essay on online learning during pandemic

The COVID-19 pandemic has changed education forever. This is how 

With schools shut across the world, millions of children have had to adapt to new types of learning. Image:  REUTERS/Gonzalo Fuentes

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Farah lalani.

• The COVID-19 has resulted in schools shut all across the world. Globally, over 1.2 billion children are out of the classroom.
• As a result, education has changed dramatically, with the distinctive rise of e-learning, whereby teaching is undertaken remotely and on digital platforms.
• Research suggests that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay.

While countries are at different points in their COVID-19 infection rates, worldwide there are currently more than 1.2 billion children in 186 countries affected by school closures due to the pandemic. In Denmark, children up to the age of 11 are returning to nurseries and schools after initially closing on 12 March , but in South Korea students are responding to roll calls from their teachers online .

With this sudden shift away from the classroom in many parts of the globe, some are wondering whether the adoption of online learning will continue to persist post-pandemic, and how such a shift would impact the worldwide education market.

Even before COVID-19, there was already high growth and adoption in education technology, with global edtech investments reaching US$18.66 billion in 2019 and the overall market for online education projected to reach $350 Billion by 2025 . Whether it is language apps , virtual tutoring , video conferencing tools, or online learning software , there has been a significant surge in usage since COVID-19.

How is the education sector responding to COVID-19?

In response to significant demand, many online learning platforms are offering free access to their services, including platforms like BYJU’S , a Bangalore-based educational technology and online tutoring firm founded in 2011, which is now the world’s most highly valued edtech company . Since announcing free live classes on its Think and Learn app, BYJU’s has seen a 200% increase in the number of new students using its product, according to Mrinal Mohit, the company's Chief Operating Officer.

Tencent classroom, meanwhile, has been used extensively since mid-February after the Chinese government instructed a quarter of a billion full-time students to resume their studies through online platforms. This resulted in the largest “online movement” in the history of education with approximately 730,000 , or 81% of K-12 students, attending classes via the Tencent K-12 Online School in Wuhan.

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Other companies are bolstering capabilities to provide a one-stop shop for teachers and students. For example, Lark, a Singapore-based collaboration suite initially developed by ByteDance as an internal tool to meet its own exponential growth, began offering teachers and students unlimited video conferencing time, auto-translation capabilities, real-time co-editing of project work, and smart calendar scheduling, amongst other features. To do so quickly and in a time of crisis, Lark ramped up its global server infrastructure and engineering capabilities to ensure reliable connectivity.

Alibaba’s distance learning solution, DingTalk, had to prepare for a similar influx: “To support large-scale remote work, the platform tapped Alibaba Cloud to deploy more than 100,000 new cloud servers in just two hours last month – setting a new record for rapid capacity expansion,” according to DingTalk CEO, Chen Hang.

Some school districts are forming unique partnerships, like the one between The Los Angeles Unified School District and PBS SoCal/KCET to offer local educational broadcasts, with separate channels focused on different ages, and a range of digital options. Media organizations such as the BBC are also powering virtual learning; Bitesize Daily , launched on 20 April, is offering 14 weeks of curriculum-based learning for kids across the UK with celebrities like Manchester City footballer Sergio Aguero teaching some of the content.

What does this mean for the future of learning?

While some believe that the unplanned and rapid move to online learning – with no training, insufficient bandwidth, and little preparation – will result in a poor user experience that is unconducive to sustained growth, others believe that a new hybrid model of education will emerge, with significant benefits. “I believe that the integration of information technology in education will be further accelerated and that online education will eventually become an integral component of school education,“ says Wang Tao, Vice President of Tencent Cloud and Vice President of Tencent Education.

There have already been successful transitions amongst many universities. For example, Zhejiang University managed to get more than 5,000 courses online just two weeks into the transition using “DingTalk ZJU”. The Imperial College London started offering a course on the science of coronavirus, which is now the most enrolled class launched in 2020 on Coursera .

Many are already touting the benefits: Dr Amjad, a Professor at The University of Jordan who has been using Lark to teach his students says, “It has changed the way of teaching. It enables me to reach out to my students more efficiently and effectively through chat groups, video meetings, voting and also document sharing, especially during this pandemic. My students also find it is easier to communicate on Lark. I will stick to Lark even after coronavirus, I believe traditional offline learning and e-learning can go hand by hand."

These 3 charts show the global growth in online learning

The challenges of online learning.

There are, however, challenges to overcome. Some students without reliable internet access and/or technology struggle to participate in digital learning; this gap is seen across countries and between income brackets within countries. For example, whilst 95% of students in Switzerland, Norway, and Austria have a computer to use for their schoolwork, only 34% in Indonesia do, according to OECD data .

In the US, there is a significant gap between those from privileged and disadvantaged backgrounds: whilst virtually all 15-year-olds from a privileged background said they had a computer to work on, nearly 25% of those from disadvantaged backgrounds did not. While some schools and governments have been providing digital equipment to students in need, such as in New South Wales , Australia, many are still concerned that the pandemic will widenthe digital divide .

Is learning online as effective?

For those who do have access to the right technology, there is evidence that learning online can be more effective in a number of ways. Some research shows that on average, students retain 25-60% more material when learning online compared to only 8-10% in a classroom. This is mostly due to the students being able to learn faster online; e-learning requires 40-60% less time to learn than in a traditional classroom setting because students can learn at their own pace, going back and re-reading, skipping, or accelerating through concepts as they choose.

Nevertheless, the effectiveness of online learning varies amongst age groups. The general consensus on children, especially younger ones, is that a structured environment is required , because kids are more easily distracted. To get the full benefit of online learning, there needs to be a concerted effort to provide this structure and go beyond replicating a physical class/lecture through video capabilities, instead, using a range of collaboration tools and engagement methods that promote “inclusion, personalization and intelligence”, according to Dowson Tong, Senior Executive Vice President of Tencent and President of its Cloud and Smart Industries Group.

Since studies have shown that children extensively use their senses to learn, making learning fun and effective through use of technology is crucial, according to BYJU's Mrinal Mohit. “Over a period, we have observed that clever integration of games has demonstrated higher engagement and increased motivation towards learning especially among younger students, making them truly fall in love with learning”, he says.

A changing education imperative

It is clear that this pandemic has utterly disrupted an education system that many assert was already losing its relevance . In his book, 21 Lessons for the 21st Century , scholar Yuval Noah Harari outlines how schools continue to focus on traditional academic skills and rote learning , rather than on skills such as critical thinking and adaptability, which will be more important for success in the future. Could the move to online learning be the catalyst to create a new, more effective method of educating students? While some worry that the hasty nature of the transition online may have hindered this goal, others plan to make e-learning part of their ‘new normal’ after experiencing the benefits first-hand.

The importance of disseminating knowledge is highlighted through COVID-19

Major world events are often an inflection point for rapid innovation – a clear example is the rise of e-commerce post-SARS . While we have yet to see whether this will apply to e-learning post-COVID-19, it is one of the few sectors where investment has not dried up . What has been made clear through this pandemic is the importance of disseminating knowledge across borders, companies, and all parts of society. If online learning technology can play a role here, it is incumbent upon all of us to explore its full potential.

