, or send an email to my teacher quickly and efficiently.

Being visually impaired in a high school Is challenging, next to navigating the hallways and putting a lot of trust into my technology for it to work properly that day, I feel as though my anxiety has dropped tremendously while being home, because if something were to go wrong with my tech, I can simply pause what I’m doing and fix it, and not have to worry about missing something or slowing the others down.

I would love to have online learning integrated into the natural high school environment, seeing as I am getting so much more done in such a shorter time, and I feel like I’m actually learning the material and not just grazing over it like I would in a standard classroom.

“I can more easily express what accommodations I need”

Rich Yamamoto is a junior at the Kansas State School for the Blind in Kansas City:

As a sophomore, I did a couple of online classes through my public school in Andover, while attending the Kansas State School for the Blind (KSSB). Those experiences compared to this year were less than pleasant, simply because at the time, I didn’t know what I was doing, and I tended to overwork myself silly. This year, we’re doing all of our classes via Zoom, and that’s greatly impacted my views on online learning. I’m always in constant verbal communication with my teachers, I can more easily express what accommodations I need, and I can get to know my teacher a lot better than if we were just communicating over a comment thread in Google Classroom or email. It’s much more relaxed now, and I must say, it’s rather enjoyable.

I don’t know if I would want to have more online learning in the future because if I’m being truly honest, I like the look and feel of a regular classroom sometimes. However, that doesn’t mean that I would be opposed to doing assignments online; I just want the instruction to be in a classroom, because it’s nice to know that you’re truly not the only one who may be lost. Unfortunately, because of the time we are living in right now, online learning is becoming more of a necessity if we want to keep on learning the skills that we learn in the classroom, and something tells me that due to updates in technology, online learning is going to be a lot more prevalent even after this pandemic is over.

Oral commentaries from a podcast

Patrick Wilson Jr., Mara Hug, and Rich Yamamoto (the same student who wrote the preceding contribution) are hosts of Discover Podcasting at the Kansas State School for the Blind.

Rich (Junior) is an all-around student, participating in sports; forensics; and is popular with others students and adults for his willingness to help others.

Patrick (Freshman) loves being creative and trying anything new in technology. He loves talking about technology and amazes us on the topics he is familiar with.

Mara (Freshman) loves to read and spend time with her friends. She has used a variety of technologies and loves to apply them with everyday challenges.

Here is their podcast titled “Distance Learning Reflections From the Students’ Point Of View” :

Thanks to Tristan, Sydni, Carsen, Jay, Rich, Patrick, and Mara for their contributions!

Please feel free to leave a comment with your reactions to the topic or directly to anything that has been said in this post.

Consider contributing a question to be answered in a future post. You can send one to me at [email protected] . When you send it in, let me know if I can use your real name if it’s selected or if you’d prefer remaining anonymous and have a pseudonym in mind.

You can also contact me on Twitter at @Larryferlazzo .

Education Week has published a collection of posts from this blog, along with new material, in an e-book form. It’s titled Classroom Management Q&As: Expert Strategies for Teaching .

Just a reminder, you can subscribe and receive updates from this blog via email or RSS Reader. And if you missed any of the highlights from the first eight years of this blog, you can see a categorized list below. The list doesn’t include ones from this current year, but you can find those by clicking on the “answers” category found in the sidebar.

This Year’s Most Popular Q&A Posts

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The purpose of this study is to analyze the effect of online education, which has been extensively used on student achievement since the beginning of the pandemic. In line with this purpose, a meta-analysis of the related studies focusing on the effect of online education on students’ academic achievement in several countries between the years 2010 and 2021 was carried out. Furthermore, this study will provide a source to assist future studies with comparing the effect of online education on academic achievement before and after the pandemic. This meta-analysis study consists of 27 studies in total. The meta-analysis involves the studies conducted in the USA, Taiwan, Turkey, China, Philippines, Ireland, and Georgia. The studies included in the meta-analysis are experimental studies, and the total sample size is 1772. In the study, the funnel plot, Duval and Tweedie’s Trip and Fill Analysis, Orwin’s Safe N Analysis, and Egger’s Regression Test were utilized to determine the publication bias, which has been found to be quite low. Besides, Hedge’s g statistic was employed to measure the effect size for the difference between the means performed in accordance with the random effects model. The results of the study show that the effect size of online education on academic achievement is on a medium level. The heterogeneity test results of the meta-analysis study display that the effect size does not differ in terms of class level, country, online education approaches, and lecture moderators.

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1 Introduction

Information and communication technologies have become a powerful force in transforming the educational settings around the world. The pandemic has been an important factor in transferring traditional physical classrooms settings through adopting information and communication technologies and has also accelerated the transformation. The literature supports that learning environments connected to information and communication technologies highly satisfy students. Therefore, we need to keep interest in technology-based learning environments. Clearly, technology has had a huge impact on young people's online lives. This digital revolution can synergize the educational ambitions and interests of digitally addicted students. In essence, COVID-19 has provided us with an opportunity to embrace online learning as education systems have to keep up with the rapid emergence of new technologies.

Information and communication technologies that have an effect on all spheres of life are also actively included in the education field. With the recent developments, using technology in education has become inevitable due to personal and social reasons (Usta, 2011a ). Online education may be given as an example of using information and communication technologies as a consequence of the technological developments. Also, it is crystal clear that online learning is a popular way of obtaining instruction (Demiralay et al., 2016 ; Pillay et al., 2007 ), which is defined by Horton ( 2000 ) as a way of education that is performed through a web browser or an online application without requiring an extra software or a learning source. Furthermore, online learning is described as a way of utilizing the internet to obtain the related learning sources during the learning process, to interact with the content, the teacher, and other learners, as well as to get support throughout the learning process (Ally, 2004 ). Online learning has such benefits as learning independently at any time and place (Vrasidas & MsIsaac, 2000 ), granting facility (Poole, 2000 ), flexibility (Chizmar & Walbert, 1999 ), self-regulation skills (Usta, 2011b ), learning with collaboration, and opportunity to plan self-learning process.

Even though online education practices have not been comprehensive as it is now, internet and computers have been used in education as alternative learning tools in correlation with the advances in technology. The first distance education attempt in the world was initiated by the ‘Steno Courses’ announcement published in Boston newspaper in 1728. Furthermore, in the nineteenth century, Sweden University started the “Correspondence Composition Courses” for women, and University Correspondence College was afterwards founded for the correspondence courses in 1843 (Arat & Bakan, 2011 ). Recently, distance education has been performed through computers, assisted by the facilities of the internet technologies, and soon, it has evolved into a mobile education practice that is emanating from progress in the speed of internet connection, and the development of mobile devices.

With the emergence of pandemic (Covid-19), face to face education has almost been put to a halt, and online education has gained significant importance. The Microsoft management team declared to have 750 users involved in the online education activities on the 10 th March, just before the pandemic; however, on March 24, they informed that the number of users increased significantly, reaching the number of 138,698 users (OECD, 2020 ). This event supports the view that it is better to commonly use online education rather than using it as a traditional alternative educational tool when students do not have the opportunity to have a face to face education (Geostat, 2019 ). The period of Covid-19 pandemic has emerged as a sudden state of having limited opportunities. Face to face education has stopped in this period for a long time. The global spread of Covid-19 affected more than 850 million students all around the world, and it caused the suspension of face to face education. Different countries have proposed several solutions in order to maintain the education process during the pandemic. Schools have had to change their curriculum, and many countries supported the online education practices soon after the pandemic. In other words, traditional education gave its way to online education practices. At least 96 countries have been motivated to access online libraries, TV broadcasts, instructions, sources, video lectures, and online channels (UNESCO, 2020 ). In such a painful period, educational institutions went through online education practices by the help of huge companies such as Microsoft, Google, Zoom, Skype, FaceTime, and Slack. Thus, online education has been discussed in the education agenda more intensively than ever before.

Although online education approaches were not used as comprehensively as it has been used recently, it was utilized as an alternative learning approach in education for a long time in parallel with the development of technology, internet and computers. The academic achievement of the students is often aimed to be promoted by employing online education approaches. In this regard, academicians in various countries have conducted many studies on the evaluation of online education approaches and published the related results. However, the accumulation of scientific data on online education approaches creates difficulties in keeping, organizing and synthesizing the findings. In this research area, studies are being conducted at an increasing rate making it difficult for scientists to be aware of all the research outside of their ​​expertise. Another problem encountered in the related study area is that online education studies are repetitive. Studies often utilize slightly different methods, measures, and/or examples to avoid duplication. This erroneous approach makes it difficult to distinguish between significant differences in the related results. In other words, if there are significant differences in the results of the studies, it may be difficult to express what variety explains the differences in these results. One obvious solution to these problems is to systematically review the results of various studies and uncover the sources. One method of performing such systematic syntheses is the application of meta-analysis which is a methodological and statistical approach to draw conclusions from the literature. At this point, how effective online education applications are in increasing the academic success is an important detail. Has online education, which is likely to be encountered frequently in the continuing pandemic period, been successful in the last ten years? If successful, how much was the impact? Did different variables have an impact on this effect? Academics across the globe have carried out studies on the evaluation of online education platforms and publishing the related results (Chiao et al., 2018 ). It is quite important to evaluate the results of the studies that have been published up until now, and that will be published in the future. Has the online education been successful? If it has been, how big is the impact? Do the different variables affect this impact? What should we consider in the next coming online education practices? These questions have all motivated us to carry out this study. We have conducted a comprehensive meta-analysis study that tries to provide a discussion platform on how to develop efficient online programs for educators and policy makers by reviewing the related studies on online education, presenting the effect size, and revealing the effect of diverse variables on the general impact.

There have been many critical discussions and comprehensive studies on the differences between online and face to face learning; however, the focus of this paper is different in the sense that it clarifies the magnitude of the effect of online education and teaching process, and it represents what factors should be controlled to help increase the effect size. Indeed, the purpose here is to provide conscious decisions in the implementation of the online education process.

The general impact of online education on the academic achievement will be discovered in the study. Therefore, this will provide an opportunity to get a general overview of the online education which has been practiced and discussed intensively in the pandemic period. Moreover, the general impact of online education on academic achievement will be analyzed, considering different variables. In other words, the current study will allow to totally evaluate the study results from the related literature, and to analyze the results considering several cultures, lectures, and class levels. Considering all the related points, this study seeks to answer the following research questions:

What is the effect size of online education on academic achievement?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the country?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the class level?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the lecture?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the online education approaches?

This study aims at determining the effect size of online education, which has been highly used since the beginning of the pandemic, on students’ academic achievement in different courses by using a meta-analysis method. Meta-analysis is a synthesis method that enables gathering of several study results accurately and efficiently, and getting the total results in the end (Tsagris & Fragkos, 2018 ).

2.1 Selecting and coding the data (studies)

The required literature for the meta-analysis study was reviewed in July, 2020, and the follow-up review was conducted in September, 2020. The purpose of the follow-up review was to include the studies which were published in the conduction period of this study, and which met the related inclusion criteria. However, no study was encountered to be included in the follow-up review.

In order to access the studies in the meta-analysis, the databases of Web of Science, ERIC, and SCOPUS were reviewed by utilizing the keywords ‘online learning and online education’. Not every database has a search engine that grants access to the studies by writing the keywords, and this obstacle was considered to be an important problem to be overcome. Therefore, a platform that has a special design was utilized by the researcher. With this purpose, through the open access system of Cukurova University Library, detailed reviews were practiced using EBSCO Information Services (EBSCO) that allow reviewing the whole collection of research through a sole searching box. Since the fundamental variables of this study are online education and online learning, the literature was systematically reviewed in the related databases (Web of Science, ERIC, and SCOPUS) by referring to the keywords. Within this scope, 225 articles were accessed, and the studies were included in the coding key list formed by the researcher. The name of the researchers, the year, the database (Web of Science, ERIC, and SCOPUS), the sample group and size, the lectures that the academic achievement was tested in, the country that the study was conducted in, and the class levels were all included in this coding key.

The following criteria were identified to include 225 research studies which were coded based on the theoretical basis of the meta-analysis study: (1) The studies should be published in the refereed journals between the years 2020 and 2021, (2) The studies should be experimental studies that try to determine the effect of online education and online learning on academic achievement, (3) The values of the stated variables or the required statistics to calculate these values should be stated in the results of the studies, and (4) The sample group of the study should be at a primary education level. These criteria were also used as the exclusion criteria in the sense that the studies that do not meet the required criteria were not included in the present study.

After the inclusion criteria were determined, a systematic review process was conducted, following the year criterion of the study by means of EBSCO. Within this scope, 290,365 studies that analyze the effect of online education and online learning on academic achievement were accordingly accessed. The database (Web of Science, ERIC, and SCOPUS) was also used as a filter by analyzing the inclusion criteria. Hence, the number of the studies that were analyzed was 58,616. Afterwards, the keyword ‘primary education’ was used as the filter and the number of studies included in the study decreased to 3152. Lastly, the literature was reviewed by using the keyword ‘academic achievement’ and 225 studies were accessed. All the information of 225 articles was included in the coding key.

It is necessary for the coders to review the related studies accurately and control the validity, safety, and accuracy of the studies (Stewart & Kamins, 2001 ). Within this scope, the studies that were determined based on the variables used in this study were first reviewed by three researchers from primary education field, then the accessed studies were combined and processed in the coding key by the researcher. All these studies that were processed in the coding key were analyzed in accordance with the inclusion criteria by all the researchers in the meetings, and it was decided that 27 studies met the inclusion criteria (Atici & Polat, 2010 ; Carreon, 2018 ; Ceylan & Elitok Kesici, 2017 ; Chae & Shin, 2016 ; Chiang et al. 2014 ; Ercan, 2014 ; Ercan et al., 2016 ; Gwo-Jen et al., 2018 ; Hayes & Stewart, 2016 ; Hwang et al., 2012 ; Kert et al., 2017 ; Lai & Chen, 2010 ; Lai et al., 2015 ; Meyers et al., 2015 ; Ravenel et al., 2014 ; Sung et al., 2016 ; Wang & Chen, 2013 ; Yu, 2019 ; Yu & Chen, 2014 ; Yu & Pan, 2014 ; Yu et al., 2010 ; Zhong et al., 2017 ). The data from the studies meeting the inclusion criteria were independently processed in the second coding key by three researchers, and consensus meetings were arranged for further discussion. After the meetings, researchers came to an agreement that the data were coded accurately and precisely. Having identified the effect sizes and heterogeneity of the study, moderator variables that will show the differences between the effect sizes were determined. The data related to the determined moderator variables were added to the coding key by three researchers, and a new consensus meeting was arranged. After the meeting, researchers came to an agreement that moderator variables were coded accurately and precisely.

2.2 Study group

27 studies are included in the meta-analysis. The total sample size of the studies that are included in the analysis is 1772. The characteristics of the studies included are given in Table 1 .

2.3 Publication bias

Publication bias is the low capability of published studies on a research subject to represent all completed studies on the same subject (Card, 2011 ; Littell et al., 2008 ). Similarly, publication bias is the state of having a relationship between the probability of the publication of a study on a subject, and the effect size and significance that it produces. Within this scope, publication bias may occur when the researchers do not want to publish the study as a result of failing to obtain the expected results, or not being approved by the scientific journals, and consequently not being included in the study synthesis (Makowski et al., 2019 ). The high possibility of publication bias in a meta-analysis study negatively affects (Pecoraro, 2018 ) the accuracy of the combined effect size, causing the average effect size to be reported differently than it should be (Borenstein et al., 2009 ). For this reason, the possibility of publication bias in the included studies was tested before determining the effect sizes of the relationships between the stated variables. The possibility of publication bias of this meta-analysis study was analyzed by using the funnel plot, Orwin’s Safe N Analysis, Duval and Tweedie’s Trip and Fill Analysis, and Egger’s Regression Test.

2.4 Selecting the model

After determining the probability of publication bias of this meta-analysis study, the statistical model used to calculate the effect sizes was selected. The main approaches used in the effect size calculations according to the differentiation level of inter-study variance are fixed and random effects models (Pigott, 2012 ). Fixed effects model refers to the homogeneity of the characteristics of combined studies apart from the sample sizes, while random effects model refers to the parameter diversity between the studies (Cumming, 2012 ). While calculating the average effect size in the random effects model (Deeks et al., 2008 ) that is based on the assumption that effect predictions of different studies are only the result of a similar distribution, it is necessary to consider several situations such as the effect size apart from the sample error of combined studies, characteristics of the participants, duration, scope, and pattern of the study (Littell et al., 2008 ). While deciding the model in the meta-analysis study, the assumptions on the sample characteristics of the studies included in the analysis and the inferences that the researcher aims to make should be taken into consideration. The fact that the sample characteristics of the studies conducted in the field of social sciences are affected by various parameters shows that using random effects model is more appropriate in this sense. Besides, it is stated that the inferences made with the random effects model are beyond the studies included in the meta-analysis (Field, 2003 ; Field & Gillett, 2010 ). Therefore, using random effects model also contributes to the generalization of research data. The specified criteria for the statistical model selection show that according to the nature of the meta-analysis study, the model should be selected just before the analysis (Borenstein et al., 2007 ; Littell et al., 2008 ). Within this framework, it was decided to make use of the random effects model, considering that the students who are the samples of the studies included in the meta-analysis are from different countries and cultures, the sample characteristics of the studies differ, and the patterns and scopes of the studies vary as well.