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Home — Essay Samples — Nursing & Health — Public Health — Understanding COVID-19: An Informative Essay for Students

Understanding Covid-19: an Informative Essay for Students

• Categories: Public Health

About this sample

Words: 765 |

Published: Dec 17, 2024

Words: 765 | Pages: 2 | 4 min read

Table of contents

The shift to online learning, adapting to new learning environments, the importance of mental health awareness, cultivating community spirit, rethinking our future goals, a new perspective on global issues, the road ahead: embracing change.

• Baker, S., & McKenzie R., "The Impact Of COVID-19 On Student Life." Education Journal (2021).
• Cohen L., "Mental Health Resources During The Pandemic." Journal Of Psychology And Education (2021).
• Pandey A., "Lessons Learned From Virtual Learning." International Journal Of Educational Technology (2021).
• "COVID-19 And Its Effects On Higher Education," National Center For Education Statistics (2020).
• "Adapting To Online Classes: Student Perspectives," Educational Research Review (2021).

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Students’ experience of online learning during the COVID‐19 pandemic: A province‐wide survey study

Lixiang yan, alexander whitelock‐wainwright, quanlong guan, gangxin wen, dragan gašević, guanliang chen.

• Author information
• Article notes
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Correspondence , Guanliang Chen, Centre for Learning Analytics at Monash, Faculty of Information Technology, Monash University, 25 Exhibition Walk, Clayton VIC 3800, Australia. Email: [email protected]

Corresponding author.

Revised 2021 Mar 16; Received 2020 Nov 19; Accepted 2021 Mar 30; Issue date 2021 Sep.

This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

Online learning is currently adopted by educational institutions worldwide to provide students with ongoing education during the COVID‐19 pandemic. Even though online learning research has been advancing in uncovering student experiences in various settings (i.e., tertiary, adult, and professional education), very little progress has been achieved in understanding the experience of the K‐12 student population, especially when narrowed down to different school‐year segments (i.e., primary and secondary school students). This study explores how students at different stages of their K‐12 education reacted to the mandatory full‐time online learning during the COVID‐19 pandemic. For this purpose, we conducted a province‐wide survey study in which the online learning experience of 1,170,769 Chinese students was collected from the Guangdong Province of China. We performed cross‐tabulation and Chi‐square analysis to compare students’ online learning conditions, experiences, and expectations. Results from this survey study provide evidence that students’ online learning experiences are significantly different across school years. Foremost, policy implications were made to advise government authorises and schools on improving the delivery of online learning, and potential directions were identified for future research into K‐12 online learning.

Practitioner notes.

What is already known about this topic

Online learning has been widely adopted during the COVID‐19 pandemic to ensure the continuation of K‐12 education.

Student success in K‐12 online education is substantially lower than in conventional schools.

Students experienced various difficulties related to the delivery of online learning.

What this paper adds

Provide empirical evidence for the online learning experience of students in different school years.

Identify the different needs of students in primary, middle, and high school.

Identify the challenges of delivering online learning to students of different age.

Implications for practice and/or policy

Authority and schools need to provide sufficient technical support to students in online learning.

The delivery of online learning needs to be customised for students in different school years.

Keywords: e‐learning, learner attitudes/perceptions, primary education, questionnaire, secondary education

INTRODUCTION

The ongoing COVID‐19 pandemic poses significant challenges to the global education system. By July 2020, the UN Educational, Scientific and Cultural Organization (2020) reported nationwide school closure in 111 countries, affecting over 1.07 billion students, which is around 61% of the global student population. Traditional brick‐and‐mortar schools are forced to transform into full‐time virtual schools to provide students with ongoing education (Van Lancker & Parolin,  2020 ). Consequently, students must adapt to the transition from face‐to‐face learning to fully remote online learning, where synchronous video conferences, social media, and asynchronous discussion forums become their primary venues for knowledge construction and peer communication.

For K‐12 students, this sudden transition is problematic as they often lack prior online learning experience (Barbour & Reeves,  2009 ). Barbour and LaBonte ( 2017 ) estimated that even in countries where online learning is growing rapidly, such as USA and Canada, less than 10% of the K‐12 student population had prior experience with this format. Maladaptation to online learning could expose inexperienced students to various vulnerabilities, including decrements in academic performance (Molnar et al.,  2019 ), feeling of isolation (Song et al.,  2004 ), and lack of learning motivation (Muilenburg & Berge,  2005 ). Unfortunately, with confirmed cases continuing to rise each day, and new outbreaks occur on a global scale, full‐time online learning for most students could last longer than anticipated (World Health Organization,  2020 ). Even after the pandemic, the current mass adoption of online learning could have lasting impacts on the global education system, and potentially accelerate and expand the rapid growth of virtual schools on a global scale (Molnar et al.,  2019 ). Thus, understanding students' learning conditions and their experiences of online learning during the COVID pandemic becomes imperative.

Emerging evidence on students’ online learning experience during the COVID‐19 pandemic has identified several major concerns, including issues with internet connection (Agung et al.,  2020 ; Basuony et al.,  2020 ), problems with IT equipment (Bączek et al.,  2021 ; Niemi & Kousa,  2020 ), limited collaborative learning opportunities (Bączek et al.,  2021 ; Yates et al.,  2020 ), reduced learning motivation (Basuony et al.,  2020 ; Niemi & Kousa,  2020 ; Yates et al.,  2020 ), and increased learning burdens (Niemi & Kousa,  2020 ). Although these findings provided valuable insights about the issues students experienced during online learning, information about their learning conditions and future expectations were less mentioned. Such information could assist educational authorises and institutions to better comprehend students’ difficulties and potentially improve their online learning experience. Additionally, most of these recent studies were limited to higher education, except for Yates et al. ( 2020 ) and Niemi and Kousa’s ( 2020 ) studies on senior high school students. Empirical research targeting the full spectrum of K‐12students remain scarce. Therefore, to address these gaps, the current paper reports the findings of a large‐scale study that sought to explore K‐12 students’ online learning experience during the COVID‐19 pandemic in a provincial sample of over one million Chinese students. The findings of this study provide policy recommendations to educational institutions and authorities regarding the delivery of K‐12 online education.

LITERATURE REVIEW

Learning conditions and technologies.

Having stable access to the internet is critical to students’ learning experience during online learning. Berge ( 2005 ) expressed the concern of the divide in digital‐readiness, and the pedagogical approach between different countries could influence students’ online learning experience. Digital‐readiness is the availability and adoption of information technologies and infrastructures in a country. Western countries like America (3rd) scored significantly higher in digital‐readiness compared to Asian countries like China (54th; Cisco,  2019 ). Students from low digital‐readiness countries could experience additional technology‐related problems. Supporting evidence is emerging in recent studies conducted during the COVID‐19 pandemic. In Egypt's capital city, Basuony et al. ( 2020 ) found that only around 13.9%of the students experienced issues with their internet connection. Whereas more than two‐thirds of the students in rural Indonesia reported issues of unstable internet, insufficient internet data, and incompatible learning device (Agung et al.,  2020 ).

Another influential factor for K‐12 students to adequately adapt to online learning is the accessibility of appropriate technological devices, especially having access to a desktop or a laptop (Barbour et al., 2018 ). However, it is unlikely for most of the students to satisfy this requirement. Even in higher education, around 76% of students reported having incompatible devices for online learning and only 15% of students used laptop for online learning, whereas around 85% of them used smartphone (Agung et al.,  2020 ). It is very likely that K‐12 students also suffer from this availability issue as they depend on their parents to provide access to relevant learning devices.

Technical issues surrounding technological devices could also influence students’ experience in online learning. (Barbour & Reeves,  2009 ) argues that students need to have a high level of digital literacy to find and use relevant information and communicate with others through technological devices. Students lacking this ability could experience difficulties in online learning. Bączek et al. ( 2021 ) found that around 54% of the medical students experienced technical problems with IT equipment and this issue was more prevalent in students with lower years of tertiary education. Likewise, Niemi and Kousa ( 2020 ) also find that students in a Finish high school experienced increased amounts of technical problems during the examination period, which involved additional technical applications. These findings are concerning as young children and adolescent in primary and lower secondary school could be more vulnerable to these technical problems as they are less experienced with the technologies in online learning (Barbour & LaBonte,  2017 ). Therefore, it is essential to investigate the learning conditions and the related difficulties experienced by students in K‐12 education as the extend of effects on them remain underexplored.

Learning experience and interactions

Apart from the aforementioned issues, the extent of interaction and collaborative learning opportunities available in online learning could also influence students’ experience. The literature on online learning has long emphasised the role of effective interaction for the success of student learning. According to Muirhead and Juwah ( 2004 ), interaction is an event that can take the shape of any type of communication between two or subjects and objects. Specifically, the literature acknowledges the three typical forms of interactions (Moore,  1989 ): (i) student‐content, (ii) student‐student, and (iii) student‐teacher. Anderson ( 2003 ) posits, in the well‐known interaction equivalency theorem, learning experiences will not deteriorate if only one of the three interaction is of high quality, and the other two can be reduced or even eliminated. Quality interaction can be accomplished by across two dimensions: (i) structure—pedagogical means that guide student interaction with contents or other students and (ii) dialogue—communication that happens between students and teachers and among students. To be able to scale online learning and prevent the growth of teaching costs, the emphasise is typically on structure (i.e., pedagogy) that can promote effective student‐content and student‐student interaction. The role of technology and media is typically recognised as a way to amplify the effect of pedagogy (Lou et al.,  2006 ). Novel technological innovations—for example learning analytics‐based personalised feedback at scale (Pardo et al.,  2019 ) —can also empower teachers to promote their interaction with students.

Online education can lead to a sense of isolation, which can be detrimental to student success (McInnerney & Roberts,  2004 ). Therefore, integration of social interaction into pedagogy for online learning is essential, especially at the times when students do not actually know each other or have communication and collaboration skills underdeveloped (Garrison et al.,  2010 ; Gašević et al.,  2015 ). Unfortunately, existing evidence suggested that online learning delivery during the COVID‐19 pandemic often lacks interactivity and collaborative experiences (Bączek et al.,  2021 ; Yates et al.,  2020 ). Bączek et al., ( 2021 ) found that around half of the medical students reported reduced interaction with teachers, and only 4% of students think online learning classes are interactive. Likewise, Yates et al. ( 2020 )’s study in high school students also revealed that over half of the students preferred in‐class collaboration over online collaboration as they value the immediate support and the proximity to teachers and peers from in‐class interaction.

Learning expectations and age differentiation

Although these studies have provided valuable insights and stressed the need for more interactivity in online learning, K‐12 students in different school years could exhibit different expectations for the desired activities in online learning. Piaget's Cognitive Developmental Theory illustrated children's difficulties in understanding abstract and hypothetical concepts (Thomas,  2000 ). Primary school students will encounter many abstract concepts in their STEM education (Uttal & Cohen,  2012 ). In face‐to‐face learning, teachers provide constant guidance on students’ learning progress and can help them to understand difficult concepts. Unfortunately, the level of guidance significantly drops in online learning, and, in most cases, children have to face learning obstacles by themselves (Barbour,  2013 ). Additionally, lower primary school students may lack the metacognitive skills to use various online learning functions, maintain engagement in synchronous online learning, develop and execute self‐regulated learning plans, and engage in meaningful peer interactions during online learning (Barbour,  2013 ; Broadbent & Poon,  2015 ; Huffaker & Calvert, 2003; Wang et al.,  2013 ). Thus, understanding these younger students’ expectations is imperative as delivering online learning to them in the same way as a virtual high school could hinder their learning experiences. For students with more matured metacognition, their expectations of online learning could be substantially different from younger students. Niemi et al.’s study ( 2020 ) with students in a Finish high school have found that students often reported heavy workload and fatigue during online learning. These issues could cause anxiety and reduce students’ learning motivation, which would have negative consequences on their emotional well‐being and academic performance (Niemi & Kousa,  2020 ; Yates et al.,  2020 ), especially for senior students who are under the pressure of examinations. Consequently, their expectations of online learning could be orientated toward having additional learning support functions and materials. Likewise, they could also prefer having more opportunities for peer interactions as these interactions are beneficial to their emotional well‐being and learning performance (Gašević et al., 2013 ; Montague & Rinaldi, 2001 ). Therefore, it is imperative to investigate the differences between online learning expectations in students of different school years to suit their needs better.

Research questions

By building upon the aforementioned relevant works, this study aimed to contribute to the online learning literature with a comprehensive understanding of the online learning experience that K‐12 students had during the COVID‐19 pandemic period in China. Additionally, this study also aimed to provide a thorough discussion of what potential actions can be undertaken to improve online learning delivery. Formally, this study was guided by three research questions (RQs):

RQ1 . What learning conditions were experienced by students across 12 years of education during their online learning process in the pandemic period? RQ2 . What benefits and obstacles were perceived by students across 12 years of education when performing online learning? RQ3 . What expectations do students, across 12 years of education, have for future online learning practices ?

Participants

The total number of K‐12 students in the Guangdong Province of China is around 15 million. In China, students of Year 1–6, Year 7–9, and Year 10–12 are referred to as students of primary school, middle school, and high school, respectively. Typically, students in China start their study in primary school at the age of around six. At the end of their high‐school study, students have to take the National College Entrance Examination (NCEE; also known as Gaokao) to apply for tertiary education. The survey was administrated across the whole Guangdong Province, that is the survey was exposed to all of the 15 million K‐12 students, though it was not mandatory for those students to accomplish the survey. A total of 1,170,769 students completed the survey, which accounts for a response rate of 7.80%. After removing responses with missing values and responses submitted from the same IP address (duplicates), we had 1,048,575 valid responses, which accounts to about 7% of the total K‐12 students in the Guangdong Province. The number of students in different school years is shown in Figure  1 . Overall, students were evenly distributed across different school years, except for a smaller sample in students of Year 10–12.

The number of students in each school year

Survey design

The survey was designed collaboratively by multiple relevant parties. Firstly, three educational researchers working in colleges and universities and three educational practitioners working in the Department of Education in Guangdong Province were recruited to co‐design the survey. Then, the initial draft of the survey was sent to 30 teachers from different primary and secondary schools, whose feedback and suggestions were considered to improve the survey. The final survey consisted of a total of 20 questions, which, broadly, can be classified into four categories: demographic, behaviours, experiences, and expectations. Details are available in Appendix.