2.5 Heterogeneity

Meta-analysis facilitates analyzing the research subject with different parameters by showing the level of diversity between the included studies. Within this frame, whether there is a heterogeneous distribution between the studies included in the study or not has been evaluated in the present study. The heterogeneity of the studies combined in this meta-analysis study has been determined through Q and I 2 tests. Q test evaluates the random distribution probability of the differences between the observed results (Deeks et al., 2008 ). Q value exceeding 2 value calculated according to the degree of freedom and significance, indicates the heterogeneity of the combined effect sizes (Card, 2011 ). I 2 test, which is the complementary of the Q test, shows the heterogeneity amount of the effect sizes (Cleophas & Zwinderman, 2017 ). I 2 value being higher than 75% is explained as high level of heterogeneity.

In case of encountering heterogeneity in the studies included in the meta-analysis, the reasons of heterogeneity can be analyzed by referring to the study characteristics. The study characteristics which may be related to the heterogeneity between the included studies can be interpreted through subgroup analysis or meta-regression analysis (Deeks et al., 2008 ). While determining the moderator variables, the sufficiency of the number of variables, the relationship between the moderators, and the condition to explain the differences between the results of the studies have all been considered in the present study. Within this scope, it was predicted in this meta-analysis study that the heterogeneity can be explained with the country, class level, and lecture moderator variables of the study in terms of the effect of online education, which has been highly used since the beginning of the pandemic, and it has an impact on the students’ academic achievement in different lectures. Some subgroups were evaluated and categorized together, considering that the number of effect sizes of the sub-dimensions of the specified variables is not sufficient to perform moderator analysis (e.g. the countries where the studies were conducted).

2.6 Interpreting the effect sizes

Effect size is a factor that shows how much the independent variable affects the dependent variable positively or negatively in each included study in the meta-analysis (Dinçer, 2014 ). While interpreting the effect sizes obtained from the meta-analysis, the classifications of Cohen et al. ( 2007 ) have been utilized. The case of differentiating the specified relationships of the situation of the country, class level, and school subject variables of the study has been identified through the Q test, degree of freedom, and p significance value Fig.  1 and 2 .

3 Findings and results

The purpose of this study is to determine the effect size of online education on academic achievement. Before determining the effect sizes in the study, the probability of publication bias of this meta-analysis study was analyzed by using the funnel plot, Orwin’s Safe N Analysis, Duval and Tweedie’s Trip and Fill Analysis, and Egger’s Regression Test.

When the funnel plots are examined, it is seen that the studies included in the analysis are distributed symmetrically on both sides of the combined effect size axis, and they are generally collected in the middle and lower sections. The probability of publication bias is low according to the plots. However, since the results of the funnel scatter plots may cause subjective interpretations, they have been supported by additional analyses (Littell et al., 2008 ). Therefore, in order to provide an extra proof for the probability of publication bias, it has been analyzed through Orwin’s Safe N Analysis, Duval and Tweedie’s Trip and Fill Analysis, and Egger’s Regression Test (Table 2 ).

Table 2 consists of the results of the rates of publication bias probability before counting the effect size of online education on academic achievement. According to the table, Orwin Safe N analysis results show that it is not necessary to add new studies to the meta-analysis in order for Hedges g to reach a value outside the range of ± 0.01. The Duval and Tweedie test shows that excluding the studies that negatively affect the symmetry of the funnel scatter plots for each meta-analysis or adding their exact symmetrical equivalents does not significantly differentiate the calculated effect size. The insignificance of the Egger tests results reveals that there is no publication bias in the meta-analysis study. The results of the analysis indicate the high internal validity of the effect sizes and the adequacy of representing the studies conducted on the relevant subject.

In this study, it was aimed to determine the effect size of online education on academic achievement after testing the publication bias. In line with the first purpose of the study, the forest graph regarding the effect size of online education on academic achievement is shown in Fig.  3 , and the statistics regarding the effect size are given in Table 3 .

The flow chart of the scanning and selection process of the studies

Funnel plot graphics representing the effect size of the effects of online education on academic success

Forest graph related to the effect size of online education on academic success

The square symbols in the forest graph in Fig.  3 represent the effect sizes, while the horizontal lines show the intervals in 95% confidence of the effect sizes, and the diamond symbol shows the overall effect size. When the forest graph is analyzed, it is seen that the lower and upper limits of the combined effect sizes are generally close to each other, and the study loads are similar. This similarity in terms of study loads indicates the similarity of the contribution of the combined studies to the overall effect size.

Figure  3 clearly represents that the study of Liu and others (Liu et al., 2018 ) has the lowest, and the study of Ercan and Bilen ( 2014 ) has the highest effect sizes. The forest graph shows that all the combined studies and the overall effect are positive. Furthermore, it is simply understood from the forest graph in Fig.  3 and the effect size statistics in Table 3 that the results of the meta-analysis study conducted with 27 studies and analyzing the effect of online education on academic achievement illustrate that this relationship is on average level (= 0.409).

After the analysis of the effect size in the study, whether the studies included in the analysis are distributed heterogeneously or not has also been analyzed. The heterogeneity of the combined studies was determined through the Q and I 2 tests. As a result of the heterogeneity test, Q statistical value was calculated as 29.576. With 26 degrees of freedom at 95% significance level in the chi-square table, the critical value is accepted as 38.885. The Q statistical value (29.576) counted in this study is lower than the critical value of 38.885. The I 2 value, which is the complementary of the Q statistics, is 12.100%. This value indicates that the accurate heterogeneity or the total variability that can be attributed to variability between the studies is 12%. Besides, p value is higher than (0.285) p = 0.05. All these values [Q (26) = 29.579, p = 0.285; I2 = 12.100] indicate that there is a homogeneous distribution between the effect sizes, and fixed effects model should be used to interpret these effect sizes. However, some researchers argue that even if the heterogeneity is low, it should be evaluated based on the random effects model (Borenstein et al., 2007 ). Therefore, this study gives information about both models. The heterogeneity of the combined studies has been attempted to be explained with the characteristics of the studies included in the analysis. In this context, the final purpose of the study is to determine the effect of the country, academic level, and year variables on the findings. Accordingly, the statistics regarding the comparison of the stated relations according to the countries where the studies were conducted are given in Table 4 .

As seen in Table 4 , the effect of online education on academic achievement does not differ significantly according to the countries where the studies were conducted in. Q test results indicate the heterogeneity of the relationships between the variables in terms of countries where the studies were conducted in. According to the table, the effect of online education on academic achievement was reported as the highest in other countries, and the lowest in the US. The statistics regarding the comparison of the stated relations according to the class levels are given in Table 5 .

As seen in Table 5 , the effect of online education on academic achievement does not differ according to the class level. However, the effect of online education on academic achievement is the highest in the 4 th class. The statistics regarding the comparison of the stated relations according to the class levels are given in Table 6 .

As seen in Table 6 , the effect of online education on academic achievement does not differ according to the school subjects included in the studies. However, the effect of online education on academic achievement is the highest in ICT subject.

The obtained effect size in the study was formed as a result of the findings attained from primary studies conducted in 7 different countries. In addition, these studies are the ones on different approaches to online education (online learning environments, social networks, blended learning, etc.). In this respect, the results may raise some questions about the validity and generalizability of the results of the study. However, the moderator analyzes, whether for the country variable or for the approaches covered by online education, did not create significant differences in terms of the effect sizes. If significant differences were to occur in terms of effect sizes, we could say that the comparisons we will make by comparing countries under the umbrella of online education would raise doubts in terms of generalizability. Moreover, no study has been found in the literature that is not based on a special approach or does not contain a specific technique conducted under the name of online education alone. For instance, one of the commonly used definitions is blended education which is defined as an educational model in which online education is combined with traditional education method (Colis & Moonen, 2001 ). Similarly, Rasmussen ( 2003 ) defines blended learning as “a distance education method that combines technology (high technology such as television, internet, or low technology such as voice e-mail, conferences) with traditional education and training.” Further, Kerres and Witt (2003) define blended learning as “combining face-to-face learning with technology-assisted learning.” As it is clearly observed, online education, which has a wider scope, includes many approaches.

As seen in Table 7 , the effect of online education on academic achievement does not differ according to online education approaches included in the studies. However, the effect of online education on academic achievement is the highest in Web Based Problem Solving Approach.

4 Conclusions and discussion

Considering the developments during the pandemics, it is thought that the diversity in online education applications as an interdisciplinary pragmatist field will increase, and the learning content and processes will be enriched with the integration of new technologies into online education processes. Another prediction is that more flexible and accessible learning opportunities will be created in online education processes, and in this way, lifelong learning processes will be strengthened. As a result, it is predicted that in the near future, online education and even digital learning with a newer name will turn into the main ground of education instead of being an alternative or having a support function in face-to-face learning. The lessons learned from the early period online learning experience, which was passed with rapid adaptation due to the Covid19 epidemic, will serve to develop this method all over the world, and in the near future, online learning will become the main learning structure through increasing its functionality with the contribution of new technologies and systems. If we look at it from this point of view, there is a necessity to strengthen online education.

In this study, the effect of online learning on academic achievement is at a moderate level. To increase this effect, the implementation of online learning requires support from teachers to prepare learning materials, to design learning appropriately, and to utilize various digital-based media such as websites, software technology and various other tools to support the effectiveness of online learning (Rolisca & Achadiyah, 2014 ). According to research conducted by Rahayu et al. ( 2017 ), it has been proven that the use of various types of software increases the effectiveness and quality of online learning. Implementation of online learning can affect students' ability to adapt to technological developments in that it makes students use various learning resources on the internet to access various types of information, and enables them to get used to performing inquiry learning and active learning (Hart et al., 2019 ; Prestiadi et al., 2019 ). In addition, there may be many reasons for the low level of effect in this study. The moderator variables examined in this study could be a guide in increasing the level of practical effect. However, the effect size did not differ significantly for all moderator variables. Different moderator analyzes can be evaluated in order to increase the level of impact of online education on academic success. If confounding variables that significantly change the effect level are detected, it can be spoken more precisely in order to increase this level. In addition to the technical and financial problems, the level of impact will increase if a few other difficulties are eliminated such as students, lack of interaction with the instructor, response time, and lack of traditional classroom socialization.

In addition, COVID-19 pandemic related social distancing has posed extreme difficulties for all stakeholders to get online as they have to work in time constraints and resource constraints. Adopting the online learning environment is not just a technical issue, it is a pedagogical and instructive challenge as well. Therefore, extensive preparation of teaching materials, curriculum, and assessment is vital in online education. Technology is the delivery tool and requires close cross-collaboration between teaching, content and technology teams (CoSN, 2020 ).

Online education applications have been used for many years. However, it has come to the fore more during the pandemic process. This result of necessity has brought with it the discussion of using online education instead of traditional education methods in the future. However, with this research, it has been revealed that online education applications are moderately effective. The use of online education instead of face-to-face education applications can only be possible with an increase in the level of success. This may have been possible with the experience and knowledge gained during the pandemic process. Therefore, the meta-analysis of experimental studies conducted in the coming years will guide us. In this context, experimental studies using online education applications should be analyzed well. It would be useful to identify variables that can change the level of impacts with different moderators. Moderator analyzes are valuable in meta-analysis studies (for example, the role of moderators in Karl Pearson's typhoid vaccine studies). In this context, each analysis study sheds light on future studies. In meta-analyses to be made about online education, it would be beneficial to go beyond the moderators determined in this study. Thus, the contribution of similar studies to the field will increase more.

The purpose of this study is to determine the effect of online education on academic achievement. In line with this purpose, the studies that analyze the effect of online education approaches on academic achievement have been included in the meta-analysis. The total sample size of the studies included in the meta-analysis is 1772. While the studies included in the meta-analysis were conducted in the US, Taiwan, Turkey, China, Philippines, Ireland, and Georgia, the studies carried out in Europe could not be reached. The reason may be attributed to that there may be more use of quantitative research methods from a positivist perspective in the countries with an American academic tradition. As a result of the study, it was found out that the effect size of online education on academic achievement (g = 0.409) was moderate. In the studies included in the present research, we found that online education approaches were more effective than traditional ones. However, contrary to the present study, the analysis of comparisons between online and traditional education in some studies shows that face-to-face traditional learning is still considered effective compared to online learning (Ahmad et al., 2016 ; Hamdani & Priatna, 2020 ; Wei & Chou, 2020 ). Online education has advantages and disadvantages. The advantages of online learning compared to face-to-face learning in the classroom is the flexibility of learning time in online learning, the learning time does not include a single program, and it can be shaped according to circumstances (Lai et al., 2019 ). The next advantage is the ease of collecting assignments for students, as these can be done without having to talk to the teacher. Despite this, online education has several weaknesses, such as students having difficulty in understanding the material, teachers' inability to control students, and students’ still having difficulty interacting with teachers in case of internet network cuts (Swan, 2007 ). According to Astuti et al ( 2019 ), face-to-face education method is still considered better by students than e-learning because it is easier to understand the material and easier to interact with teachers. The results of the study illustrated that the effect size (g = 0.409) of online education on academic achievement is of medium level. Therefore, the results of the moderator analysis showed that the effect of online education on academic achievement does not differ in terms of country, lecture, class level, and online education approaches variables. After analyzing the literature, several meta-analyses on online education were published (Bernard et al., 2004 ; Machtmes & Asher, 2000 ; Zhao et al., 2005 ). Typically, these meta-analyzes also include the studies of older generation technologies such as audio, video, or satellite transmission. One of the most comprehensive studies on online education was conducted by Bernard et al. ( 2004 ). In this study, 699 independent effect sizes of 232 studies published from 1985 to 2001 were analyzed, and face-to-face education was compared to online education, with respect to success criteria and attitudes of various learners from young children to adults. In this meta-analysis, an overall effect size close to zero was found for the students' achievement (g +  = 0.01).

In another meta-analysis study carried out by Zhao et al. ( 2005 ), 98 effect sizes were examined, including 51 studies on online education conducted between 1996 and 2002. According to the study of Bernard et al. ( 2004 ), this meta-analysis focuses on the activities done in online education lectures. As a result of the research, an overall effect size close to zero was found for online education utilizing more than one generation technology for students at different levels. However, the salient point of the meta-analysis study of Zhao et al. is that it takes the average of different types of results used in a study to calculate an overall effect size. This practice is problematic because the factors that develop one type of learner outcome (e.g. learner rehabilitation), particularly course characteristics and practices, may be quite different from those that develop another type of outcome (e.g. learner's achievement), and it may even cause damage to the latter outcome. While mixing the studies with different types of results, this implementation may obscure the relationship between practices and learning.

Some meta-analytical studies have focused on the effectiveness of the new generation distance learning courses accessed through the internet for specific student populations. For instance, Sitzmann and others (Sitzmann et al., 2006 ) reviewed 96 studies published from 1996 to 2005, comparing web-based education of job-related knowledge or skills with face-to-face one. The researchers found that web-based education in general was slightly more effective than face-to-face education, but it is insufficient in terms of applicability ("knowing how to apply"). In addition, Sitzmann et al. ( 2006 ) revealed that Internet-based education has a positive effect on theoretical knowledge in quasi-experimental studies; however, it positively affects face-to-face education in experimental studies performed by random assignment. This moderator analysis emphasizes the need to pay attention to the factors of designs of the studies included in the meta-analysis. The designs of the studies included in this meta-analysis study were ignored. This can be presented as a suggestion to the new studies that will be conducted.

Another meta-analysis study was conducted by Cavanaugh et al. ( 2004 ), in which they focused on online education. In this study on internet-based distance education programs for students under 12 years of age, the researchers combined 116 results from 14 studies published between 1999 and 2004 to calculate an overall effect that was not statistically different from zero. The moderator analysis carried out in this study showed that there was no significant factor affecting the students' success. This meta-analysis used multiple results of the same study, ignoring the fact that different results of the same student would not be independent from each other.

In conclusion, some meta-analytical studies analyzed the consequences of online education for a wide range of students (Bernard et al., 2004 ; Zhao et al., 2005 ), and the effect sizes were generally low in these studies. Furthermore, none of the large-scale meta-analyzes considered the moderators, database quality standards or class levels in the selection of the studies, while some of them just referred to the country and lecture moderators. Advances in internet-based learning tools, the pandemic process, and increasing popularity in different learning contexts have required a precise meta-analysis of students' learning outcomes through online learning. Previous meta-analysis studies were typically based on the studies, involving narrow range of confounding variables. In the present study, common but significant moderators such as class level and lectures during the pandemic process were discussed. For instance, the problems have been experienced especially in terms of eligibility of class levels in online education platforms during the pandemic process. It was found that there is a need to study and make suggestions on whether online education can meet the needs of teachers and students.

Besides, the main forms of online education in the past were to watch the open lectures of famous universities and educational videos of institutions. In addition, online education is mainly a classroom-based teaching implemented by teachers in their own schools during the pandemic period, which is an extension of the original school education. This meta-analysis study will stand as a source to compare the effect size of the online education forms of the past decade with what is done today, and what will be done in the future.

Lastly, the heterogeneity test results of the meta-analysis study display that the effect size does not differ in terms of class level, country, online education approaches, and lecture moderators.

*Studies included in meta-analysis

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Taking distance learning ‘offline’: Lessons learned from navigating the digital divide during COVID-19

Subscribe to the center for universal education bulletin, angelica towne amporo and angelica towne amporo chief strategy and innovation officer & co-founder - educate hawah nabbuye hawah nabbuye 2018 echidna global scholar - the brookings institution, uganda country director - educate uganda @hawahhawah.

August 7, 2020

As we adjust to life during a global pandemic, it’s hard to imagine what life was like over a century ago during outbreaks. While in the past most faced quarantines without a telephone or a radio, today there is an expansive universe online. Even as the coronavirus forces physical isolation, the spectacular technological advances of the digital age make local and global connection possible. However, within education, the new centrality of communication technology in the context of the vast digital divide means the pandemic is exacerbating inequality, excluding many youth from their right to learn.