All K‐12 students in the Guangdong Province were made to have full‐time online learning from March 1, 2020 after the outbreak of COVID‐19 in January in China. A province‐level online learning platform was provided to all schools by the government. In addition to the learning platform, these schools can also use additional third‐party platforms to facilitate the teaching activities, for example WeChat and Dingding, which provide services similar to WhatsApp and Zoom. The main change for most teachers was that they had to shift the classroom‐based lectures to online lectures with the aid of web‐conferencing tools. Similarly, these teachers also needed to perform homework marking and have consultation sessions in an online manner.

The Department of Education in the Guangdong Province of China distributed the survey to all K‐12 schools in the province on March 21, 2020 and collected responses on March 26, 2020. Students could access and answer the survey anonymously by either scan the Quick Response code along with the survey or click the survey address link on their mobile device. The survey was administrated in a completely voluntary manner and no incentives were given to the participants. Ethical approval was granted by the Department of Education in the Guangdong Province. Parental approval was not required since the survey was entirely anonymous and facilitated by the regulating authority, which satisfies China's ethical process.

The original survey was in Chinese, which was later translated by two bilingual researchers and verified by an external translator who is certified by the Australian National Accreditation Authority of Translators and Interpreters. The original and translated survey questionnaires are available in Supporting Information. Given the limited space we have here and the fact that not every survey item is relevant to the RQs, the following items were chosen to answer the RQs: item Q3 (learning media) and Q11 (learning approaches) for RQ1, item Q13 (perceived obstacle) and Q19 (perceived benefits) for RQ2, and item Q19 (expected learning activities) for RQ3. Cross‐tabulation based approaches were used to analyse the collected data. To scrutinise whether the differences displayed by students of different school years were statistically significant, we performed Chi‐square tests and calculated the Cramer's V to assess the strengths of the association after chi‐square had determined significance.

For the analyses, students were segmented into four categories based on their school years, that is Year 1–3, Year 4–6, Year 7–9, and Year 10–12, to provide a clear understanding of the different experiences and needs that different students had for online learning. This segmentation was based on the educational structure of Chinese schools: elementary school (Year 1–6), middle school (Year 7–9), and high school (Year 10–12). Children in elementary school can further be segmented into junior (Year 1–3) or senior (Year 4–6) students because senior elementary students in China are facing more workloads compared to junior students due to the provincial Middle School Entry Examination at the end of Year 6.

Learning conditions—RQ1

Learning media.

The Chi‐square test showed significant association between school years and students’ reported usage of learning media, χ 2 (55, N  = 1,853,952) = 46,675.38, p  < 0.001. The Cramer's V is 0.07 ( df ∗ = 5), which indicates a small‐to‐medium effect according to Cohen’s ( 1988 ) guidelines. Based on Figure  2 , we observed that an average of up to 87.39% students used smartphones to perform online learning, while only 25.43% students used computer, which suggests that smartphones, with widespread availability in China (2020), have been adopted by students for online learning. As for the prevalence of the two media, we noticed that both smartphones ( χ 2 (3, N  = 1,048,575) = 9,395.05, p < 0.001, Cramer's V  = 0.10 ( df ∗ = 1)) and computers ( χ 2 (3, N  = 1,048,575) = 11,025.58, p <.001, Cramer's V  = 0.10 ( df ∗ = 1)) were more adopted by high‐school‐year (Year 7–12) than early‐school‐year students (Year 1–6), both with a small effect size. Besides, apparent discrepancies can be observed between the usages of TV and paper‐based materials across different school years, that is early‐school‐year students reported more TV usage ( χ 2 (3, N  = 1,048,575) = 19,505.08, p <.001), with a small‐to‐medium effect size, Cramer's V  = 0.14( df ∗ = 1). High‐school‐year students (especially Year 10–12) reported more usage of paper‐based materials ( χ 2 (3, N  = 1,048,575) = 23,401.64, p < 0.001), with a small‐to‐medium effect size, Cramer's V  = 0.15( df ∗ = 1).

Learning media used by students in online learning

Learning approaches

School years is also significantly associated with the different learning approaches students used to tackle difficult concepts during online learning, χ 2 (55, N  = 2,383,751) = 58,030.74, p < 0.001. The strength of this association is weak to moderate as shown by the Cramer's V (0.07, df ∗ = 5; Cohen,  1988 ). When encountering problems related to difficult concepts, students typically chose to “solve independently by searching online” or “rewatch recorded lectures” instead of consulting to their teachers or peers (Figure  3 ). This is probably because, compared to classroom‐based education, it is relatively less convenient and more challenging for students to seek help from others when performing online learning. Besides, compared to high‐school‐year students, early‐school‐year students (Year 1–6), reported much less use of “solve independently by searching online” ( χ 2 (3, N  = 1,048,575) = 48,100.15, p <.001), with a small‐to‐medium effect size, Cramer's V  = 0.21 ( df ∗ = 1). Also, among those approaches of seeking help from others, significantly more high‐school‐year students preferred “communicating with other students” than early‐school‐year students ( χ 2 (3, N  = 1,048,575) = 81,723.37, p < 0.001), with a medium effect size, Cramer's V  = 0.28 ( df ∗ = 1).

Learning approaches used by students in online learning

Perceived benefits and obstacles—RQ2

Perceived benefits.

The association between school years and perceived benefits in online learning is statistically significant, χ 2 (66, N  = 2,716,127) = 29,534.23, p  < 0.001, and the Cramer's V (0.04, df ∗ = 6) indicates a small effect (Cohen,  1988 ). Unsurprisingly, benefits brought by the convenience of online learning are widely recognised by students across all school years (Figure  4 ), that is up to 75% of students reported that it is “more convenient to review course content” and 54% said that they “can learn anytime and anywhere” . Besides, we noticed that about 50% of early‐school‐year students appreciated the “access to courses delivered by famous teachers” and 40%–47% of high‐school‐year students indicated that online learning is “helpful to develop self‐regulation and autonomy” .

Perceived benefits of online learning reported by students

Perceived obstacles

The Chi‐square test shows a significant association between school years and students’ perceived obstacles in online learning, χ 2 (77, N  = 2,699,003) = 31,987.56, p < 0.001. This association is relatively weak as shown by the Cramer's V (0.04, df ∗ = 7; Cohen,  1988 ). As shown in Figure  5 , the biggest obstacles encountered by up to 73% of students were the “eyestrain caused by long staring at screens” . Disengagement caused by nearby disturbance was reported by around 40% of students, especially those of Year 1–3 and 10–12. Technological‐wise, about 50% of students experienced poor Internet connection during their learning process, and around 20% of students reported the “confusion in setting up the platforms” across of school years.

Perceived obstacles of online learning reported by students

Expectations for future practices of online learning – RQ3

Online learning activities.

The association between school years and students’ expected online learning activities is significant, χ 2 (66, N  = 2,416,093) = 38,784.81, p < 0.001. The Cramer's V is 0.05 ( df ∗ = 6) which suggests a small effect (Cohen,  1988 ). As shown in Figure  6 , the most expected activity for future online learning is “real‐time interaction with teachers” (55%), followed by “online group discussion and collaboration” (38%). We also observed that more early‐school‐year students expect reflective activities, such as “regular online practice examinations” ( χ 2 (3, N  = 1,048,575) = 11,644.98, p < 0.001), with a small effect size, Cramer's V  = 0.11 ( df ∗ = 1). In contrast, more high‐school‐year students expect “intelligent recommendation system …” ( χ 2 (3, N  = 1,048,575) = 15,327.00, p < 0.001), with a small effect size, Cramer's V  = 0.12 ( df ∗ = 1).

Students’ expected online learning activities

Regarding students’ learning conditions, substantial differences were observed in learning media, family dependency, and learning approaches adopted in online learning between students in different school years. The finding of more computer and smartphone usage in high‐school‐year than early‐school‐year students can probably be explained by that, with the growing abilities in utilising these media as well as the educational systems and tools which run on these media, high‐school‐year students tend to make better use of these media for online learning practices. Whereas, the differences in paper‐based materials may imply that high‐school‐year students in China have to accomplish a substantial amount of exercise, assignments, and exam papers to prepare for the National College Entrance Examination (NCEE), whose delivery was not entirely digitised due to the sudden transition to online learning. Meanwhile, high‐school‐year students may also have preferred using paper‐based materials for exam practice, as eventually, they would take their NCEE in the paper format. Therefore, these substantial differences in students’ usage of learning media should be addressed by customising the delivery method of online learning for different school years.

Other than these between‐age differences in learning media, the prevalence of smartphone in online learning resonates with Agung et al.’s ( 2020 ) finding on the issues surrounding the availability of compatible learning device. The prevalence of smartphone in K‐12 students is potentially problematic as the majority of the online learning platform and content is designed for computer‐based learning (Berge,  2005 ; Molnar et al.,  2019 ). Whereas learning with smartphones has its own unique challenges. For example, Gikas and Grant ( 2013 ) discovered that students who learn with smartphone experienced frustration with the small screen‐size, especially when trying to type with the tiny keypad. Another challenge relates to the distraction of various social media applications. Although similar distractions exist in computer and web‐based social media, the level of popularity, especially in the young generation, are much higher in mobile‐based social media (Montag et al.,  2018 ). In particular, the message notification function in smartphones could disengage students from learning activities and allure them to social media applications (Gikas & Grant,  2013 ). Given these challenges of learning with smartphones, more research efforts should be devoted to analysing students’ online learning behaviour in the setting of mobile learning to accommodate their needs better.

The differences in learning approaches, once again, illustrated that early‐school‐year students have different needs compared to high‐school‐year students. In particular, the low usage of the independent learning methods in early‐school‐year students may reflect their inability to engage in independent learning. Besides, the differences in help seeking behaviours demonstrated the distinctive needs for communication and interaction between different students, that is early‐school‐year students have a strong reliance on teachers and high‐school‐year students, who are equipped with stronger communication ability, are more inclined to interact with their peers. This finding implies that the design of online learning platforms should take students’ different needs into account. Thus, customisation is urgently needed for the delivery of online learning to different school years.

In terms of the perceived benefits and challenges of online learning, our results resonate with several previous findings. In particular, the benefits of convenience are in line with the flexibility advantages of online learning, which were mentioned in prior works (Appana,  2008 ; Bączek et al.,  2021 ; Barbour,  2013 ; Basuony et al.,  2020 ; Harvey et al.,  2014 ). Early‐school‐year students’ higher appreciation in having “access to courses delivered by famous teachers” and lower appreciation in the independent learning skills developed through online learning are also in line with previous literature (Barbour,  2013 ; Harvey et al.,  2014 ; Oliver et al.,  2009 ). Again, these similar findings may indicate the strong reliance that early‐school‐year students place on teachers, while high‐school‐year students are more capable of adapting to online learning by developing independent learning skills.

Technology‐wise, students’ experience of poor internet connection and confusion in setting up online learning platforms are particularly concerning. The problem of poor internet connection corroborated the findings reported in prior studies (Agung et al.,  2020 ; Barbour,  2013 ; Basuony et al.,  2020 ; Berge,  2005 ; Rice,  2006 ), that is the access issue surrounded the digital divide as one of the main challenges of online learning. In the era of 4G and 5G networks, educational authorities and institutions that deliver online education could fall into the misconception of most students have a stable internet connection at home. The internet issue we observed is particularly vital to students’ online learning experience as most students prefer real‐time communications (Figure  6 ), which rely heavily on stable internet connection. Likewise, the finding of students’ confusion in technology is also consistent with prior studies (Bączek et al.,  2021 ; Muilenburg & Berge,  2005 ; Niemi & Kousa,  2020 ; Song et al.,  2004 ). Students who were unsuccessfully in setting up the online learning platforms could potentially experience declines in confidence and enthusiasm for online learning, which would cause a subsequent unpleasant learning experience. Therefore, both the readiness of internet infrastructure and student technical skills remain as the significant challenges for the mass‐adoption of online learning.

On the other hand, students’ experience of eyestrain from extended screen time provided empirical evidence to support Spitzer’s ( 2001 ) speculation about the potential ergonomic impact of online learning. This negative effect is potentially related to the prevalence of smartphone device and the limited screen size of these devices. This finding not only demonstrates the potential ergonomic issues that would be caused by smartphone‐based online learning but also resonates with the aforementioned necessity of different platforms and content designs for different students.

A less‐mentioned problem in previous studies on online learning experiences is the disengagement caused by nearby disturbance, especially in Year 1–3 and 10–12. It is likely that early‐school‐year students suffered from this problem because of their underdeveloped metacognitive skills to concentrate on online learning without teachers’ guidance. As for high‐school‐year students, the reasons behind their disengagement require further investigation in the future. Especially it would be worthwhile to scrutinise whether this type of disengagement is caused by the substantial amount of coursework they have to undertake and the subsequent a higher level of pressure and a lower level of concentration while learning.

Across age‐level differences are also apparent in terms of students’ expectations of online learning. Although, our results demonstrated students’ needs of gaining social interaction with others during online learning, findings (Bączek et al.,  2021 ; Harvey et al.,  2014 ; Kuo et al.,  2014 ; Liu & Cavanaugh,  2012 ; Yates et al.,  2020 ). This need manifested differently across school years, with early‐school‐year students preferring more teacher interactions and learning regulation support. Once again, this finding may imply that early‐school‐year students are inadequate in engaging with online learning without proper guidance from their teachers. Whereas, high‐school‐year students prefer more peer interactions and recommendation to learning resources. This expectation can probably be explained by the large amount of coursework exposed to them. Thus, high‐school‐year students need further guidance to help them better direct their learning efforts. These differences in students’ expectations for future practices could guide the customisation of online learning delivery.

Implications

As shown in our results, improving the delivery of online learning not only requires the efforts of policymakers but also depend on the actions of teachers and parents. The following sub‐sections will provide recommendations for relevant stakeholders and discuss their essential roles in supporting online education.

Technical support

The majority of the students has experienced technical problems during online learning, including the internet lagging and confusion in setting up the learning platforms. These problems with technology could impair students’ learning experience (Kauffman,  2015 ; Muilenburg & Berge,  2005 ). Educational authorities and schools should always provide a thorough guide and assistance for students who are experiencing technical problems with online learning platforms or other related tools. Early screening and detection could also assist schools and teachers to direct their efforts more effectively in helping students with low technology skills (Wilkinson et al.,  2010 ). A potential identification method involves distributing age‐specific surveys that assess students’ Information and Communication Technology (ICT) skills at the beginning of online learning. For example, there are empirical validated ICT surveys available for both primary (Aesaert et al.,  2014 ) and high school (Claro et al.,  2012 ) students.