Prior to the outbreak of COVID-19, our East African youth skills organization, Educate! , reached youth primarily through national education systems—delivering our model directly in schools or working with the government. For over a decade, we’ve been operating this way, partnering with secondary schools in Uganda to prepare youth with the skills to succeed in today’s economy, as well as working on systems-level integration of skills-based learning in Uganda, Rwanda, and Kenya. But schools across East Africa have been closed since March, and access to tools like smartphones, internet, and electricity is scarce in the region. This means that many distance learning strategies being deployed in other parts of the world are not feasible, and we’ve observed a significant gap in solutions for youth. The challenges inherent to delivering distance learning in resource-constrained areas remain largely unsolved—requiring creative, context-driven solutions.

Our approach

When schools across East Africa closed in mid-March, Educate! acted quickly to launch a response—aiming to embrace the now and act swiftly —pivoting to deliver components of our skills-based model to youth remotely through radio, SMS (text messaging), and interactive voice response (“robocalls”). During this time, our team began executing extensive remote learning research, as well as developing data collection platforms, which would be key to ensuring our program best fit the needs of our learners.

Although moving to distance learning was new territory for us, luckily we didn’t have to reinvent the wheel. First, we invested in learning from the many organizations working to tackle the digital divide prior to COVID-19: Girl Effect in girls’ empowerment, Eneza and M-Shule in academic learning, and the countless organizations providing learning continuity in humanitarian emergencies . Leveraging these learnings and equity-focused best practices , our local teams of curriculum and learning experience designers hit the ground running.

In just over three months of implementation, we’ve experienced exciting progress and key breakthroughs, coupled with failures, flops, and stubborn challenges—all of which have been critical for developing distance learning strategies of our own. By sharing our emerging best practices, we hope to contribute to the creation of quality and equitable distance learning solutions, allowing young people in every corner of the world to stay engaged with their education.

Lessons learned for effective distance learning solutions

1. leverage user data to tailor programmatic design to learner realities.

Our greatest obstacle has been determining how to consistently reach youth with limited access to the internet and connectivity through phone or radio. To address this challenge and inform an effective response, we needed to deeply understand our students’ realities. And to understand our students’ realities, we needed data! While we leveraged existing country data on school closures, as well as young people’s broad access to technology, we needed to collect data specific to our students’ lives. We needed to understand what life was like at home, how frequently our students could access a phone or radio, what barriers they faced learning outside the classroom, and if gender affected their ability to participate.

While collecting data under countrywide coronavirus restrictions has been challenging, it has been critical for informing our response. To collect data, our team leveraged low-tech means, including disseminating surveys to youth through SMS, WhatsApp, and phone calls. We leveraged phone-based surveys to guide our programmatic decisionmaking and used WhatsApp groups for rapid design feedback. We have also targeted data on gender, developing a data point within our student contact database, allowing us to disaggregate by gender. As our team targets equal participation among boys and girls in our programming, disaggregation by gender has been critical for informing our remote gender equity strategy (discussed below in learning #5).

While these data collection platforms don’t reach all of our students, these systems have generated rich datasets on key indicators, such as participation. A key barrier we discovered through student surveys is that many youth have taken on new home responsibilities, cutting into time for their studies. Mornings are especially busy, as many students are completing household chores or supporting their families with agricultural work. In response to these learnings, we scheduled radio lessons on the weekends and sent learning prompts via SMS later in the day, when youth had finished their chores. By listening closely to our students and looking at a holistic picture of their lives, we have been able to increase participation in our remote programming quite simply, without addressing the complex issues of technology access.

2. Go beyond broadcasting content: Layer strategies and build in interaction

It’s widely recognized that real and meaningful learning occurs in the classroom only when curriculum goes beyond rote memorization and lecture-based instruction. We believe that the same approach should be applied to distance learning, so we have prioritized hybrid distance learning strategies that have two-way engagement built in.

We are taking a multipronged approach in Uganda—leveraging radio for content delivery, with robocalls, SMS, and remote mentorship for follow-up assessment, engagement, and guidance. While we don’t believe that distance learning strategies can replace in-person instruction, we think that “layering” strategies with built-in engagement can strengthen their impact. Evidence backs this up: In Kenya, a study examining the multimedia platform Shujazz showed that youth exhibited positive behavior changes after receiving targeted content through comics, social media, and SMS. Lastly, building in student responses to these mechanisms has the added advantage of supporting critical data collection.

3. Look for new ways to engage families

As schools began to close in March, our team urgently worked to collect student phone numbers to enroll students in our remote programming. However, of the 13,000 phone numbers we were able to collect, fewer than 50 percent were active. In addition, research conducted by our team at the outset of the pandemic found that many of our students only have access to a shared device for about 30 minutes per day.

Drawing on lessons learned from past emergencies, we conducted targeted outreach to parents and family members. We quickly learned that youth could participate more consistently in our remote programming if they used a family member’s phone rather than their own, as parents and relatives were more likely to own a phone as well as keep their phone numbers active. We also believe this strategy enhances the quality of the learning for youth because parents can help ensure their children engage actively with learning prompts. Further, a number of studies show that when communities and parents are engaged in students’ learning, academic achievement increases.

After targeting outreach to families, we saw a 29 percent increase in participation in our remote programming, and since launching, we have grown our reach from roughly 10 percent of our previous student level to 50-60 percent, with the expectation that our reach will continue to grow as we scale nationally. As with all things technology-enabled, this growth is exponential and has a snowball effect, so we’re hopeful about the future.

4. Incorporate story-based learning to keep youth engaged

Our team leveraged this feedback to rewrite radio scripts, rework linear learning activities, and introduce new characters within the lessons. While we are continuing to iterate on our distance learning curriculum, we are already beginning to see a positive impact, as 90 percent of our listeners have reported they relate to these story-based activities.

5. Think critically about pedagogy and content delivery to better support girls

Educate!’s curriculum was developed with gender responsiveness at the forefront, and we’ve designed our model to address critical gaps girls face—such as asset and skills gaps—to impact life outcomes. As we’ve worked to transition our curriculum to entirely new delivery mechanisms, we have taken a deliberate approach to integrating gender equity within our remote programming’s design and delivery.

Leveraging the data collection strategies outlined above, we discovered that boys in our programming were more likely to own their own phones than girls—making it challenging for our female learners to participate actively during radio lessons, as well as with assessments and learning prompts delivered via SMS. While we are still working to tackle the core issue of access among female learners, our team has set out to support girls and promote equal participation through a variety of programmatic components.

Our team of designers ensures that the content of every lesson and learning prompt delivered by radio or SMS is gender-responsive. For example, lead characters within our curriculum are female secondary school students, and we select confident female entrepreneurs within our case studies. Through our in-school model, we’ve seen that this strategy is effective in combating the socialization of girls to be quiet and reserved, as well as the negative stereotypes that typecast girls as less competent. In addition to gender-responsive pedagogy, we have begun exploring the implementation of all-girl listener groups as a way to create safe spaces at home for female learners. Following the release of a radio lesson, a female Educate! youth mentor convenes five to 10 girls on a conference call, where they connect to reflect on what they learned in the lesson, as well as discuss challenges they face learning at home.

In the foreseeable future, it seems likely that restrictions on gatherings will remain, limiting the education sector’s ability to reach youth directly in schools. By sharing these early lessons in effective distance learning, we believe we can work together as a sector to navigate this new normal. Together, we can rethink traditional education on a global level—pushing it further into the 21st century and toward a more equitable future.

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• Research article
• Open access
• Published: 15 February 2018

Blended learning: the new normal and emerging technologies

• Charles Dziuban 1 ,
• Charles R. Graham 2 ,
• Patsy D. Moskal   ORCID: orcid.org/0000-0001-6376-839X 1 ,
• Anders Norberg 3 &
• Nicole Sicilia 1  

International Journal of Educational Technology in Higher Education volume  15 , Article number:  3 ( 2018 ) Cite this article

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This study addressed several outcomes, implications, and possible future directions for blended learning (BL) in higher education in a world where information communication technologies (ICTs) increasingly communicate with each other. In considering effectiveness, the authors contend that BL coalesces around access, success, and students’ perception of their learning environments. Success and withdrawal rates for face-to-face and online courses are compared to those for BL as they interact with minority status. Investigation of student perception about course excellence revealed the existence of robust if-then decision rules for determining how students evaluate their educational experiences. Those rules were independent of course modality, perceived content relevance, and expected grade. The authors conclude that although blended learning preceded modern instructional technologies, its evolution will be inextricably bound to contemporary information communication technologies that are approximating some aspects of human thought processes.

Introduction

Blended learning and research issues.

Blended learning (BL), or the integration of face-to-face and online instruction (Graham 2013 ), is widely adopted across higher education with some scholars referring to it as the “new traditional model” (Ross and Gage 2006 , p. 167) or the “new normal” in course delivery (Norberg et al. 2011 , p. 207). However, tracking the accurate extent of its growth has been challenging because of definitional ambiguity (Oliver and Trigwell 2005 ), combined with institutions’ inability to track an innovative practice, that in many instances has emerged organically. One early nationwide study sponsored by the Sloan Consortium (now the Online Learning Consortium) found that 65.2% of participating institutions of higher education (IHEs) offered blended (also termed hybrid ) courses (Allen and Seaman 2003 ). A 2008 study, commissioned by the U.S. Department of Education to explore distance education in the U.S., defined BL as “a combination of online and in-class instruction with reduced in-class seat time for students ” (Lewis and Parsad 2008 , p. 1, emphasis added). Using this definition, the study found that 35% of higher education institutions offered blended courses, and that 12% of the 12.2 million documented distance education enrollments were in blended courses.

The 2017 New Media Consortium Horizon Report found that blended learning designs were one of the short term forces driving technology adoption in higher education in the next 1–2 years (Adams Becker et al. 2017 ). Also, blended learning is one of the key issues in teaching and learning in the EDUCAUSE Learning Initiative’s 2017 annual survey of higher education (EDUCAUSE 2017 ). As institutions begin to examine BL instruction, there is a growing research interest in exploring the implications for both faculty and students. This modality is creating a community of practice built on a singular and pervasive research question, “How is blended learning impacting the teaching and learning environment?” That question continues to gain traction as investigators study the complexities of how BL interacts with cognitive, affective, and behavioral components of student behavior, and examine its transformation potential for the academy. Those issues are so compelling that several volumes have been dedicated to assembling the research on how blended learning can be better understood (Dziuban et al. 2016 ; Picciano et al. 2014 ; Picciano and Dziuban 2007 ; Bonk and Graham 2007 ; Kitchenham 2011 ; Jean-François 2013 ; Garrison and Vaughan 2013 ) and at least one organization, the Online Learning Consortium, sponsored an annual conference solely dedicated to blended learning at all levels of education and training (2004–2015). These initiatives address blended learning in a wide variety of situations. For instance, the contexts range over K-12 education, industrial and military training, conceptual frameworks, transformational potential, authentic assessment, and new research models. Further, many of these resources address students’ access, success, withdrawal, and perception of the degree to which blended learning provides an effective learning environment.

Currently the United States faces a widening educational gap between our underserved student population and those communities with greater financial and technological resources (Williams 2016 ). Equal access to education is a critical need, one that is particularly important for those in our underserved communities. Can blended learning help increase access thereby alleviating some of the issues faced by our lower income students while resulting in improved educational equality? Although most indicators suggest “yes” (Dziuban et al. 2004 ), it seems that, at the moment, the answer is still “to be determined.” Quality education presents a challenge, evidenced by many definitions of what constitutes its fundamental components (Pirsig 1974 ; Arum et al. 2016 ). Although progress has been made by initiatives, such as, Quality Matters ( 2016 ), the OLC OSCQR Course Design Review Scorecard developed by Open SUNY (Open SUNY n.d. ), the Quality Scorecard for Blended Learning Programs (Online Learning Consortium n.d. ), and SERVQUAL (Alhabeeb 2015 ), the issue is by no means resolved. Generally, we still make quality education a perceptual phenomenon where we ascribe that attribute to a course, educational program, or idea, but struggle with precisely why we reached that decision. Searle ( 2015 ), summarizes the problem concisely arguing that quality does not exist independently, but is entirely observer dependent. Pirsig ( 1974 ) in his iconic volume on the nature of quality frames the context this way,

“There is such thing as Quality, but that as soon as you try to define it, something goes haywire. You can’t do it” (p. 91).

Therefore, attempting to formulate a semantic definition of quality education with syntax-based metrics results in what O’Neil (O'Neil 2017 ) terms surrogate models that are rough approximations and oversimplified. Further, the derived metrics tend to morph into goals or benchmarks, losing their original measurement properties (Goodhart 1975 ).

Information communication technologies in society and education

Blended learning forces us to consider the characteristics of digital technology, in general, and information communication technologies (ICTs), more specifically. Floridi ( 2014 ) suggests an answer proffered by Alan Turing: that digital ICTs can process information on their own, in some sense just as humans and other biological life. ICTs can also communicate information to each other, without human intervention, but as linked processes designed by humans. We have evolved to the point where humans are not always “in the loop” of technology, but should be “on the loop” (Floridi 2014 , p. 30), designing and adapting the process. We perceive our world more and more in informational terms, and not primarily as physical entities (Floridi 2008 ). Increasingly, the educational world is dominated by information and our economies rest primarily on that asset. So our world is also blended, and it is blended so much that we hardly see the individual components of the blend any longer. Floridi ( 2014 ) argues that the world has become an “infosphere” (like biosphere) where we live as “inforgs.” What is real for us is shifting from the physical and unchangeable to those things with which we can interact.

Floridi also helps us to identify the next blend in education, involving ICTs, or specialized artificial intelligence (Floridi 2014 , 25; Norberg 2017 , 65). Learning analytics, adaptive learning, calibrated peer review, and automated essay scoring (Balfour 2013 ) are advanced processes that, provided they are good interfaces, can work well with the teacher— allowing him or her to concentrate on human attributes such as being caring, creative, and engaging in problem-solving. This can, of course, as with all technical advancements, be used to save resources and augment the role of the teacher. For instance, if artificial intelligence can be used to work along with teachers, allowing them more time for personal feedback and mentoring with students, then, we will have made a transformational breakthrough. The Edinburg University manifesto for teaching online says bravely, “Automation need not impoverish education – we welcome our robot colleagues” (Bayne et al. 2016 ). If used wisely, they will teach us more about ourselves, and about what is truly human in education. This emerging blend will also affect curricular and policy questions, such as the what? and what for? The new normal for education will be in perpetual flux. Floridi’s ( 2014 ) philosophy offers us tools to understand and be in control and not just sit by and watch what happens. In many respects, he has addressed the new normal for blended learning.

Literature of blended learning

A number of investigators have assembled a comprehensive agenda of transformative and innovative research issues for blended learning that have the potential to enhance effectiveness (Garrison and Kanuka 2004 ; Picciano 2009 ). Generally, research has found that BL results in improvement in student success and satisfaction, (Dziuban and Moskal 2011 ; Dziuban et al. 2011 ; Means et al. 2013 ) as well as an improvement in students’ sense of community (Rovai and Jordan 2004 ) when compared with face-to-face courses. Those who have been most successful at blended learning initiatives stress the importance of institutional support for course redesign and planning (Moskal et al. 2013 ; Dringus and Seagull 2015 ; Picciano 2009 ; Tynan et al. 2015 ). The evolving research questions found in the literature are long and demanding, with varied definitions of what constitutes “blended learning,” facilitating the need for continued and in-depth research on instructional models and support needed to maximize achievement and success (Dringus and Seagull 2015 ; Bloemer and Swan 2015 ).

Educational access

The lack of access to educational technologies and innovations (sometimes termed the digital divide) continues to be a challenge with novel educational technologies (Fairlie 2004 ; Jones et al. 2009 ). One of the promises of online technologies is that they can increase access to nontraditional and underserved students by bringing a host of educational resources and experiences to those who may have limited access to on-campus-only higher education. A 2010 U.S. report shows that students with low socioeconomic status are less likely to obtain higher levels of postsecondary education (Aud et al. 2010 ). However, the increasing availability of distance education has provided educational opportunities to millions (Lewis and Parsad 2008 ; Allen et al. 2016 ). Additionally, an emphasis on open educational resources (OER) in recent years has resulted in significant cost reductions without diminishing student performance outcomes (Robinson et al. 2014 ; Fischer et al. 2015 ; Hilton et al. 2016 ).

Unfortunately, the benefits of access may not be experienced evenly across demographic groups. A 2015 study found that Hispanic and Black STEM majors were significantly less likely to take online courses even when controlling for academic preparation, socioeconomic status (SES), citizenship, and English as a second language (ESL) status (Wladis et al. 2015 ). Also, questions have been raised about whether the additional access afforded by online technologies has actually resulted in improved outcomes for underserved populations. A distance education report in California found that all ethnic minorities (except Asian/Pacific Islanders) completed distance education courses at a lower rate than the ethnic majority (California Community Colleges Chancellor’s Office 2013 ). Shea and Bidjerano ( 2014 , 2016 ) found that African American community college students who took distance education courses completed degrees at significantly lower rates than those who did not take distance education courses. On the other hand, a study of success factors in K-12 online learning found that for ethnic minorities, only 1 out of 15 courses had significant gaps in student test scores (Liu and Cavanaugh 2011 ). More research needs to be conducted, examining access and success rates for different populations, when it comes to learning in different modalities, including fully online and blended learning environments.

Framing a treatment effect

Over the last decade, there have been at least five meta-analyses that have addressed the impact of blended learning environments and its relationship to learning effectiveness (Zhao et al. 2005 ; Sitzmann et al. 2006 ; Bernard et al. 2009 ; Means et al. 2010 , 2013 ; Bernard et al. 2014 ). Each of these studies has found small to moderate positive effect sizes in favor of blended learning when compared to fully online or traditional face-to-face environments. However, there are several considerations inherent in these studies that impact our understanding the generalizability of outcomes.

Dziuban and colleagues (Dziuban et al. 2015 ) analyzed the meta-analyses conducted by Means and her colleagues (Means et al. 2013 ; Means et al. 2010 ), concluding that their methods were impressive as evidenced by exhaustive study inclusion criteria and the use of scale-free effect size indices. The conclusion, in both papers, was that there was a modest difference in multiple outcome measures for courses featuring online modalities—in particular, blended courses. However, with blended learning especially, there are some concerns with these kinds of studies. First, the effect sizes are based on the linear hypothesis testing model with the underlying assumption that the treatment and the error terms are uncorrelated, indicating that there is nothing else going on in the blending that might confound the results. Although the blended learning articles (Means et al. 2010 ) were carefully vetted, the assumption of independence is tenuous at best so that these meta-analysis studies must be interpreted with extreme caution.

There is an additional concern with blended learning as well. Blends are not equivalent because of the manner on which they are configured. For instance, a careful reading of the sources used in the Means, et al. papers will identify, at minimum, the following blending techniques: laboratory assessments, online instruction, e-mail, class web sites, computer laboratories, mapping and scaffolding tools, computer clusters, interactive presentations and e-mail, handwriting capture, evidence-based practice, electronic portfolios, learning management systems, and virtual apparatuses. These are not equivalent ways in which to configure courses, and such nonequivalence constitutes the confounding we describe. We argue here that, in actuality, blended learning is a general construct in the form of a boundary object (Star and Griesemer 1989 ) rather than a treatment effect in the statistical sense. That is, an idea or concept that can support a community of practice, but is weakly defined fostering disagreement in the general group. Conversely, it is stronger in individual constituencies. For instance, content disciplines (i.e. education, rhetoric, optics, mathematics, and philosophy) formulate a more precise definition because of commonly embraced teaching and learning principles. Quite simply, the situation is more complicated than that, as Leonard Smith ( 2007 ) says after Tolstoy,

“All linear models resemble each other, each non nonlinear system is unique in its own way” (p. 33).

This by no means invalidates these studies, but effect size associated with blended learning should be interpreted with caution where the impact is evaluated within a particular learning context.

Study objectives

This study addressed student access by examining success and withdrawal rates in the blended learning courses by comparing them to face-to-face and online modalities over an extended time period at the University of Central Florida. Further, the investigators sought to assess the differences in those success and withdrawal rates with the minority status of students. Secondly, the investigators examined the student end-of-course ratings of blended learning and other modalities by attempting to develop robust if-then decision rules about what characteristics of classes and instructors lead students to assign an “excellent” value to their educational experience. Because of the high stakes nature of these student ratings toward faculty promotion, awards, and tenure, they act as a surrogate measure for instructional quality. Next, the investigators determined the conditional probabilities for students conforming to the identified rule cross-referenced by expected grade, the degree to which they desired to take the course, and course modality.

Student grades by course modality were recoded into a binary variable with C or higher assigned a value of 1, and remaining values a 0. This was a declassification process that sacrificed some specificity but compensated for confirmation bias associated with disparate departmental policies regarding grade assignment. At the measurement level this was an “on track to graduation index” for students. Withdrawal was similarly coded by the presence or absence of its occurrence. In each case, the percentage of students succeeding or withdrawing from blended, online or face-to-face courses was calculated by minority and non-minority status for the fall 2014 through fall 2015 semesters.

Next, a classification and regression tree (CART) analysis (Brieman et al. 1984 ) was performed on the student end-of-course evaluation protocol ( Appendix 1 ). The dependent measure was a binary variable indicating whether or not a student assigned an overall rating of excellent to his or her course experience. The independent measures in the study were: the remaining eight rating items on the protocol, college membership, and course level (lower undergraduate, upper undergraduate, and graduate). Decision trees are efficient procedures for achieving effective solutions in studies such as this because with missing values imputation may be avoided with procedures such as floating methods and the surrogate formation (Brieman et al. 1984 , Olshen et al. 1995 ). For example, a logistic regression method cannot efficiently handle all variables under consideration. There are 10 independent variables involved here; one variable has three levels, another has nine, and eight have five levels each. This means the logistic regression model must incorporate more than 50 dummy variables and an excessively large number of two-way interactions. However, the decision-tree method can perform this analysis very efficiently, permitting the investigator to consider higher order interactions. Even more importantly, decision trees represent appropriate methods in this situation because many of the variables are ordinally scaled. Although numerical values can be assigned to each category, those values are not unique. However, decision trees incorporate the ordinal component of the variables to obtain a solution. The rules derived from decision trees have an if-then structure that is readily understandable. The accuracy of these rules can be assessed with percentages of correct classification or odds-ratios that are easily understood. The procedure produces tree-like rule structures that predict outcomes.

The model-building procedure for predicting overall instructor rating

For this study, the investigators used the CART method (Brieman et al. 1984 ) executed with SPSS 23 (IBM Corp 2015 ). Because of its strong variance-sharing tendencies with the other variables, the dependent measure for the analysis was the rating on the item Overall Rating of the Instructor , with the previously mentioned indicator variables (college, course level, and the remaining 8 questions) on the instrument. Tree methods are recursive, and bisect data into subgroups called nodes or leaves. CART analysis bases itself on: data splitting, pruning, and homogeneous assessment.

Splitting the data into two (binary) subsets comprises the first stage of the process. CART continues to split the data until the frequencies in each subset are either very small or all observations in a subset belong to one category (e.g., all observations in a subset have the same rating). Usually the growing stage results in too many terminate nodes for the model to be useful. CART solves this problem using pruning methods that reduce the dimensionality of the system.

The final stage of the analysis involves assessing homogeneousness in growing and pruning the tree. One way to accomplish this is to compute the misclassification rates. For example, a rule that produces a .95 probability that an instructor will receive an excellent rating has an associated error of 5.0%.

Implications for using decision trees

Although decision-tree techniques are effective for analyzing datasets such as this, the reader should be aware of certain limitations. For example, since trees use ranks to analyze both ordinal and interval variables, information can be lost. However, the most serious weakness of decision tree analysis is that the results can be unstable because small initial variations can lead to substantially different solutions.

For this study model, these problems were addressed with the k-fold cross-validation process. Initially the dataset was partitioned randomly into 10 subsets with an approximately equal number of records in each subset. Each cohort is used as a test partition, and the remaining subsets are combined to complete the function. This produces 10 models that are all trained on different subsets of the original dataset and where each has been used as the test partition one time only.

Although computationally dense, CART was selected as the analysis model for a number of reasons— primarily because it provides easily interpretable rules that readers will be able evaluate in their particular contexts. Unlike many other multivariate procedures that are even more sensitive to initial estimates and require a good deal of statistical sophistication for interpretation, CART has an intuitive resonance with researcher consumers. The overriding objective of our choice of analysis methods was to facilitate readers’ concentration on our outcomes rather than having to rely on our interpretation of the results.

Institution-level evaluation: Success and withdrawal

The University of Central Florida (UCF) began a longitudinal impact study of their online and blended courses at the start of the distributed learning initiative in 1996. The collection of similar data across multiple semesters and academic years has allowed UCF to monitor trends, assess any issues that may arise, and provide continual support for both faculty and students across varying demographics. Table  1 illustrates the overall success rates in blended, online and face-to-face courses, while also reporting their variability across minority and non-minority demographics.

While success (A, B, or C grade) is not a direct reflection of learning outcomes, this overview does provide an institutional level indication of progress and possible issues of concern. BL has a slight advantage when looking at overall success and withdrawal rates. This varies by discipline and course, but generally UCF’s blended modality has evolved to be the best of both worlds, providing an opportunity for optimizing face-to-face instruction through the effective use of online components. These gains hold true across minority status. Reducing on-ground time also addresses issues that impact both students and faculty such as parking and time to reach class. In addition, UCF requires faculty to go through faculty development tailored to teaching in either blended or online modalities. This 8-week faculty development course is designed to model blended learning, encouraging faculty to redesign their course and not merely consider blended learning as a means to move face-to-face instructional modules online (Cobb et al. 2012 ; Lowe 2013 ).

Withdrawal (Table  2 ) from classes impedes students’ success and retention and can result in delayed time to degree, incurred excess credit hour fees, or lost scholarships and financial aid. Although grades are only a surrogate measure for learning, they are a strong predictor of college completion. Therefore, the impact of any new innovation on students’ grades should be a component of any evaluation. Once again, the blended modality is competitive and in some cases results in lower overall withdrawal rates than either fully online or face-to-face courses.

The students’ perceptions of their learning environments

Other potentially high-stakes indicators can be measured to determine the impact of an innovation such as blended learning on the academy. For instance, student satisfaction and attitudes can be measured through data collection protocols, including common student ratings, or student perception of instruction instruments. Given that those ratings often impact faculty evaluation, any negative reflection can derail the successful implementation and scaling of an innovation by disenfranchised instructors. In fact, early online and blended courses created a request by the UCF faculty senate to investigate their impact on faculty ratings as compared to face-to-face sections. The UCF Student Perception of Instruction form is released automatically online through the campus web portal near the end of each semester. Students receive a splash page with a link to each course’s form. Faculty receive a scripted email that they can send to students indicating the time period that the ratings form will be available. The forms close at the beginning of finals week. Faculty receive a summary of their results following the semester end.

The instrument used for this study was developed over a ten year period by the faculty senate of the University of Central Florida, recognizing the evolution of multiple course modalities including blended learning. The process involved input from several constituencies on campus (students, faculty, administrators, instructional designers, and others), in attempt to provide useful formative and summative instructional information to the university community. The final instrument was approved by resolution of the senate and, currently, is used across the university. Students’ rating of their classes and instructors comes with considerable controversy and disagreement with researchers aligning themselves on both sides of the issue. Recently, there have been a number of studies criticizing the process (Uttl et al. 2016 ; Boring et al. 2016 ; & Stark and Freishtat 2014 ). In spite of this discussion, a viable alternative has yet to emerge in higher education. So in the foreseeable future, the process is likely to continue. Therefore, with an implied faculty senate mandate this study was initiated by this team of researchers.

Prior to any analysis of the item responses collected in this campus-wide student sample, the psychometric quality (domain sampling) of the information yielded by the instrument was assessed. Initially, the reliability (internal consistency) was derived using coefficient alpha (Cronbach 1951 ). In addition, Guttman ( 1953 ) developed a theorem about item properties that leads to evidence about the quality of one’s data, demonstrating that as the domain sampling properties of items improve, the inverse of the correlation matrix among items will approach a diagonal. Subsequently, Kaiser and Rice ( 1974 ) developed the measure of sampling adequacy (MSA) that is a function of the Guttman Theorem. The index has an upper bound of one with Kaiser offering some decision rules for interpreting the value of MSA. If the value of the index is in the .80 to .99 range, the investigator has evidence of an excellent domain sample. Values in the .70s signal an acceptable result, and those in the .60s indicate data that are unacceptable. Customarily, the MSA has been used for data assessment prior to the application of any dimensionality assessments. Computation of the MSA value gave the investigators a benchmark for the construct validity of the items in this study. This procedure has been recommended by Dziuban and Shirkey ( 1974 ) prior to any latent dimension analysis and was used with the data obtained for this study. The MSA for the current instrument was .98 suggesting excellent domain sampling properties with an associated alpha reliability coefficient of .97 suggesting superior internal consistency. The psychometric properties of the instrument were excellent with both measures.

The online student ratings form presents an electronic data set each semester. These can be merged across time to create a larger data set of completed ratings for every course across each semester. In addition, captured data includes course identification variables including prefix, number, section and semester, department, college, faculty, and class size. The overall rating of effectiveness is used most heavily by departments and faculty in comparing across courses and modalities (Table  3 ).

The finally derived tree (decision rules) included only three variables—survey items that asked students to rate the instructor’s effectiveness at:

Helping students achieve course objectives,

Creating an environment that helps students learn, and

Communicating ideas and information.

None of the demographic variables associated with the courses contributed to the final model. The final rule specifies that if a student assigns an excellent rating to those three items, irrespective of their status on any other condition, the probability is .99 that an instructor will receive an overall rating of excellent. The converse is true as well. A poor rating on all three of those items will lead to a 99% chance of an instructor receiving an overall rating of poor.

Tables  4 , 5 and 6 present a demonstration of the robustness of the CART rule for variables on which it was not developed: expected course grade, desire to take the course and modality.

In each case, irrespective of the marginal probabilities, those students conforming to the rule have a virtually 100% chance of seeing the course as excellent. For instance, 27% of all students expecting to fail assigned an excellent rating to their courses, but when they conformed to the rule the percentage rose to 97%. The same finding is true when students were asked about their desire to take the course with those who strongly disagreed assigning excellent ratings to their courses 26% of the time. However, for those conforming to the rule, that category rose to 92%. When course modality is considered in the marginal sense, blended learning is rated as the preferred choice. However, from Table  6 we can observe that the rule equates student assessment of their learning experiences. If they conform to the rule, they will see excellence.

This study addressed increasingly important issues of student success, withdrawal and perception of the learning environment across multiple course modalities. Arguably these components form the crux of how we will make more effective decisions about how blended learning configures itself in the new normal. The results reported here indicate that blending maintains or increases access for most student cohorts and produces improved success rates for minority and non-minority students alike. In addition, when students express their beliefs about the effectiveness of their learning environments, blended learning enjoys the number one rank. However, upon more thorough analysis of key elements students view as important in their learning, external and demographic variables have minimal impact on those decisions. For example college (i.e. discipline) membership, course level or modality, expected grade or desire to take a particular course have little to do with their course ratings. The characteristics they view as important relate to clear establishment and progress toward course objectives, creating an effective learning environment and the instructors’ effective communication. If in their view those three elements of a course are satisfied they are virtually guaranteed to evaluate their educational experience as excellent irrespective of most other considerations. While end of course rating protocols are summative the three components have clear formative characteristics in that each one is directly related to effective pedagogy and is responsive to faculty development through units such as the faculty center for teaching and learning. We view these results as encouraging because they offer potential for improving the teaching and learning process in an educational environment that increases the pressure to become more responsive to contemporary student lifestyles.

Clearly, in this study we are dealing with complex adaptive systems that feature the emergent property. That is, their primary agents and their interactions comprise an environment that is more than the linear combination of their individual elements. Blending learning, by interacting with almost every aspect of higher education, provides opportunities and challenges that we are not able to fully anticipate.

This pedagogy alters many assumptions about the most effective way to support the educational environment. For instance, blending, like its counterpart active learning, is a personal and individual phenomenon experienced by students. Therefore, it should not be surprising that much of what we have called blended learning is, in reality, blended teaching that reflects pedagogical arrangements. Actually, the best we can do for assessing impact is to use surrogate measures such as success, grades, results of assessment protocols, and student testimony about their learning experiences. Whether or not such devices are valid indicators remains to be determined. We may be well served, however, by changing our mode of inquiry to blended teaching.

Additionally, as Norberg ( 2017 ) points out, blended learning is not new. The modality dates back, at least, to the medieval period when the technology of textbooks was introduced into the classroom where, traditionally, the professor read to the students from the only existing manuscript. Certainly, like modern technologies, books were disruptive because they altered the teaching and learning paradigm. Blended learning might be considered what Johnson describes as a slow hunch (2010). That is, an idea that evolved over a long period of time, achieving what Kaufmann ( 2000 ) describes as the adjacent possible – a realistic next step occurring in many iterations.

The search for a definition for blended learning has been productive, challenging, and, at times, daunting. The definitional continuum is constrained by Oliver and Trigwell ( 2005 ) castigation of the concept for its imprecise vagueness to Sharpe et al.’s ( 2006 ) notion that its definitional latitude enhances contextual relevance. Both extremes alter boundaries such as time, place, presence, learning hierarchies, and space. The disagreement leads us to conclude that Lakoff’s ( 2012 ) idealized cognitive models i.e. arbitrarily derived concepts (of which blended learning might be one) are necessary if we are to function effectively. However, the strong possibility exists that blended learning, like quality, is observer dependent and may not exist outside of our perceptions of the concept. This, of course, circles back to the problem of assuming that blending is a treatment effect for point hypothesis testing and meta-analysis.

Ultimately, in this article, we have tried to consider theoretical concepts and empirical findings about blended learning and their relationship to the new normal as it evolves. Unfortunately, like unresolved chaotic solutions, we cannot be sure that there is an attractor or that it will be the new normal. That being said, it seems clear that blended learning is the harbinger of substantial change in higher education and will become equally impactful in K-12 schooling and industrial training. Blended learning, because of its flexibility, allows us to maximize many positive education functions. If Floridi ( 2014 ) is correct and we are about to live in an environment where we are on the communication loop rather than in it, our educational future is about to change. However, if our results are correct and not over fit to the University of Central Florida and our theoretical speculations have some validity, the future of blended learning should encourage us about the coming changes.

Adams Becker, S., Cummins, M., Davis, A., Freeman, A., Hall Giesinger, C., & Ananthanarayanan, V. (2017). NMC horizon report: 2017 higher Education Edition . Austin: The New Media Consortium.

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The authors acknowledge the contributions of several investigators and course developers from the Center for Distributed Learning at the University of Central Florida, the McKay School of Education at Brigham Young University, and Scholars at Umea University, Sweden. These professionals contributed theoretical and practical ideas to this research project and carefully reviewed earlier versions of this manuscript. The Authors gratefully acknowledge their support and assistance.