For students who had problems with internet lagging, the delivery of online learning should provide options that require fewer data and bandwidth. Lecture recording is the existing option but fails to address students’ need for real‐time interaction (Clark et al.,  2015 ; Malik & Fatima,  2017 ). A potential alternative involves providing students with the option to learn with digital or physical textbooks and audio‐conferencing, instead of screen sharing and video‐conferencing. This approach significantly reduces the amount of data usage and lowers the requirement of bandwidth for students to engage in smooth online interactions (Cisco,  2018 ). It also requires little additional efforts from teachers as official textbooks are often available for each school year, and thus, they only need to guide students through the materials during audio‐conferencing. Educational authority can further support this approach by making digital textbooks available for teachers and students, especially those in financial hardship. However, the lack of visual and instructor presence could potentially reduce students’ attention, recall of information, and satisfaction in online learning (Wang & Antonenko,  2017 ). Therefore, further research is required to understand whether the combination of digital or physical textbooks and audio‐conferencing is appropriate for students with internet problems. Alternatively, suppose the local technological infrastructure is well developed. In that case, governments and schools can also collaborate with internet providers to issue data and bandwidth vouchers for students who are experiencing internet problems due to financial hardship.

For future adoption of online learning, policymakers should consider the readiness of the local internet infrastructure. This recommendation is particularly important for developing countries, like Bangladesh, where the majority of the students reported the lack of internet infrastructure (Ramij & Sultana,  2020 ). In such environments, online education may become infeasible, and alternative delivery method could be more appropriate, for example, the Telesecundaria program provides TV education for rural areas of Mexico (Calderoni,  1998 ).

Other than technical problems, choosing a suitable online learning platform is also vital for providing students with a better learning experience. Governments and schools should choose an online learning platform that is customised for smartphone‐based learning, as the majority of students could be using smartphones for online learning. This recommendation is highly relevant for situations where students are forced or involuntarily engaged in online learning, like during the COVID‐19 pandemic, as they might not have access to a personal computer (Molnar et al.,  2019 ).

Customisation of delivery methods

Customising the delivery of online learning for students in different school years is the theme that appeared consistently across our findings. This customisation process is vital for making online learning an opportunity for students to develop independent learning skills, which could help prepare them for tertiary education and lifelong learning. However, the pedagogical design of K‐12 online learning programs should be differentiated from adult‐orientated programs as these programs are designed for independent learners, which is rarely the case for students in K‐12 education (Barbour & Reeves,  2009 ).

For early‐school‐year students, especially Year 1–3 students, providing them with sufficient guidance from both teachers and parents should be the priority as these students often lack the ability to monitor and reflect on learning progress. In particular, these students would prefer more real‐time interaction with teachers, tutoring from parents, and regular online practice examinations. These forms of guidance could help early‐school‐year students to cope with involuntary online learning, and potentially enhance their experience in future online learning. It should be noted that, early‐school‐year students demonstrated interest in intelligent monitoring and feedback systems for learning. Additional research is required to understand whether these young children are capable of understanding and using learning analytics that relay information on their learning progress. Similarly, future research should also investigate whether young children can communicate effectively through digital tools as potential inability could hinder student learning in online group activities. Therefore, the design of online learning for early‐school‐year students should focus less on independent learning but ensuring that students are learning effective under the guidance of teachers and parents.

In contrast, group learning and peer interaction are essential for older children and adolescents. The delivery of online learning for these students should focus on providing them with more opportunities to communicate with each other and engage in collaborative learning. Potential methods to achieve this goal involve assigning or encouraging students to form study groups (Lee et al.,  2011 ), directing students to use social media for peer communication (Dabbagh & Kitsantas,  2012 ), and providing students with online group assignments (Bickle & Rucker,  2018 ).

Special attention should be paid to students enrolled in high schools. For high‐school‐year students, in particular, students in Year 10–12, we also recommend to provide them with sufficient access to paper‐based learning materials, such as revision booklet and practice exam papers, so they remain familiar with paper‐based examinations. This recommendation applies to any students who engage in online learning but has to take their final examination in paper format. It is also imperative to assist high‐school‐year students who are facing examinations to direct their learning efforts better. Teachers can fulfil this need by sharing useful learning resources on the learning management system, if it is available, or through social media groups. Alternatively, students are interested in intelligent recommendation systems for learning resources, which are emerging in the literature (Corbi & Solans,  2014 ; Shishehchi et al.,  2010 ). These systems could provide personalised recommendations based on a series of evaluation on learners’ knowledge. Although it is infeasible for situations where the transformation to online learning happened rapidly (i.e., during the COVID‐19 pandemic), policymakers can consider embedding such systems in future online education.

Limitations

The current findings are limited to primary and secondary Chinese students who were involuntarily engaged in online learning during the COVID‐19 pandemic. Despite the large sample size, the population may not be representative as participants are all from a single province. Also, information about the quality of online learning platforms, teaching contents, and pedagogy approaches were missing because of the large scale of our study. It is likely that the infrastructures of online learning in China, such as learning platforms, instructional designs, and teachers’ knowledge about online pedagogy, were underprepared for the sudden transition. Thus, our findings may not represent the experience of students who voluntarily participated in well‐prepared online learning programs, in particular, the virtual school programs in America and Canada (Barbour & LaBonte,  2017 ; Molnar et al.,  2019 ). Lastly, the survey was only evaluated and validated by teachers but not students. Therefore, students with the lowest reading comprehension levels might have a different understanding of the items’ meaning, especially terminologies that involve abstract contracts like self‐regulation and autonomy in item Q17.

In conclusion, we identified across‐year differences between primary and secondary school students’ online learning experience during the COVID‐19 pandemic. Several recommendations were made for the future practice and research of online learning in the K‐12 student population. First, educational authorities and schools should provide sufficient technical support to help students to overcome potential internet and technical problems, as well as choosing online learning platforms that have been customised for smartphones. Second, customising the online pedagogy design for students in different school years, in particular, focusing on providing sufficient guidance for young children, more online collaborative opportunity for older children and adolescent, and additional learning resource for senior students who are facing final examinations.

CONFLICT OF INTEREST

There is no potential conflict of interest in this study.

ETHICS STATEMENT

The data are collected by the Department of Education of the Guangdong Province who also has the authority to approve research studies in K12 education in the province.

Supporting information

Supplementary Material

ACKNOWLEDGEMENTS

This work is supported by the National Natural Science Foundation of China (62077028, 61877029), the Science and Technology Planning Project of Guangdong (2020B0909030005, 2020B1212030003, 2020ZDZX3013, 2019B1515120010, 2018KTSCX016, 2019A050510024), the Science and Technology Planning Project of Guangzhou (201902010041), and the Fundamental Research Funds for the Central Universities (21617408, 21619404).

SURVEY ITEMS

Yan, L , Whitelock‐Wainwright, A , Guan, Q , Wen, G , Gašević, D , & Chen, G . Students’ experience of online learning during the COVID‐19 pandemic: A province‐wide survey study. Br J Educ Technol. 2021;52:2038–2057. 10.1111/bjet.13102

DATA AVAILABILITY STATEMENT

The data is not openly available as it is restricted by the Chinese government.

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• Published: 25 January 2021

Online education in the post-COVID era

• Barbara B. Lockee 1  

Nature Electronics volume  4 ,  pages 5–6 ( 2021 ) Cite this article

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The coronavirus pandemic has forced students and educators across all levels of education to rapidly adapt to online learning. The impact of this — and the developments required to make it work — could permanently change how education is delivered.

The COVID-19 pandemic has forced the world to engage in the ubiquitous use of virtual learning. And while online and distance learning has been used before to maintain continuity in education, such as in the aftermath of earthquakes 1 , the scale of the current crisis is unprecedented. Speculation has now also begun about what the lasting effects of this will be and what education may look like in the post-COVID era. For some, an immediate retreat to the traditions of the physical classroom is required. But for others, the forced shift to online education is a moment of change and a time to reimagine how education could be delivered 2 .

Looking back

Online education has traditionally been viewed as an alternative pathway, one that is particularly well suited to adult learners seeking higher education opportunities. However, the emergence of the COVID-19 pandemic has required educators and students across all levels of education to adapt quickly to virtual courses. (The term ‘emergency remote teaching’ was coined in the early stages of the pandemic to describe the temporary nature of this transition 3 .) In some cases, instruction shifted online, then returned to the physical classroom, and then shifted back online due to further surges in the rate of infection. In other cases, instruction was offered using a combination of remote delivery and face-to-face: that is, students can attend online or in person (referred to as the HyFlex model 4 ). In either case, instructors just had to figure out how to make it work, considering the affordances and constraints of the specific learning environment to create learning experiences that were feasible and effective.

The use of varied delivery modes does, in fact, have a long history in education. Mechanical (and then later electronic) teaching machines have provided individualized learning programmes since the 1950s and the work of B. F. Skinner 5 , who proposed using technology to walk individual learners through carefully designed sequences of instruction with immediate feedback indicating the accuracy of their response. Skinner’s notions formed the first formalized representations of programmed learning, or ‘designed’ learning experiences. Then, in the 1960s, Fred Keller developed a personalized system of instruction 6 , in which students first read assigned course materials on their own, followed by one-on-one assessment sessions with a tutor, gaining permission to move ahead only after demonstrating mastery of the instructional material. Occasional class meetings were held to discuss concepts, answer questions and provide opportunities for social interaction. A personalized system of instruction was designed on the premise that initial engagement with content could be done independently, then discussed and applied in the social context of a classroom.

These predecessors to contemporary online education leveraged key principles of instructional design — the systematic process of applying psychological principles of human learning to the creation of effective instructional solutions — to consider which methods (and their corresponding learning environments) would effectively engage students to attain the targeted learning outcomes. In other words, they considered what choices about the planning and implementation of the learning experience can lead to student success. Such early educational innovations laid the groundwork for contemporary virtual learning, which itself incorporates a variety of instructional approaches and combinations of delivery modes.

Online learning and the pandemic

Fast forward to 2020, and various further educational innovations have occurred to make the universal adoption of remote learning a possibility. One key challenge is access. Here, extensive problems remain, including the lack of Internet connectivity in some locations, especially rural ones, and the competing needs among family members for the use of home technology. However, creative solutions have emerged to provide students and families with the facilities and resources needed to engage in and successfully complete coursework 7 . For example, school buses have been used to provide mobile hotspots, and class packets have been sent by mail and instructional presentations aired on local public broadcasting stations. The year 2020 has also seen increased availability and adoption of electronic resources and activities that can now be integrated into online learning experiences. Synchronous online conferencing systems, such as Zoom and Google Meet, have allowed experts from anywhere in the world to join online classrooms 8 and have allowed presentations to be recorded for individual learners to watch at a time most convenient for them. Furthermore, the importance of hands-on, experiential learning has led to innovations such as virtual field trips and virtual labs 9 . A capacity to serve learners of all ages has thus now been effectively established, and the next generation of online education can move from an enterprise that largely serves adult learners and higher education to one that increasingly serves younger learners, in primary and secondary education and from ages 5 to 18.

The COVID-19 pandemic is also likely to have a lasting effect on lesson design. The constraints of the pandemic provided an opportunity for educators to consider new strategies to teach targeted concepts. Though rethinking of instructional approaches was forced and hurried, the experience has served as a rare chance to reconsider strategies that best facilitate learning within the affordances and constraints of the online context. In particular, greater variance in teaching and learning activities will continue to question the importance of ‘seat time’ as the standard on which educational credits are based 10 — lengthy Zoom sessions are seldom instructionally necessary and are not aligned with the psychological principles of how humans learn. Interaction is important for learning but forced interactions among students for the sake of interaction is neither motivating nor beneficial.

While the blurring of the lines between traditional and distance education has been noted for several decades 11 , the pandemic has quickly advanced the erasure of these boundaries. Less single mode, more multi-mode (and thus more educator choices) is becoming the norm due to enhanced infrastructure and developed skill sets that allow people to move across different delivery systems 12 . The well-established best practices of hybrid or blended teaching and learning 13 have served as a guide for new combinations of instructional delivery that have developed in response to the shift to virtual learning. The use of multiple delivery modes is likely to remain, and will be a feature employed with learners of all ages 14 , 15 . Future iterations of online education will no longer be bound to the traditions of single teaching modes, as educators can support pedagogical approaches from a menu of instructional delivery options, a mix that has been supported by previous generations of online educators 16 .

Also significant are the changes to how learning outcomes are determined in online settings. Many educators have altered the ways in which student achievement is measured, eliminating assignments and changing assessment strategies altogether 17 . Such alterations include determining learning through strategies that leverage the online delivery mode, such as interactive discussions, student-led teaching and the use of games to increase motivation and attention. Specific changes that are likely to continue include flexible or extended deadlines for assignment completion 18 , more student choice regarding measures of learning, and more authentic experiences that involve the meaningful application of newly learned skills and knowledge 19 , for example, team-based projects that involve multiple creative and social media tools in support of collaborative problem solving.

In response to the COVID-19 pandemic, technological and administrative systems for implementing online learning, and the infrastructure that supports its access and delivery, had to adapt quickly. While access remains a significant issue for many, extensive resources have been allocated and processes developed to connect learners with course activities and materials, to facilitate communication between instructors and students, and to manage the administration of online learning. Paths for greater access and opportunities to online education have now been forged, and there is a clear route for the next generation of adopters of online education.

Before the pandemic, the primary purpose of distance and online education was providing access to instruction for those otherwise unable to participate in a traditional, place-based academic programme. As its purpose has shifted to supporting continuity of instruction, its audience, as well as the wider learning ecosystem, has changed. It will be interesting to see which aspects of emergency remote teaching remain in the next generation of education, when the threat of COVID-19 is no longer a factor. But online education will undoubtedly find new audiences. And the flexibility and learning possibilities that have emerged from necessity are likely to shift the expectations of students and educators, diminishing further the line between classroom-based instruction and virtual learning.