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Dziuban, C., Graham, C.R., Moskal, P.D. et al. Blended learning: the new normal and emerging technologies. Int J Educ Technol High Educ 15 , 3 (2018). https://doi.org/10.1186/s41239-017-0087-5

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An effective blended online teaching and learning strategy during the COVID-19 pandemic

Lorico ds. lapitan, jr..

a Department of Chemical Engineering, Faculty of Engineering, University of Santo Tomas, Manila, Philippines

b Research Center for the Natural and Applied Sciences, Manila, Philippines

Cristina E. Tiangco

Divine angela g. sumalinog, noel s. sabarillo, joey mark diaz.

c Leeds Institute of Medical Research, School of Medicine, University of Leeds, Leeds West Yorkshire, United Kingdom

Associated Data

The shift to distance teaching and learning during the COVID-19 pandemic brought about a real challenge for both instructors and students. To face these difficulties in teaching undergraduate Chemistry courses at the University of Santo Tomas, a blended learning strategy in the context of teaching and learning of Physical Chemistry 1 and Analytical Chemistry for Chemical Engineering students were employed. Here, we present an online strategy that facilitated the transition from traditional face-to-face learning to full online instruction. This is a five-component blended learning strategy referred to as Discover, Learn, Practice, Collaborate and Assess (DLPCA). In DLPCA, the asynchronous part of the teaching was achieved through broadcast of pre-recorded lecture videos on YouTube to allow students to study and progress with learning at their own pace. The synchronous part of the teaching was conducted using video conferencing platforms, such as Zoom or Google Meet. The DLPCA strategy was presented and discussed to the students prior to its implementation. The analysis of the teaching and learning experience based on three indicators (i) student’s learning experience, (ii) student’s academic performance and (iii) instructor observations showed that DLPCA had a positive impact on students and instructors. The identified challenges were stability of internet connection and instructor’s familiarity with readily available internet-based teaching tools, such as video conferencing software. Instructors must also find means to improve their interaction with students and maintain student interest and engagement during online classes. The survey also indicated that most of the students are satisfied with the DLCPA strategy. Hence, this strategy is considered a manageable and effective alternative that can be adapted to full online instruction to other undergraduate Chemistry lecture courses. Overall, the findings and insights in this study will add valuable resources for further hybrid instruction in the post-COVID-19 time in higher education.

1. Introduction

1.1. context of the study.

The Coronavirus Disease 2019 (COVID-19) pandemic has dramatically changed the higher education system in the Philippines with a distinctive shift in online instruction as an effort to limit further transmission of the virus. This sudden change to online instruction raised concern among many teachers and students because a large segment of the population have unstable internet access and limited electronic devices ( Pastor, 2020 ; Mirandilla-Santos, 2016 ). Since the pandemic started and presently shows little signs of declining, worries whether internet connection would not suffice to support online education persist as a challenge. Undergraduate Chemical Engineering students are required to take Analytical Chemistry and Physical Chemistry 1 courses during their first and second year of studies at universities in the Philippines. The Physical Chemistry 1 curriculum for Chemical Engineering undergraduate students includes topics in properties of gases, laws of thermodynamics, and phase equilibria. The Analytical Chemistry course includes topics in chemical equilibrium, classical quantitative analysis, and instrumental methods analysis.

The second term of the academic year (AY) 2019–2020, which is from January to May 2020, at the University of Santo Tomas (UST) was indefinitely suspended at around March due to the steadily increasing COVID-19 cases in Metro Manila, risks and local transmission concerns of COVID-19. This led to all courses being advised to shift online until the end of the second term. Due to the projected continuous increase of cases, it was also later decided by the University that online classes will be implemented until the first semester of AY 2020–2021 (August to December 2020). The sudden shift to full online instruction led faculty members to adjust their teaching plans, teaching styles and assessment methods. Students also faced the challenge to quickly adapt to the “new normal” in higher education setting. The shift to online instruction was a contingency plan to secure the continuation of the courses offered by the University and enable students to continue with their studies. However, developing countries, like the Philippines, have areas that do not have a reliable or existent internet connection which posed a great and major challenge to the shift to full online instruction.

As the immediate future is uncertain with new outbreaks and looming lockdowns, many instructors had to consider online instruction, which can be given in one of three pedagogical approaches: (1) synchronous, (2) asynchronous and (3) blended learning strategy. In synchronous online lectures (real-time), instructors and students meet online using a video conferencing software during the designated class hours and instructors give lectures on the course. Students participate in the lectures and are able to ask questions vocally or via live text chat. In asynchronous lectures, instructors record lecture videos and upload them in Blackboard learning management system (LMS) or YouTube, so that students can access them in their most convenient time.

The blended online learning strategy is deemed to be the most practical method to adapt as this combines the advantages of synchronous and asynchronous strategies. The main motivation in choosing the blended strategy is to increase the student’s participation in their own learning process rather than quietly sitting during a synchronous discussion. The basis of this approach is the cognitive load theory, on the basis that novice learners are immediately overwhelmed by a large amount of new ideas and terminologies, and resort to surface learning ( Darabi and Jin, 2013 ; Seery and Donnelly, 2012 ; Seery, 2013 ). This type of active learning pedagogy is called “flipped classroom” approach ( Bergmann and Sams, 2012 ; Olakanmi, 2017 ). In this learning approach, traditional lecture and homework are replaced by pre-class activities, such as viewing short, pre-recorded lecture videos. The class time is devoted to further reinforce the topics through problem solving examples, interactive activities and detailed discussions ( Pienta, 2016 ; Rau et al., 2017 ). However, the synchronous online class sessions (called the “virtual classroom”) replaced the traditional face-to-face class for engaging the students with activities and guided problem-solving discussions in the traditional flipped classroom.

The benefits from flipped classroom were reported by economists ( Lage et al., 2000 ). Lage and colleagues showed that reducing variability in teaching styles across classroom and implementing various activities to create an inclusive classroom resulted to an improved student performance ( Lage et al., 2000 ). Several other disciplines have reported a similar success with implementing the flipped learning in materials science courses ( Liou et al., 2016 ), pharmacy ( Koo et al., 2016 ), statistics ( Peterson, 2016 ), engineering education ( Kerr, 2015 ; Chiquito et al., 2020 ), computer science ( Sohrabi and Traj, 2016 ; Davies et al., 2013 ), and health science courses ( Betihavas et al., 2016 ; McLaughlin et al., 2014 ). In chemistry, flipped classrooms were first introduced in a high school general chemistry curriculum ( Bergmann and Sams, 2012 ). There are several literatures that discuss the benefits that can be accrued from flip learning in chemistry courses with most of the examples presented involve high school general chemistry ( Bergmann and Sams, 2012 ; Schultz et al., 2014 ). Moreover, substantial amount of work has been published on the effectiveness of the flipped classroom when implemented higher education chemistry courses such as General chemistry, Organic chemistry, and Biochemistry ( Smith, 2013 ; Fautch, 2015 ; Seery, 2015 ; Mooring et al., 2016 ; Ojennus, 2016 ; Bokosmaty et al., 2019 ). Interestingly, reports published about the effectiveness of flipped learning in calculation intensive courses such as Analytical Chemistry and Physical Chemistry are scarce ( Fitzgerald and Li, 2015 ; Esson, 2016 ). Therefore, it is important for this paper to contribute to this current information gap.

1.2. Course format

The next focus of the instructors was to organize and deliver the content to achieve the learning objectives of the course. Unlike in some developed countries where teaching is designed with the assumption that all the students have equal technical and cultural resources to access academic materials, developing countries, such as the Philippines, must give high consideration on the socio-technical constraints of all students when designing the course content and delivery.

The Discover, Learn, Practice, Collaborate and Assess (DLPCA) strategy was conceptualized for this blended learning technique with the goal of integrating the instructors, students, and readily available technologies to meet the challenges of higher education during this pandemic. Fig. 1 shows the five (5) components of DLCPA with a brief explanation of each component. Students were first asked to discover all learning materials prepared for the assigned topic which were uploaded in the UST Blackboard LMS ( Fig. 1 a). Next, the students are expected to learn the terminologies, concepts, and calculations through the pre-recorded lecture videos and other materials provided, such as notes, web links to other resources (e.g. Khan Academy, ChemLibreText), and chemistry infographics ( Fig. 1 b). The practice component allows students to apply what they learned using the self-assessment questions (SAQs) ( Fig. 1 c). Students are given enough time to view short, pre-recorded video lectures and answer the SAQs before joining the online class session. The class time is devoted for students to collaborate in doing interactive activities, such as quiz bees and discussions ( Fig. 1 d). The synchronous online sessions were used to discuss and clarify specific aspects of the concepts and calculations that students found difficult to understand. The collaborate component is expected to positively impact student engagement with the instructor and peer learning. Finally, the assess component are quizzes or exams that are given with allotted time to test the student’s comprehension of the topics based on the declared intended course learning outcomes ( Fig. 1 e).

The 5 components of the DLPCA strategy – (a) Discover , (b) Learn , (c) Practice , (d) Collaborate , and (e) Assess .

Online lectures are not very common in most universities in the Philippines and chemistry lectures are generally given in classroom settings. The COVID-19 pandemic undeniably accelerated the process of transition to full online instruction and provided opportunities to carry out effective online teaching. It is worthwhile to examine if the implemented DLPCA strategy is an effective method for full online instruction. By collecting the experiences of the authors and students who have worked and studied during the COVID-19 pandemic, we aim to provide a better understanding on how the DLPCA strategy enabled teachers and students to rise to the challenges of online instruction given the resources and technologies present at the time. Specifically, we investigated three important aspects of online instruction, namely: (i) online content delivery strategy, (ii) learning mechanisms (synchronous and asynchronous), and (iii) assessment type and strategies. The results presented in this paper will provide a preliminary basis on the adaption of DLPCA strategy in online undergraduate Analytical chemistry and Physical chemistry courses and will help build a strong foundation for future pedagogical decisions regarding online instruction.

2. Methodology

2.1. equipment and software for recorded lecture videos.

Recorded lecture videos are a very important part of DLPCA strategy which were given to students before attending the synchronous sessions. Lecture videos were made simple, readable, visually appealing, understandable, and easily accessible for students. Narrations or discussions were recorded using Microsoft PowerPoint and was saved as MP4 file. Sound quality adjustments, if necessary, and the addition of introductory and end music animations were done using Movavi video editor software. The lecture videos were then uploaded on YouTube for accessibility and the links were given to students through Blackboard.

2.2. Evaluation and data collection

This study was based on a survey of students who experienced online instruction using the DLPCA strategy. The questionnaires were designed with the aim to understand their opinions on chemistry online teaching and learning, if the students are aware of the DLCPA strategy, impact of online strategy on them, and as well as their satisfaction with the online teaching strategy during the COVID-19 pandemic. The survey was made using the google form and composed mainly of Likert scale questions where the participants indicate their level of agreement or disagreement on statements that cover general feedback on the various aspects of the course. The questionnaire is based on a 5-point Likert scale which are as follows: 1 (strongly disagree), 2 (disagree), 3 (neutral), 4 (agree), and 5 (strongly agree). The last section of the questionnaire also invited students’ feedback and sentiments through open-ended questions. To assess the internal consistency of the Likert scale questions, Chronbach’s alpha was calculated (Supplemental Information, SI-1). This measures how well a questionnaire measures a variable based on a set of questions like those in a Likert scale ( Tavakol and Dennick, 2011 ; Glen, 2021 ). Pretesting of the questionnaire was administered to 59 respondents and yielded a Cronbach’s alpha of 59%–88% implying that the questionnaire’s reliability is acceptable ( Taber, 2018 ). Data gathering which took place at the end of the second term for Physical chemistry 1 (May 2020) and Special Term for Analytical chemistry (July 2020). The UST course codes for Physical Chemistry 1 and Analytical Chemistry are CHE 216 and CHE 211, respectively. These course codes were used in the questionnaire. The google form link containing the questionnaire was sent to the students through their university email accounts. Responses were received over a period of one week.

2.3. Data processing

Descriptive statistics using frequency, percentage, and means, were calculated from the responses to 5-point Likert scale questions. Mean response for each item in the construct variables, as well as the overall mean response per construct variable were calculated and then interpreted using the guide shown in Table 1 ( Sözen and Güven, 2019 ).

Interpretation of responses of the Likert-type scale.

For the open-ended questions, we then performed a text mining and word cloud analysis using R software using a package called tm ( Feinerer and Hornik, 2019 ; Feinerer et al., 2008 ). This comes with an available tutorial published by the Statistical Tool for High-throughput Data Analysis website ( STHDA, 2020 ). This package allowed us to determine the most frequently used keywords in the 3 open-ended questions in the survey. We also removed punctuation, common stop words such as “there”, “as”, “and” “the” and non-printable characters such as emojis in the comments metadata. A word cloud was generated to have a visual representation of the data. The word cloud is an image made of words and the size of the word corresponds to how it often appears (frequency) the students answer in the open-ended questions.

Visualisation of the scores in the four quizzes in CHE 211 during the online term was done using box plots. Analysis of variance (ANOVA) was used to compare the scores of the students among the four quiz periods. Post-hoc test using Tukey’s LSD was used to identify which among quiz scores are significantly different. Welch t -test was used to compare the final grade of students between online and face-to-face classes in CHE 211. All tests are performed at 95 % confidence level.

2.4. Participants

The questionnaires were answered by Chemical engineering student majors enrolled in Physical chemistry 1 (N = 77) and Analytical chemistry (N = 91) during the second and special term of AY 2019–2020. The students were informed about the purpose of this questionnaire and were aware that the data would be used only for research and academic purposes. The participants responded in the survey anonymously. The empirical data were gathered and analysed. Initial results showing frequency and percentages of response in each Likert type question were automatically generated by the Google form.

3. Results and discussion

3.1. development of the teaching approach in online classroom instruction in chemistry, 3.1.1. educational theory.

Several factors were considered in designing the appropriate teaching approach for Analytical chemistry and Physical chemistry. One is by evaluating the proper pedagogical model to use. Among the main learning theories, the cognitivism and constructivism approach are deemed to apply best in the online classroom setting. The concept of cognitivism focuses on the stimulation of the student’s learning strategies ( Acevedo et al., 2020 ). It describes the idea that students process the information that they receive and reorganizes them to gain and store new knowledge. This is promoted through practical discussions and problem solving. On the other hand, constructivism focuses on the idea that students acquire new information by building on their previous knowledge and experience through a series of various activities and assessments ( Ripoll et al., 2021 ). In DLPCA strategy, new information is given in a module-based approach wherein the concepts are linked and built from previous modules. The discussions do not only revolve around the technical topic at hand, but also on practical applications or real-world problems. Assessments are given to challenge their understanding and problem-solving skills. These strategies are believed to be enough to provide learnings to students as these methods also address the conception of learning most applicable to this situation. Negovan et al. (2015) found that students, whether in a face-to-face or distant learning setting, highly regard learning as understanding, which incorporates increasing one’s knowledge, memorizing, and applying what was learned. The proposed DLPCA strategy combines these theories and concepts with the goal of maximum learning for the students through its course content, delivery and assessments.

3.1.2. Socio-technical constraints in online teaching and learning

Designing an effective teaching and learning strategy not only requires the study of different pedagogies, but also the consideration of the students’ and instructors’ current social and technical conditions amidst the on-going pandemic. The different constraints and difficulties experienced by students and instructors alike were first identified. The following constraints were considered in designing the DLPCA:

• a) Due to the unpredicted and short notice of lockdown in the middle of March 2020, most students went home and left their textbooks and other learning materials in their school lockers and/or dormitories.
• b) Students may have technical and personal constraints that may prevent them from online learning during the lockdown, such as lack of computers/laptops or other gadgets, lack of stable internet access, power interruptions, lack of quiet and isolated room to study, slow and old computers, non-academic responsibilities within the family, and some students may need necessary medical appointments.
• c) Asynchronous teaching materials must be made accessible for all students. The differences in the availability and speed of internet connection of the students must be considered.
• d) Physical Chemistry and Analytical Chemistry courses involve a lot of calculations which must be properly taught to students. The online delivery of lectures may pose a challenge in effectively communicating concepts and theories to students.
• e) There is an imminent overload of internet networks due to the large number of students doing online learning and most employees are in a work-from-home arrangement. It is therefore necessary to choose a stable, free of charge, and universally accessible platform for online synchronous class discussions. Moreover, this platform must have the following capabilities: (i) call encryption for security, (ii) screen-sharing, (iii) built-in video recording function, and (iv) can be added or synced to calendar.
• f) Slow or unstable internet connections would result in students being frequently disconnected during synchronous lecture discussions. These students may have difficulty joining the session rooms again and add stress to students.
• g) Some instructors are with other family members which may result in disruptions during the class.
• h) Assessment methods must be re-structured to minimize academic dishonesty while still training the students with the required numerical and analytical skills in solving word problems. It is therefore important to create exams that will minimize collaboration or reduce internet searching.
• i) The difficulty of the provided assessment must be balanced with the given time frame. In addition, the time frame must also consider other factors, such as the time needed to scan and save their solutions, and the upload speed of their internet connection. These factors should not be neglected to promote fairness among students.

Table 2 shows how each DLPCA component addresses the different constraints of the online teaching and learning, and the proposed plans to minimize these constraints. The DLPCA strategy combines the use of asynchronous and synchronous techniques of teaching learning.

Alignment of DLCPA components with online teaching and learning constraints and plans to minimize these constraints.

Asynchronous learning promotes a positive learning environment because it allows the students to feel more involved and responsible for their learning progress. However, with this method alone, students cannot get instant feedback and message from the instructor and vice-versa. This may also lead to students feeling disconnected from their instructors and be less motivated. Thus, it is coupled with a synchronous session using a reliable video conferencing platform. This provides a way for a more effective communication between instructors and students, which is important for clarifications, topic emphasis and instructor-student connection, especially during the challenging time of the pandemic.

3.1.3. The role of instructor, student and LMS

Though the pedagogical theories considered in the design of DLPCA are learner-centred, the roles of the instructor and the technology utilized are also important in the online classroom. In a learner-centred approach, the teachers mainly act as guide for instruction and provide the learning direction to students. They provide the necessary tools and resources that will aid in the students’ development of their knowledge ( Owusu-Agyeman et al., 2017 ). Students then must then take an active role in their own learning process and decisions throughout the course.

Meanwhile, the use of technology in modern systems of teaching and learning approaches have already been widely employed. The integration of instructional technology, such as lecture videos, online course delivery and online assessments, has also been found to promote the development of knowledge and skills of instructors and students alike ( McConnell, 2006 ; Burden et al., 2016 ).

3.2. Organization and delivery of learning objectives

3.2.1. revised course plan and checklists.

During the initial shift to online instruction, course syllabi were reviewed and modified accordingly to ensure that students will still be able to complete their course (refer to Section 3.2.4.1 ). The revised course plan delivery contained weekly expectations of lessons, deadlines for submission of tasks, list of online reference materials, and modified grade components and distribution. In principle, the revised course plan delivery provided continuity and steadiness during the abrupt change of instruction.

Checklists are also recommended in the practice of online and flipped classes because students often preferred more structure in flipped classrooms ( Brandon, 2020 ; O’Flaherty and Craig, 2015 ). Therefore, a progress tracker (Supplemental Information, SI-2) was created in addition to the revised course plan. The progress tracker contained the complete list of all topics in the module, the synchronous and asynchronous tasks for each lecture, and the specific topics included for each exam. The students can tick the appropriate boxes whenever they have accomplished the tasks, thus, keeping them on track with the formative and gradable requirements. Infographic-style weekly expectations announcements were also employed and posted in Blackboard and sent to the students through their university email at the beginning of each week. These announcements reminded students of the specific topics, new materials uploaded, and changes in schedule or exams, if any. Overall, these tools of disseminating information provided a substructure for the instructors and students to achieve learning milestones within the agreed period.

3.2.2. Asynchronous teaching and learning

The use of educational videos has shown positive impact to teaching and learning of chemistry even before the full transition to online lectures ( Smith, 2014 ; Christensson and Jesper, 2014 ). All Analytical Chemistry and Physical Chemistry lecture videos are available to students at any time throughout the semester, and they can fully grasp the knowledge by simply watching it at their most convenient time and they can repeat it whenever some concepts were not understood. Thus, lecture videos offer flexibility and convenience on the part of the students and promote active learning by allowing them to replay parts or the whole video and increasing accessibility to students ( Newton et al., 2014 ).

However, one drawback of using lecture videos in the flipped classroom is the fact that students are trusted to independently complete watching the recorded videos ( Eichler and Peeples, 2016 ). If students do not successfully complete this task and make significant learning gains, then the completion of the synchronous session will be more difficult. The effect can be that students will not gain mastery of the intended learning outcomes. To address this potential drawback, problem-solving based SAQs were given at the end of each lecture video to promote the student’s commitment in completing the lecture. Students were required to answer and submit the SAQs as a dedicated exercise that applies the problem-solving skills discussed in the video. In addition, these problem-solving SAQs present prospects for inquiry and personalisation of learning and avoid the passive watching of videos ( Nerantzi, 2020 ).

Flores and Savage (2007) have previously shown that pre-recorded lecture materials aid in achieving a higher student performance and students pay more attention to classes that makes use of recorded lectures. There are plenty of chemistry videos of practically any topic are readily available on the internet. The main motivation of the authors in preparing their own video materials is the advantage of being more personal to students. Studies have also shown that students reported a higher level of engagement and expressed strong preference for multimedia created by their own instructor in an online course ( Xu and Jaggars, 2014 ; Briggs, 2005 ). In fact, some students expressed their appreciation to the authors for the efforts they put in creating the videos. Some students also commented that they like listening to their instructors’ voice especially when they add humour or explain difficult concepts using the local language. However, the weekly preparation of lecture video recordings was found to be a challenging and exhausting task on the instructors’ end. This problem was resolved by effective collaboration and task distribution between the authors in developing the lecture videos and other online learning materials, such as handouts and SAQs for each topic. The concerted efforts helped amplify advantages of online instruction and lessen any drawbacks involved in online delivery.

The Analytical chemistry playlist shown in Fig. 2 (a) contains 11 videos with an average run time of 24 min and the longest video of 50 min and 35 s. There are 30 lecture videos prepared for Physical chemistry 1 playlist as shown in Fig. 2 (b) . The average run time is 15 min with the longest one being 36 min long. Ideally, the lecture videos should be kept short in length to fully engage the students. In this case, longer topics were divided into several shorter videos (i.e., segmentations). The technical know-how in creating lecture videos was the major challenge because the authors are not trained in making videos. The authors had to record their lectures in their own homes, resulting in lecture videos that are not as fancy as those produced with the help of experts. It is noted that lecture videos have a profound impact on how students process and comprehend the content. Therefore, a video editing software was used to further enhance the lecture videos. A close-ended question with “too short/ low”, “just right”, or “too much” option was surveyed regarding the difficulty level, amount of work, and run time for the lecture videos. It is encouraging that most of the respondents in CHE 211 and 216 responded “just right” when asked about the level of difficulty, amount of work, and run time for the recorded videos.

Lecture video playlists in YouTube for (a) CHE 211 and (b) CHE 216. The student responses to the features of lecture videos for (c) CHE211 and (d) CHE 216. Work refers to the time spent in watching the video and answering the SAQs.

The quality of video lectures represents how the video lectures are designed or how it appears to the students ( Lange and Costley, 2007 ). The lecture videos typically start with a 10 s introductory music and a welcome slide to stimulate the attention of the students. Thereafter, the topic to be discussed is introduced and expected learning outcomes are mentioned before proceeding to the actual discussion. A short summary of the lecture is given before the end slide. A common PowerPoint template design, and font type were used to ensure uniformity in all lecture videos for each Chemistry course. Table 3 shows the results on students’ satisfaction in using the pre-recorded lecture videos. Majority of students in CHE 211 (92.3 %) and CHE 216 (97.4 %) strongly agree that the videos clearly stated the learning outcomes (entry 3.1). The calculated mean values for entry 3.1 are 4.44 and 4.68 for CHE 211 and CHE 216, respectively. Most of the respondents also strongly agree that our lecture videos are useful in attaining the objectives of the topic (entry 3.2) in CHE 211 (84.7 %) and CHE 216 (97.4 %). The mean values for entry 3.2 in CHE 211 and CHE 216 are 4.27 and 4.66, respectively. Most of the respondents in CHE 211 (73.7 %) and CHE 216 (91 %) agree that explanations of solutions for sample problems (entry 3.3) are easy to understand. The calculated mean for entry 3.3 is 4.03 and 4.35 for CHE 211 and CHE 216, respectively. Majority of respondents in CHE 211 (86 %) and CHE 216 (92.2 %) also agree that theories and concepts in the lecture videos were clearly presented in the video (entry 3.4). Most students in CHE 211 (71.5 %) and CHE 216 (76.7 %) also agree that there are enough guided problems discussed in the video (entry 3.5). The mean values for entry 3.5 in CHE 211 and CHE 216 are 3.85 and 4.08, respectively. These data suggest that students agree that there are sufficient guided problems discussed in the lecture videos.

Distribution of students’ response to Analytical chemistry (CHE 211) and Physical chemistry (CHE 216) questionnaire on lecture videos reported as frequency, percentage and mean for each entry. The total participant surveyed for Analytical chemistry and Physical chemistry are N = 91 and N = 77, respectively. The response for CHE 216 is shown in blue colour.

Clarity of presentation is essential to ensure student engagement and ultimately learning. Audio and visual clarity of lecture videos is a concern among students in online classes because this can have a negative effect on how students perceive and comprehend instruction ( Molnar, 2017 ; Lange and Costley, 2007 ). The production quality and the delivery of the content by the instructor are crucial for engaging the students. Poor audio and visual quality will ultimately decrease attention and understanding among learners ( Molnar, 2017 ). Hence, a video editing software was used to ensure the images, videos and sound are as clear as possible before using the videos to deliver information. To enhance the audio intelligibility, the voice of the instructor was amplified, and extraneous sounds were removed that might distract students from listening to their instructors’ voice. YouTube has a built-in subtitle function that allows text to accompany the narration and incremental audio and visual speed controls. These features can be used by students depending on their need for the video to be perceived manageable. A close-ended question with the “yes” or “no” option was also surveyed regarding whether visuals and audio recording are clear. Majority of respondents in CHE 211 and CHE 216 answered “yes” when asked if the visuals and audio components in the lecture videos are clear (Supplemental Information SI-3).

It is recognized that the learning environment of students differs from each other as well as the capacities of students in understanding the concepts. Common problems, such as power interruptions, unstable internet connection, and non-academic responsibilities are some hurdles encountered during asynchronous learning. These reasons contributed why some CHE 211 students found it difficult to keep in pace with the asynchronous online learning. The aesthetics, production values, and overall design of lecture videos all influence the learning process ( Lange and Costley, 2007 ; Leacock and Nesbi, 2007 ). Hence, lecture videos were evaluated if they had a positive impact on the learning experience of students. Most of the students in CHE 211 (72.5 %) and CHE 216 (81.8 %) agree that they can describe the important concepts in the lecture video (entry 3.7). This is supported by a mean value of 3.91 (Agree) and 3.97 (Agree) for CHE 211 and CHE 216, respectively. The students in CHE 211 (73.7 %7) and CHE 216 (83.81 %) also agree that they can give an overview of the topic after watching the lecture video (entry 3.8). The mean values for entry 3.8 are 3.97 (Agree) and 4.03 (Agree) for CHE 211 and CHE 216, respectively. The students in CHE 211 (52.8 %; mean = 3.60) and CHE 216 (62.4 %) agree that they can present complex facts illustratively in the lecture video (entry 3.9). This is supported by the mean values of 3.60 and 3.68 for CHE 211 and CHE 216, respectively. Moreover, respondents in CHE 211 (66 %) agree (mean = 3.88) and CHE 216 (87.1 %) strongly agree (mean = 4.23) that they can work independently on typical word problems after watching the videos (entry 3.10). These data show that CHE 211 has a lower mean value for statements 3.8, 3.9, and 3.10 as compared to CHE 216. Again, these slightly lower mean scores were attributed to the 5-week intensive Special Term when CHE 211 was offered. It is highly suggested that enough time is necessary to fully understand the discussions in the lecture video. The mean of entries from 3.1–3.10 for CHE 211 and CHE 216 were calculated as 4.00 and 4.24, respectively. In general, CHE 211 students agree while CHE 216 students strongly agree that our pre-recorded lecture videos are effective in delivering the learning outcomes, engaging, and useful in their online learning. These results emphasize that lecture videos can reduce cognitive load of the students. The underlying premise of the cognitive load theory is that we have a limited amount of memory and overloading with information impedes learning ( Abeysekera and Dawson, 2015 ). Students can watch the videos several times, pause and/or rewind portions of the videos as needed. This student-pacing may aid in better learning by reducing cognitive load ( Esson, 2016 ).

3.2.3. Synchronous teaching and learning

The common misconception about flipped classrooms is that most people think only of videos. Bergmann et al. (2013) and Tucker (2012) highlighted that watching videos is not enough to make flipped learning effective. The collaborative interaction and learning activities that occur during the face-to-face ( Bergmann et al., 2013 ; Tucker, 2012 ) or online setting ( Nerantzi, 2020 ) is very important. Hence, synchronous lecture sessions were conducted using Google meet ( Google Meet, 2019 ) or Zoom ( Zoom, 2019 ). The synchronous meetings were also recorded for those students who were unable to attend the scheduled meeting and those who are struggling with internet connectivity. One of the benefits of the synchronous instruction is that it can provide students a schedule and sense of community. This also allowed instructors to feel the “whole-class” teaching experience and increase communication for instructor – student engagement. The synchronous sessions were dedicated mainly to reinforce difficult concepts and a summary of learning outcomes of the video lectures. During the synchronous sessions, students were asked to present and explain their solutions to their classmates and answer questions as they arose (Supplemental Information, SI-4). This was done to increase student participation and allowed them to present their alternative solutions to a problem. The instructors also made corrections (if necessary) to the solutions or answers that were presented by the students and answered any further questions on the problems. These activities provide an opportunity to devote more time at higher levels of Bloom’s taxonomy ( i.e., applying, analysing, and evaluating) ( Krathwohl, 2002 ).

The instructors have also requested the students to turn on their video cameras during synchronous sessions to promote visual communication. However, most students were unwilling to use their webcams and some reported that their webcams are not working properly. There are several possible reasons for non-video during synchronous meetings and these include: (i) students are shy to show their backgrounds particularly if there are family members present at home; (ii) feeling of not properly dressed or groomed during the synchronous session; (iii) computers have no webcam or the webcam are not working; and (iv) preference of students of being more comfortable with audio-only mode during online synchronous sessions. Therefore, it is difficult to find out whether students are really paying enough attention during the synchronous class. These reasons might have decreased the effectiveness of student-instructor engagement during synchronous online lectures. Therefore, it is advisable that plans should be taken into consideration to promote this vital component in an online class. Based on our personal experience, many students have the tendency to avoid asking questions to instructors in the usual traditional face-to-face classroom. Interestingly, we experienced more questioning from the students either made vocally or through the chat box of Google Meet. It seems that this kind of communication solves the hurdles in asking questions in a traditional lecture class. A possible reason for this behaviour is that students tend to be more active in asking questions when they are not visible in the “virtual” classroom.

The synchronous online lectures in Physical Chemistry 1 were conducted by the individual instructors during the second term of AY 2019–2020, while CHE 211 was conducted through team-teaching in the succeeding term (i.e., Special term, AY 2019–2020). The teamwork of the authors in teaching CHE 211 undeniably reduced the stress and burden of preparing materials for the online classes. In team-teaching approach, each instructor was given a specific set of topics to develop materials and teach synchronously. This arrangement gave enough time for the other instructors to prepare their online materials. The usual online synchronous sessions were taught by the instructor-in-charge of the meeting (i.e., module leader) while the other instructors are also present during the synchronous session (referred as plenary sessions). This arrangement gave the following advantages: (i) peer review of lessons, (ii) best practices of the instructor are shared among colleagues, (iii) standardized lectures were given to all students, (iv) the other instructors were given a chance to add something in the lecture, and (v) other instructors may give their inputs in answering questions from students. This team-teaching approach has been previously shown effective because it allows students to gain new insights from multiple perspectives and critically evaluate these perspectives ( Anderson and Speck, 1998 ; Crawford and Jenkins, 2018 ; Tan et al., 2020 ). CHE 211 students reflected their appreciation towards this type of teaching approach during the survey and some of the comments are shown below:

“The whole plenary sessions for me is the most useful thing. I can ask questions that can benefit not just me but the whole batch too and vice versa.” “At first, I had doubts doing the plenary session since all of the ChE students in my batch would be there and perhaps may be difficult to handle since it was a 3:100 ratio of instructors to students. However, it was a great experience getting to know my future colleagues, as well as the three instructors as I learned different sets of viewpoints from them, which in turn, helped me during this short term, may it be academic related or life-related.” “One of the best features is that 3 instructors are able to provide input from their experiences in the industry giving the lesson clarity, and it makes it more interesting and motivating to hear these from professionals.”

The students were also asked in general of their experience of this synchronous teaching strategy (Supplemental Information, SI-3). Majority of students expressed that the instructors managed the team teaching effectively (94.5 %) and the plenary sessions provided a welcoming, interactive, and engaging virtual classroom (92.3 %). It is expected that large class size can increase the barriers related to student anonymity and passivity ( Eichler and Peeples, 2016 ). However, Hoyt et al. (2010) highlighted that teaching a large enrolment course can be a very engaging and productive learning experience for students and a rewarding experience for the instructor through effective classroom management, careful planning, and ingenuity. The experience in teaching synchronous sessions led the authors to realize that it is important to connect with students through video streaming and frequently ask questions to gauge student’s attention and learning. Moreover, it is also important that students present and discuss their solutions to problems to further increase student-teacher interaction.

3.2.4. Assessments and learning outcomes (LOs)

3.2.4.1. change in course assessments and alignment with los.

In most chemistry courses, assessments were originally given as exams, in-class group presentations, and individual problem sets. Problem sets are regularly given to students because solving relevant problems is indispensable to the understanding concepts, practice of numerical skills, and deepening knowledge of chemistry. Problem sets are referred here as self-assessment questions (SAQs) and module exams were the primary assessment tools employed in online CHE 211 and CHE 216. The number of items usually given in SAQs and the time-involvement are comparable to those in face-to-face lectures. This is to ensure the effectiveness of assignments would not be different. The SAQs were similar to the guided problems discussed in the lecture videos and were selected to fulfil the intended learning outcomes (LOs) of the module. At the very least, students were expected to watch the pre-recorded lecture video and answer the SAQs.

All chemistry courses offered to Chemical Engineering students used to have at least two major exams in a semester, i.e., preliminary exam and final exam and several quizzes. Now that assessments should be given online, academic integrity is one of the concerns of faculty members. In the case of CHE 216, it was decided that preliminary and final exams were replaced by module quizzes as everyone was still adjusting to the online instruction and provide more time for students to understand the lessons. The same decision was made for CHE 211 because of the short and intensive 5-week period during the special term of 2020. The assessments and learning outcomes before COVID-19 (i.e., face-to-face) and during COVID-19 (i.e., online) for CHE 211 are summarized in Table 4 . Table 4 shows how the LOs are aligned with the given assessment before and during COVID-19. The assessments with their corresponding weightings to the final grade before COVID-19 special term (AY 2018–2019) include quizzes (40 %), SAQs (10 %), preliminary exam (25 %) and final exam (25 %). However, assessments during COVID-19 special term (AY of 2019–2020) only included exams (70 %) and SAQs (30 %). Module 1 and Module 2 contained a large number of topics and were divided into smaller quizzes. There were 4 computational exams given and 2 conceptual assessments given in the form of a quiz bee.

Alignment of Assessment with LOs in Analytical Chemistry (CHE 211) before and during COVID-19. Before COVID-19 is based on the course syllabus for Special Term of AY 2018–2019 while During COVID-19 is based on the revised course syllabus for Special Term 2019–2020. Legend: Fully Consistent (●), LO not Delivered (⊘), blank (no assessment conducted).

Table 5 summarizes the alignment of assessments and change in LO’s for Physical Chemistry during the 2nd Term of AY 2019–2020. The class suspensions at the start of community lockdown led to less lecture hours in CHE 216. Hence, the instructors decided to transfer module 4 (with LO-4 and LO-5) to next the Physical Chemistry course. The assessments in CHE 216 include 3 module quizzes and SAQs. The first exam and SAQs was completed in regular classroom set-up before the community lockdown and the other two exams were completed online. Each online quiz was scheduled and conducted asynchronously. To minimize cheating, each student received a unique set of questions for the other two exams with a similar level of difficulty for each question set. A solution template was also provided where they can discuss their plan on how to solve the assigned problem and show the detailed calculations. Their solutions were submitted through specific submission links in the Blackboard portal before the deadline. It was expected that this strategy decreased the feasibility of cheating because each student must give a unique plan to solve the problem and solution. The original percentage of each component module for Physical Chemistry 1 (CHE 216) were module 1 (25 %), 2 (25 %), 3 (30 %), and 4 (20 %). The shift to online instruction necessitated an adjustment in the module weights. The corresponding revised module weights were module 1 (35 %), module 2 (35 %) and module 3 (30 %).

Alignment of Assessment with LOs in Physical Chemistry (CHE 216) before and during COVID-19. The before COVID-19 refers to the course syllabus before community lockdown during 2nd Term AY 2019–2020 while during COVID-19 is based on the same term after switching to online instruction. Legend: Fully Consistent (●), LO not Delivered (⊘), N/A (Not Applicable), blank (no assessment conducted).

3.2.4.2. Student survey on assessment and learning outcomes

The student experiences in accomplishing the assessments were examined. Table 6 shows that most of the students in CHE 211 (75.9 %) agree (mean = 4.07) and majority of students in CHE 216 (94.8 %) strongly agree (mean = 4.55) that the number of SAQs is enough to achieve the declared learning outcomes of the module (entry 6.1). However, only 52.8 % of students in CHE 211 agree (mean = 3.49) that they can easily answer the SAQs after watching the videos compared to those who strongly agree (mean = 4.31) in CHE 216 (89.7 %) (entry 6.2). The somewhat lower mean for CHE 211 might be due to the limited time for students in CHE 211 to fully understand the videos and apply the problem-solving skills discussed in the guided problems. Only 60.5 % of CHE 211 students agree (mean = 3.75) that there is enough time to answer the SAQs as compared to those who strongly agree (mean = 4.51) in CHE 216 (93.5 %) (entry 6.3). Unfortunately, it is counterproductive that some students find SAQs as mere requirements rather than authentically assessing their learning gains. Our survey suggests that students must be given enough time to watch the videos and a longer period of submission of SAQs. In this manner, students will realize the importance of SAQs in achieving the desired numerical solving skills rather than simply submitting the SAQs as a gradable component.

Distribution of students’ response to Analytical chemistry (CHE 211) and Physical chemistry (CHE 216) questionnaire on Assessment type and strategy reported as frequency, percentage and mean for each entry. The total participant surveyed for Analytical chemistry and Physical chemistry are N = 91 and N = 77, respectively. The response for CHE 216 is shown in blue colour.

The students were also asked whether the adjustments in the number of exams are enough to assess the student learning and understanding of the course (entry 6.4). Most of the respondents in CHE 211 (69.3 %) agree (mean = 3.89) and majority of respondents CHE 216 (94.8 %) strongly agree (mean = 4.57) that there are enough exams. However, 13.2 % of students in CHE 211 disagreed on this statement and expressed that additional exams should have been given (entry 6.4). We decided to give only 4 module exams within the 5-week special term of AY 2019–2020.

In CHE 216, quiz 2 and 3 were conducted asynchronously with a recommended 24 -h window for the submission of answers to allow students wider access, especially those who may have limited internet connectivity. Although internet connectivity within Metro Manila is good, it is not clear if the same situation exists in other regions of the country. Nonetheless, the majority of respondents in CHE 216 (93.5 %) strongly agree (mean = 4.47) that timed-release and submission of quizzes for Physical chemistry 1 is a good way to train their problem-solving skills (entry 6.5). The wide time frame for the online submission was also an attempt to mitigate the reduced access to Blackboard online submission from students currently staying in other regions of the Philippines with intermittent internet connections. However, a wide asynchronous window period might pose academic integrity issues. Limiting the time of unsupervised assessment format restricted the amount of time for any potential collaboration. This learning experience was applied in giving assessments in CHE 211 in the succeeding term.

A total of four quizzes were given synchronously for CHE 211. In the first quiz, six problem solving questions were given to each student to answer in 60 min. These problems were given in three consecutive batches with 2 problems and 20 min per batch. To promote academic integrity, the instructors modified the dissemination quiz questions for the succeeding quizzes. Two problem solving questions were still given per batch, however, nine different sets were deployed. To ensure the same level of difficulty, only the given values and questions were rephrased. At the request of students, the time allotted per batch was increased to 30 min to account for the time used for downloading the questions and uploading the answers. These modifications, despite prolonging the time allotment per batch, resulted in a significant decrease in student performance for Quiz 2 (Supplemental Information 9, SI-9). Interestingly, the students were able to positively accept the adjustments for Quiz 3 and Quiz 4, resulting in significant increase in student performance for both quizzes as revealed by ANOVA analysis (Supplemental Information 9, SI-9).

Even with time adjustments, 57.2 % of CHE 211 students agree (mean = 3.59) that the timed-release of the exam questions provided good training to develop their problem-solving skills (entry 6.5). The survey suggests that sufficient time is important in assessing the performance of students in courses requiring intensive numerical calculations. The concepts and theories in Analytical chemistry were assessed using a quiz bee (entry 6.6). The motivation of doing this activity is to promote student-student interaction and provide an environment for active student participation. Most of the respondents in CHE 211 (87.9 %) strongly agreed (mean = 4.52) that assessment of concepts through online game (i.e., quiz bee) provided a fun and stimulating environment. However, some respondents found this assessment strategy neither effective (6.6 %) and some students disagreed (5.5 %) that quiz bees are effective in assessing the concepts learned. This observation was attributed to the various preferences of students on the type assessment. Another possible reason is that some students have unstable internet and affect their ability to quickly send their answers during the quiz bee. Overall, the majority of students in CHE 211(Mean = 3.88) students agree and CHE 216 (Mean = 4.48) students strongly agree that our self-assessment questions SAQs and exam strategy is sufficient and effective in assessing the understanding of the students of the topics in both calculations and theory.

4. Analysis of DLPCA teaching-learning experience

4.1. impact on student learning experience.

The perception and satisfaction of students regarding their DLPCA experience is discussed in this section. It is important that online teaching and learning strategy is laid-out and clearly discussed to the students. In terms of percentage, majority if not all the respondents in CHE 211 (91.2 %) and CHE 216 (100 %) agreed that there was a clear plan (entry 7.1) on how the courses were converted into an online class. The mean values for entry 7.1 are 4.47 and 4.70 for CHE 211 and CHE 216, respectively. The regular posting of tasks and deliverables to students further helped them understand the overall structure of the strategy, thus, resulting in a better learning process. This feedback is important because this will allow students to set their expectations in the new learning environment and will give them an impression of order and continuity ( Table 7 ).

Distribution of students’ response to Analytical chemistry (CHE 211) and Physical chemistry (CHE 216) questionnaire on the structure of online instruction and student attributes reported as frequency, percentage, and mean for each entry. The total participant surveyed for CHE 211 and CHE 216 are N = 91 and N = 77, respectively. The response for CHE 216 is shown in blue colour.

Majority of the respondents in CHE 211 (93.4 %) and CHE 216 (96.1 %) agreed that they had received a clear set of instructions for the weekly tasks expected from them (entry 7.2). The calculated mean for entry 5.2 were 4.63 and 4.65 for CHE 211 and CHE 216, respectively. This feedback is also important because this will provide students an overview of their weekly tasks, thus, giving them the chance to manage and make use of their time more efficiently. The most significant difference between online and traditional classrooms is that students and instructors cannot see and communicate with each other face-to-face. Hence, DLPCA combines a balance of synchronous and asynchronous components to engage the diverse personalities of students in a more inclusive way and maximizes opportunities for self- and guided learning. Most of the respondents agreed that the DLPCA strategy is balanced (entry 7.3) with a mean of 4.11 and 4.65 for CHE 211 and CHE 216, respectively. The term “balanced” refers to having sufficient and complementing mixture of asynchronous (lecture videos and SAQs) and synchronous (online discussion and consultation) teaching strategies. In addition, the mean values were determined for entry 7.4 to be 4.32 and 4.48 for CHE 211 and CHE 216, respectively. These data suggest that students strongly agreed that the synchronous component allowed them to easily express their feedback, concerns, and ask questions about the lecture materials (entry 7.4).

The balanced blended approach can help students establish active learning habits such as proactiveness (entry 7.5). The mean values for entry 7.5 were calculated as 4.27 and 4.29 for CHE 211 and CHE 216, respectively. These results suggest that the DLPCA strategy enabled students to develop a desirable active learning habit. The balanced online strategy also increases students’ sense of responsibility for learning (entry 7.6). The calculated mean values for the entry 7.6 are 4.42 and 4.35 for CHE 211 and CHE 216, respectively. Some of the students’ comments in CHE 211 and CHE 216 related to the balanced online learning strategy are presented below:

[CHE 211] “The best thing about the online learning strategy is the balance between synchronous meetings and asynchronous videos. Since the pre-recorded videos are sharp and concise, they can be repeated multiple times before the synchronous meeting can start. This way, I can better understand the lesson and prepare for the meeting, but still anticipate for the synchronous session in order to gather more detailed information about the topics.” [CHE 211] “The best thing was the asynchronous and synchronous discussion for the lectures because there is balance with self-paced learning and synchronous learning.” [CHE 216] “Asynchronous and synchronous lectures were balanced which is good especially when there comes a technical difficulty particularly the poor internet connection.”

The general acceptance and satisfaction of the students regarding the DLPCA learning strategy was emphasized in entry 7.7. Majority of students in CHE 211 (72.5 %) and CHE 216 (96.1 %) are satisfied with the online strategy. The calculated mean for entry 7.7 were 3.91 and 4.48 for CHE 211 and CHE 216, respectively. A relatively lower agreement was observed among CHE 211 students which may be attributed to the short intensive period of the Special Term resulting in shorter time allotted for accomplishing tasks and studying the lessons. This might have affected the understanding and appreciation of the topics in CHE 211, placing the students in a very stressful situation. In the case of CHE 216, the first half of the semester was conducted in face-to-face instruction (before the community lockdown) and the second half of the term was conducted online. The longer period of the second term (5 months) has spread the workload of students in CHE 216 resulting in a very high acceptance of the online strategy. The overall mean of entries from 7.1–7.7 for CHE 211 and CHE 216 were calculated as 4.23 and 4.53, respectively. These suggest that, in general, students in both courses strongly agreed the DLPCA strategy has a clear laid-out plan, has provided a balance of synchronous and asynchronous components, has promoted active learning habits, and has been accepted by the students as an alternative to face-to-face setup. One possible reason for the high acceptance among students is that they were able to establish a routine towards the end of the semester. DLPCA provides a cohesive strategy where students know what to prepare before going to class and are reassured knowing that any questions that they would have will be answered during the synchronous sessions.

Open-ended questions regarding the students’ general impression of the DLPCA strategy were examined using word clouds. Word cloud generates an image containing the most frequently used words from the comments being analyzed – the more frequently the word is used, the larger it will appear in the image ( Bletzer, 2015 ). It is possible to look for specific patterns of words and phrases, or the lack thereof, in any text data by simply examining frequencies in a word cloud. Further interpretations of the word cloud can be carried out by detailed analysis of the responses ( DePaolo and Wilkinson, 2014 ). Three themes related to the learning experience were identified, i.e. (i) the best experience in the online course, (ii) worst experience in the online course, and (iii) suggestions to improve the online course.

4.2. Theme 1: best experience

The word cloud of the feedback received from CHE 211 and 216 students are shown in Fig. 3 a and b , respectively. The following three major topics emerged for CHE 211: (1) questions, (2) videos, sessions, lessons, and (3) asynchronous, strategy, lecture, instructors, more, time. The frequency table and graph for best experience are presented in Supplemental Information SI-6. The word “questions” was mentioned frequently because students can easily raise their questions and instructors can entertain all their questions during synchronous sessions. The second major topic includes words like “videos”, “sessions”, and “lessons”. The production of pre-recorded videos was appreciated by the students as it makes online learning easier. Further clarifications and explanations for complex lessons were done during the synchronous discussions. The third major topic included words such as “asynchronous”, “strategy”, “lecture”, “instructors”, “more”, “time”. The respondents were optimistic as they enjoyed the learning strategy and emphasized the efficiency of content delivery and the ability to control the pace of learning. The students also emphasized the enthusiasm as well as the positive attitude of the instructors that was reflected throughout the recordings.

Word cloud analysis on the best experiences in the online teaching and learning in (a) CHE 211 and (b) CHE 216.

Four major topics emerged for CHE 216: (1) learning, (2) lecture, (3) videos, asynchronous (4) students, time, understand, synchronous. Like CHE 211, students expressed that their best experience in online learning is the availability of pre-recorded lecture videos which is an essential component of asynchronous learning. Students also appreciate the synchronous sessions because it provided a platform to clarify difficult topics that were discussed in the video. Students also like the amount of time made available to them in the course. The blended learning strategy allowed them to manage their time well and understand the topics in CHE 216. Overall, analysis shows the positive impact of using pre-recorded video lectures in online learning depends on good planning and balanced integration of asynchronous and synchronous components. However, it should be noted that video lectures are not alternative options to face-to-face setup, but an essential supplementary tool in achieving the learning outcomes of the modules in online learning.

4.3. Theme 2: worst experience

Three major topics emerged in the word cloud for CHE 211 on the student’s worst experience ( Fig. 4 a). Among these responses included (1) quizzes/quiz, (2) time, and (3) internet. The word “time” refers to the insufficient amount of time allotted for quizzes. The frequency table and graph for worst experience are presented in Supplemental Information SI-7. Some students in CHE 211 expressed their frustration that exam time is too fast-paced. The exam conditions gave them an impression of being rushed to analyze, answer, and upload their solutions. Although the exam questions were prepared to be similar to the one discussed in the online session, some students still find answering the quizzes (i.e., exams) stressful because the difficulty level of the questions are different from the ones discussed in the guided problems and SAQs. Several students were also affected by unstable internet connection in CHE 211 online class. Interestingly, the word cloud for CHE 216 ( Fig. 4 b) showed the most frequent keyword “none” for their worst experience. Most students appreciated the online strategy and commended their instructors for providing course materials that were sufficient to understand the topics fully. “Internet” and “time” were also the second most frequent words in the feedback. Internet connectivity issues which affected their participation during synchronous sessions and their timely submission of SAQs also contributed to the worst experience of students in their online CHE 216 course although these were mentioned to a lesser extent.

Word cloud analysis on the worst experiences in the online teaching and in (a) CHE 211 and (b) CHE 216 courses.

4.4. Theme 3: suggestions for improvement

The students were also asked about how they would like to experience their online classes in the succeeding semesters. The goal is to determine the enhancements to the DLPCA strategy and to make the students’ learning experience more satisfying. The major topics that emerged in CHE 211 are (1) “more”, (2) “quizzes”, “synchronous” (3) “think”, (4) “time”, “lecture”, “problem”. The frequency table and graph for suggestions for improvement are presented in Supplemental Information SI-8. Most students conveyed that more quizzes, diverse guided problems during synchronous discussion and other forms of assessments must be included to compensate for low scores in their exams. Students also expressed their concern regarding the time devoted in watching the lecture videos and submitting SAQs. Specifically, flexibility in terms of extending the deadlines of SAQs for a day or two would be ideal. The words like “professors” and “instructors” also appeared in the word cloud because the students are appreciative of their teachers’ efforts in their online class ( Fig. 5 ).

Word cloud analysis on further improvements in the online teaching and learning in (a) CHE 211 and (b) CHE 216 courses.

The word cloud for CHE 216 showed the following 2 major topics (1) “course”, “professor”, “time” (2) “learning lecture”, “videos”, “strategy”, “good”. Some students expressed that additional problems must be given, and submission deadlines of assessments must be flexible. In general, there is high satisfaction of the DLPCA strategy among CHE 216 students. The students also acknowledged that CHE 216 course provided a clear structure during the quick transition to online instruction. Students expressed their desire to continue with the DLPCA strategy and credited their teachers for the commendable efforts made in their online class.

4.5. Impact on student performance

To further investigate the impact of DLPCA on student performance, the grade distribution for Special Term 2018–2019 (face-to-face) and Special Term 2019–2020 (online) for CHE 211 were compared and summarized in Fig. 6 . Students are given a 5-point numerical grade which corresponds to 1.00 as the highest and 5.00 as the failed grade at the end of the semester. The grade WP corresponds to those students who withdrew with permission while the grade INC corresponds to incomplete. A grade of INC is given if a student failed to take the final examinations or to submit a major requirement of a course on account of illness or other valid reasons ( UST Student Handbook, 2018 ).

Comparison of the grade distribution, as a percentage of students earning each grade, for the online group (3 sections, n = 98) and the face-to-face group (3 sections, n = 121) in Analytical chemistry.

Although the assessment weightings were different in the face-to-face and online semester, the content and variety of questions stayed the same. Interestingly, the final grade distribution during the online Special Term rendered a comparable grade distribution in face-to-face Special Term. The most evident changes can be seen from the grades 1.75, 2.25 and 5.00. The percentage of students who got “1.75” nearly doubled (online = 16.3 %, face-to-face = 9.1 %) while those who got “2.25” more than doubled (online = 25.5 %, face-to-face = 11.6 %) in the online setting. On the other hand, the percentage of students who got “5.00” or failing grade became significantly lower (online = 4.1 %, face-to-face = 18.2 %) in the online setting. Interestingly, no student was given a grade of “WP” or “INC” in the online flipped classroom. These trends in the grade distribution could indicate that the DLPCA strategy positively impacted the students’ performance. To further verify the observed changes from the grade distributions between online and face-to-face, Welch t -test was used to analyse the data. Results (p = 0.0002962 and p = 0.00306) showed that online grades are indeed higher than face-to-face grades (Supplemental Information 10, SI-10). Unfortunately, the grade distributions between online (i.e., 2nd Term, AY 2019–2020) and previous face-to-face classes in CHE 216 cannot be compared. The community lockdown happened in mid-March 2020 which resulted to combination of face-to-face and online instruction for CHE 216. Hence, the performance of students during 2nd term cannot be assumed the same for the previous face-to-face classes.

4.6. Instructor observation

The instructor’s observations can be used to provide information on the effectiveness of flipped classroom in a qualitative perspective ( Fautch, 2015 ). Instructors also reflected upon their experience while transitioning to online instruction and how DLPCA strategy played an important role in continuing chemistry education during the COVID-19 pandemic. One of the positive outcomes using the DLPCA strategy was the introduction of new technological teaching tools for the instructors. The switch to online instruction resulted in all instructors utilizing synchronous video conferencing tools, online assessments tools, and pre-recorded lecture videos. These changes have the potential to have long term positive impacts on instruction. Specifically, the production of self-made lecture videos, although a time-consuming process, can be a permanent teaching tool. The pre-recorded lecture videos will certainly be useful for the next semesters and will be a part of other innovative learning activities.

The transition to online learning also presented a big challenge to decide which online technology is best suited for lectures. It is very easy for instructors to be overwhelmed by the sheer number of educational platforms and online resources available. However, the DLPCA strategy streamlined all available online resources into an organized strategy. The DLCPA strategy also involved collaboration and delegation of workload (e.g., creating video lectures, construction of new activities, team teaching) among instructors which led to higher-quality learning materials. Additionally, the exchange of ideas helped instructors better plan for giving assessments.

Through the instructors’ perspective, the DLPCA strategy also showed great impact on the students’ learning. Online education has resulted to different kinds of difficulties which has somehow affected the progress of students in understanding the topics in their lecture courses. The implementation of flipped classroom learning is expected to prepare the students to participate in more interactive learning activities that require higher-order cognitive skills ( Cowden and Santiago, 2016 ). Another great benefit for the DLPCA strategy is that synchronous sessions were recorded and uploaded in Blackboard for their exclusive use, and these capture the instructor’s presentation, class discussions and the participations as they occur. The availability and accessibility of the videos is considered to have a positive effect on student learning as no student requested to repeat explanations on complex topics presented in the videos. Comparing to previous semesters where students usually ask instructors to clarify difficult concepts and calculations, this shows that DLPCA offers effectiveness, flexibility, and convenience to online learning.

Regarding the completion of SAQs, all students completed the task properly which may be because SAQs were also required as a gradable assessment. In previous semesters, the solutions to SAQs were primarily discussed by the instructor in the classroom. During the online term, students were randomly asked by the instructor to share their calculations during synchronous sessions. This activity trains students to extract information from the SAQs, organize solutions, communicate their knowledge, and develop a deeper level of thinking. Interestingly, some students would raise questions on the solutions of their classmates which encourages the exchange of ideas between students. Lastly, the team teaching conducted by the instructors had a positive effect on the students. The presence of all instructors during the class sessions allowed students to gain insights from each instructor.

5. Concluding remarks

The COVID-19 pandemic has opened venues for online teaching with a completely new outlook for educators and learners. Online education requires teachers to change from the old teaching paradigm to new teaching methods that also matches with technology. Consultation with students regarding the teaching style is important to check if the students are keeping up with the lecture and helps identify various aspects of online teaching that needs to be adjusted accordingly. This paper presented the DLPCA strategy that paved the way for transition from traditional face-to-face to online instruction during the pandemic. DLPCA consists of asynchronous learning using pre-recorded videos and synchronous session of live exchanges. The major lessons of using DLCPA strategy during the lockdown were (i) asynchronous teaching using lecture videos allowed students to progress at their own pace because they can repeatedly watch the videos at any time, (ii) checklists such as progress trackers and weekly guides helped students organize and manage their tasks, and (iii) asynchronous assessments were effective in addressing problems with slow internet connectivity. However, preventive measures must be in place to prevent unauthorized student collaboration and internet searching. In addition, the benefits of DLCPA outweighs the costs in time associated with the preparation of pre-recorded lecture videos. The various insights and results discussed in this paper could be adapted for designing synchronous and/or asynchronous components of online, flipped, or hybrid classes. In addition, DLPCA strategy can be applied in future events such as disruption of classes due to inclement weather conditions, and emergency situations when a faculty member cannot be physically present in a classroom due to health reasons.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The authors are grateful to the university administration for their support in providing trainings in online instruction. The authors are also appreciative to Dr. Edsel B. Calica for contributing materials and inputs for the learning theories.

Appendix A Supplementary material related to this article can be found, in the online version, at doi: https://doi.org/10.1016/j.ece.2021.01.012 .

Appendix A. Supplementary data

The following is Supplementary data to this article:

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Modular distance learning modality: Challenges of teachers in teaching amid the Covid-19 pandemic

2020, Research

This study aimed to identify the challenges of teachers in the use of modular distance learning modality amidst pandemic and how teachers cope with these challenges. This study is a qualitative research which employed the phenomenological research design to determine the challenges encountered by teachers in the use of modular distance learning modality. The study was conducted among teachers in different public secondary schools within Tacloban City. Ten (10) professional public secondary teachers were approached to request their voluntary involvement as key participants through convenience sampling. The personal experiences and coping mechanisms of the teachers were gathered through a survey, particularly by using a semi-structured questionnaire with open-ended questions. Colaizzi's method was used in the interpretation of data. The challenges of teachers were identified based on how they plan, prepare and distribute modules, monitor students' learning, check, evaluate outputs, and provide feedback on students' performance. Furthermore, teachers used various ways to cope with the challenges encountered in modular distance learning modality such as time management, innovating teaching strategies, adapting to the changes brought by the new normal trend in education, being flexible, providing alternative plans, being optimistic, patient, and equipping oneself with the necessary skills for the new normal ways of education. Various stakeholders need to work and plan for alternatives on different issues that may arise as they are involved in the teaching-learning process considering all the limitations in these trying times brought by the pandemic.

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Due to the COVID-19 pandemic, schools, particularly in rural areas, employed Modular Distance Learning (MDL) to ensure educational continuity. Modular distance learning is the current learning modality of primary education, where parents serve as parent-teachers to their children. This study seeks to evaluate the experiences of students and teachers of Elementary School, on modular distance learning during the pandemic. This study used the qualitative method of interviewing nine students and six teachers to learn about their MDL experiences. Data process involves combining related concepts and themes to produce a more structured and organized picture of the data. MDL strengthens family bonding, promotes independent learning, and economizes money and time. However, it is an additional workload for working parents; there needs to be more teacher-student interaction, preventing pupils from socializing and gadget distractions. The article revealed that MDL has positive and negative experiences for teachers and students. Therefore, the impact may vary depending on individual circumstances and adaptability. The study suggests that suitable strategies should address any challenges during implementation and evaluation. Furthermore, teachers must undergo training related to MDL to address existing problems in delivering their lessons.

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The main objective of the study was to determine the challenges of the secondary school teachers and coping mechanisms in Modular Distance Learning in Loon Districts. The study also sought answers on the difference between the profile such as sex, age, educational attainment, type of school connected and years of teaching of the respondents and the challenges of the secondary school teachers and coping mechanisms. The descriptivecorrelational survey method was utilized using the adopted questionnaire for data gathering, and measurement. The locale of the study was all public and private secondary school teachers in Loon North and South District, Loon, Bohol. The participants included two hundred (200) teachers. The statistical tools used in the study were percentage, weighted mean, chi square of independence and Pearson's product moment correlation. The findings showed that there was no significant difference between challenges and coping mechanisms in relation to sex, age, educational attainment, type of school connected and years of teaching profile of the teachers. It was found out that there was no significant difference between the challenges and coping mechanisms of the respondents in terms of their profile. Meanwhile, there was a significant correlation between the challenges and coping mechanisms in modular distance learning in terms of positive well-being, time management, openness to change, and peer mentoring while no significant correlation between the challenges of the secondary school teachers and coping mechanisms in terms of collaboration. Meanwhile, there was a significant correlation between the overall challenges and overall coping mechanisms of teachers in modular distance learning. It was concluded that better or strong application of coping mechanisms, the lesser challenges of the teachers experienced in Modular Distance Learning. It was recommended that formulating action plan for coping mechanisms in meeting the challenges of Modular Distance Learning can be a great help for the teachers to continue to provide quality education to the students amidst the global pandemic.

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Teachers began preparing for modular and interactive distance learning under the presumption that teaching would continue regardless of the situation. Teaching is attainable, but there are difficulties as well. This quantitative study was to investigate and describe how teachers coped during the COVID-19 pandemic, including innovative practices used by teachers to defeat or simply overcome the difficulties and challenges of the modular teaching and learning approach. The study employed a descriptive-correlational research design and utilizing a purposive random sampling technique in selecting the respondents of the study. There was a total of 50 public school teachers across the study's locale who responded to the conduct survey, which set the research sample size limitation. The findings of the study showed that the significant relationship between the innovative practices and coping mechanisms of the educators and their ability to overcome the difficulties and challenges of the modular instruction and learning approach during the COVID-19 pandemic had the overall correlation coefficient of 0.642 with a sig. (2-tailed of 0.397). Teachers must fortify their hearts to carry out their work and responsibilities despite the greater circumstances. To succeed and develop into a frontline teacher who is effective, they must devote all their effort.

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This research investigated the parents and learners’ perceptions on modular distance learning that they are experiencing during this time of pandemic as part contemporary new normal education setup. The main purpose of this study was to understand parents’ and learners’ perceptions on modular distance learning as contemporary teaching strategy and how they coped with the experiences and challenges of the new normal education settings. The participants of this study where five (5) senior high school learners and five (5) parents/guardians of senior high school learners of Sta. Catalina Integrated National High School. The research was conducted in Majayjay District from School Year 2020-2021. This study used Qualitative Research through Descriptive research where in-depth interviewing and storytelling was done to gather the narratives or accounts of the research participants. Using an interview protocol and with a strong collaboration with the participants, the researcher will manage...

Psychology and Education , ROMEL LAGRIO

The education sector was greatly affected by the global health crisis of COVID-19, resulting in massive changes in our education setup , which contributed to various problems and challenges encountered during the implementation of the modular distance learning modality. This study aimed to determine the strategies and challenges encountered by teachers in implementing modular distance learning and its impact on students' academic performance. A descriptive research design was employed. The researchers utilized an online survey method for data gathering. A total of 60 teachers and 187 selected Grade 7 learners were the study's respondents utilizing total enumeration for teachers and stratified random sampling for learners.The study's findings show that teachers could employ strategies such as setting a submission schedule and creating a group chat with the learners. Moreover, establish the appropriate health and safety protocols and safety nets for learners against violence and abuse at home and in the community, and train school personnel for the Learning Delivery Modality (LMD).On the other hand, teachers professed that printing modules were time-consuming, the distance of the learner's home from the school hindered the teachers in providing technical assistance, and learners needed help following instructions. Parents answered the modules of the learners. The need for printing materials was a significant challenge.Most of the student's grades during the first quarter were within the range of 80-84, which was considered a satisfactory academic performance. Moreover, the results signified a negligible negative correlation between teachers' strategies in implementing modular distance learning and students' academic performance. The study suggests revisiting the school's plans for implementing modular distance learning and strengthening the partnership of the school, parents, and stakeholders.

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Essay on Distance Learning Module

Introduction

Distance education involves education done across the globe at any time and location. Learners can learn at their own leisure without involving instructors during the training. In contemporary times, businesses are advancing towards distance learning and integrating it with e-learning through learning instructor.

Describe the information conveyed to trainees in the distance learning module.

Distance learning modules comprise various interactive audio-visuals aids to engage the learners always. Usually, audio-visuals are helpful since they stimulate the learner and aid in reinforcing the thoughts presented (Martin, 2018). For learners to grasp an enhanced understanding of the sexual harassment training, they require a collaborative audio-visual format to help to keep their attention and providing visuals to give examples. For successful learning, engagement must be ensured in distance learning environments. The internet facilitates distance learning by enabling users to access videos from their locations and facilitating presentations.

A module can consist of 10 phases were the initial phase id to register for the class and access course materials. From phase 2 to 9, information describing workplace sexual harassment would be availed to trainees. They would learn what sexual harassment entails, company policies about it, how to report sexual harassment and collaborative scenes for question and answer. A pass mark of 80% may be set for trainees, and upon completion, trainees would be required to take a survey for feedback.

Articulate how a distance learning module fits into the overall training program design

In the contemporary digital age, eLearning has gained popularity due to its simplicity and effectiveness in delivering learning outcomes. Currently, the world is fighting the global pandemic of COVID-19; thus, many schools have been closed. However, in most institutions, learning is still ongoing through online platforms like Cengage. Distance learning modules are adaptable and flexible for off-site personnel to finish training without the need to drive to classes. For instance, a company with thousands of employees can offer a single training within a single timeline. Victims of sexual harassment can undertake the training at the comfort of their desks, thus protecting their privacy. The overall success rate of implementing eLearning in an organization is high as training improves, and costs associated with classroom-style decreases (Martin, 2018). Trainees can also take the course multiple times to gain a better understanding and further insights they missed during initial training.

Assess the strengths and weaknesses of distance learning on an organization’s training strategy

Distance learning is available anywhere and enables learners to participate in excellent quality training conditions. Learners can participate from any location through the internet. A trainee can complete the training based on their schedule since eLearning is available throughout. There is also an opportunity to review the information learned at a later time. eLearning promotes collaborative learning as students can take part in discussions and review other’s work (University of Illinois, 2019). Besides, it eradicates content deviations, which may be attributed to different trainers. Thus, all trainees received exact content through eLearning.

Instructors must compensate for physical absence by developing a collaborative environment in eLearning. Students must feel comfortable contributing, particularly if the facilitator is accessible. Failure to achieve this can lead to alienation between the instructor and trainees. Also, not everyone may have access to the internet or computers. Others may be computer illiterate, which may hinder learning outcomes (University of Illinois, 2019). Students are expected to complete the module according to their schedule. Those with prioritization issues and bad time management may find themselves rushing during the last minute hence gaining limited content.

Illustrate how practice and feedback are incorporated to measure the transfer of learning

Practice and learning are evaluated by surveys provided to trainees at the end of training, and the number of complaints received (Higley, 2016). For instance, if we receive 20 complaints for the previous year and the current year 10 complaints, we can conclude the training has impacted the personnel. The company can also issue surveys annually to determine the personal perceptions of employees regarding the training effectiveness. After the module and test have been completed, staff behaviour would be observed and evaluated.

Analyse how distance learning supports employee development.

The idea of distance learning supports employees’ development by enabling them to be flexible with work off-site while remaining focused on their training (Martin, 2018). Employees do not have to miss work to travel to classes resulting in declined productivity. They would also commit to courses that fit in their schedules. Since workdays are slightly unpredictable, competing for such courses during idle time is better for productivity. eLearning also allows them to enhance their technological knowledge. The business environment is ever-evolving, and staying on top of the latest technologies supports personnel better in their roles. Distance learning also enables personnel to allocate more time on further modules with constrained time openings without any harassment module disrupting them. Employees may require additional skills for an individual goal that can be customized in eLearning. This increases their opportunities for growth and development.

Evaluate the effectiveness of measurements used for a distance learning module

After completing training classes, employees are held responsible for demonstrating what they gained and applied at their workplace. Effectiveness comes from the management enforcing the rules and ensuring constant training (Martin, 2018). If sexual harassment cases occur, they would be addressed according to show the implications of flouting training. Usually, an evaluation is done after each module to assess delivery methods, content satisfaction, and training components. A control group that does not attend the training can also evaluate the effectiveness of the training at the workplace.

There are many opportunities in distance learning, but the process is not perfect. It provides means of offering training to personnel; however, its content must be evaluated to determine appropriateness. Distance learning should be applied to specific employees, like other forms of corporate training. Today, the practice has been utilized further in businesses with the increase in technology.

Higley, M. (2016). Why Meaningful Online Feedback is Important. Retrieved from  https://elearningindustry.com/meaningful-online-feedback-important

Martin, F. (2018). Engagement Matters: Student Perceptions on the Importance of Engagement Strategies in the Online Learning Environment. Retrieved from  https://files.eric.ed.gov/fulltext/EJ1179659.pdf

University of Illinois. (2019). Strengths and Weaknesses of Online Learning. Retrieved from  https://www.uis.edu/ion/resources/tutorials/online-education-overview/strengths-and-weaknesses/

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