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Shifting online during COVID-19: A systematic review of teaching and learning strategies and their outcomes

• Joyce Hwee Ling Koh   ORCID: orcid.org/0000-0001-5626-4927 1 &
• Ben Kei Daniel 1  

International Journal of Educational Technology in Higher Education volume  19 , Article number:  56 ( 2022 ) Cite this article

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This systematic literature review of 36 peer-reviewed empirical articles outlines eight strategies used by higher education lecturers and students to maintain educational continuity during the COVID-19 pandemic since January 2020. The findings show that students’ online access and positive coping strategies could not eradicate their infrastructure and home environment challenges. Lecturers’ learning access equity strategies made learning resources available asynchronously, but having access did not imply that students could effectively self-direct learning. Lecturers designed classroom replication, online practical skills training, online assessment integrity, and student engagement strategies to boost online learning quality, but students who used ineffective online participation strategies had poor engagement. These findings indicate that lecturers and students need to develop more dexterity for adapting and manoeuvring their online strategies across different online teaching and learning modalities. How these online competencies could be developed in higher education are discussed.

Introduction

Higher education institutions have launched new programmes online for three decades, but their integration of online teaching and learning into on-campus programmes remained less cohesive (Kirkwood & Price, 2014 ). Since early 2020, educational institutions have been shifting online in response to the COVID-19 pandemic. Some consider this kind of emergency remote teaching a temporary online shift during a crisis, whereas online learning involves purposive design for online delivery (Hodges et al., 2020 ). Two years into the pandemic, fully online, blended or hybridised modalities are still being used in response to evolving COVID-19 health advisories (Jaschik, 2021 ). Even though standards for the pedagogical, social, administrative, and technical requirements of online learning have already been published before the pandemic (e.g. Bigatel et al., 2012 ; Goodyear et al., 2001 ), the online competencies of lecturers and students remain critical challenges for higher education institutions during the pandemic (Turnbull et al., 2021 ). Emerging systematic literature reviews about higher education online teaching and learning during the pandemic focus on the clinical aspects of health science programmes (see Dedeilia et al., 2020 ; Hao et al., 2022 ; Papa et al., 2022 ). Understanding the strategies used in other programmes and disciplines is critical for outlining higher education lecturers’ and students’ future online competency needs.

This study, therefore, presents a systematic literature review of the teaching and learning strategies that lecturers and students used to shift online in response to the pandemic and their consequent outcomes. The review was conducted through content analysis and thematic analysis of 36 peer-reviewed articles published from January 2020 to December 2021. It discusses how relevant online competencies for lecturers and students can be further developed in higher education.

Methodology

A Systematic and Tripartite Approach (STA) (Daniel & Harland, 2017 ) guided the review process. STA draws from systematic review approaches such as the Cochrane Review Methods, widely used in application-based disciplines such as the health sciences (Chandler & Hopewell, 2013 ). It develops systematic reviews through description (providing a summary of the review), synthesis (logically categorising research reviewed based on related ideas, connections and rationales), and critique (providing evidence to support, discard or offer new ideas about the literature).

Framing the review

The following research questions guided the review:

What strategies did higher education lecturers and students use when they shifted teaching and learning online in response to the pandemic?

What were the outcomes arising from these strategies?

Search strategy

Peer-reviewed articles were identified from databases indexing leading educational journals—Educational Database (ProQuest), Education Research Complete (EBSCOhost), ERIC (ProQuest), Scopus, Web of Science (Core Collection), and ProQuest Central. The following search terms were used to locate articles with empirical evidence of lecturers’ and/or students’ shifting online strategies:

(remote OR virtual OR emergency remote OR online OR digital OR eLearning) AND (teaching strateg* OR learning strateg* OR shifting online) AND (higher education OR tertiary OR university OR college) AND (covid*) AND (success OR challenge OR outcome OR effect OR case OR lesson or evidence OR reflection)

The following were the inclusion and exclusion criteria:

Review period—From January 2020 to December 2021, following the first reported case of COVID-19 (WHO, 2020 ).

Language—Only articles published in the English language were included.

Type of article—In order maintain rigour in the findings, only peer-reviewed journal articles and conference proceedings were included, and non-refereed articles and conference proceedings were excluded. Peer-reviewed articles reporting empirical data from the lecturer and/or student perspectives were included. Editorials and literature reviews were examined to deepen conceptual understanding but excluded from the review.

The article’s focus—Articles with adequate descriptions and evaluation of lecturers’ and students’ online teaching and learning strategies undertaken because of health advisories during the COVID-19 pandemic were included. K-12 studies, higher education studies with data gathered prior to January 2020, studies describing general online learning experiences that did not arise from COVID-19, studies describing the functionalities of online learning technologies, studies about tips and tricks for using online tools during COVID-19, studies about the public health impact of COVID-19, or studies purely describing online learning attitudes or successes and challenges during COVID-19 without corresponding descriptions of teaching and learning strategies and their outcomes were excluded.

A list of 547 articles published between January 2020 and December 2021 were extracted using keyword and manual search with a final list of 36 articles selected for review (see Fig.  1 ). The inclusion and exclusion criteria were applied to the PRISMA process (Moher et al., 2009 ). The articles and a summary of coding are found in Appendix .

Article screening with the PRISMA process

Data analysis

Content analysis (Weber, 1990 ) and thematic analysis (Braun & Clarke, 2006 ) were used to answer the research questions. Pertinent sections of each article outlining lecturers’ and/or students’ shifting online strategies were identified, read and re-read for data familiarisation. The first author used content analysis to generate eight teaching and learning strategies. These were verified through an inter-rater analysis where a random selection of eight articles was recoded by a second-rater (22.22% of total articles) and confirmed with adequate Cohen’s kappas (Teaching strategies: 0.88, Learning strategies: 0.78). Frequency counts were analysed to answer research question 1.

For the second research question, we first categorised the various shifting online outcomes described in each article and coded each outcome as “success”, “challenge”, or “mixed”. Successful outcomes include favourable descriptions of teaching, learning, or assessment experiences, minimal issues with technology/infrastructure, favourable test scores, or reasonable attendance/course completion rates, whereas challenging outcomes suggest otherwise. Mixed outcomes were not a success or challenge, for example, positive and negative experiences during learning, assessment or with learning infrastructure, or mixed learning outcomes such as positive test scores but lower ratings of professional confidence. Frequency distributions were used to compare the overall successes and challenges of shifting online (see Tables 1 and 2 of “ Findings ” section). Following this, the pertinent outcomes associated with each of the eight shifting online strategies were pinpointed through thematic analysis and critical relationships were visualised as theme maps. These were continually reviewed for internal homogeneity and external heterogeneity (Patton, 1990 ). To ensure trustworthiness and reliability (Creswell, 1998 ), there was frequent debriefing between the authors to refine themes and theme maps, followed by critical peer review with another lecturer specialising in higher education educational technology practices. Throughout this process, an audit trail was maintained to document the evolution of themes. These processes completed the description and synthesis aspects of the systematic literature review prior to critique and discussion (Daniel & Harland, 2017 ).

Descriptive characteristics

Descriptive characteristics of the articles are summarised in Table 1 .

Table 1 shows that articles about shifting online during the pandemic were published steadily between August 2020 and December 2021. About two-thirds of the articles were based on data from the United States of America, Asia, or Australasia, with close to 45% of the articles analysing shifting online strategies used in the disciplines of Natural Sciences and Medical and Health Sciences and around 60% focusing on degree programmes. While there was an exact representation of studies with sample sizes from below 50 to above 150, the majority were descriptive studies, with close to half based on quantitative data gathered through surveys. About half of the articles focused on teaching strategies, while around 40% also examined students' learning strategies. However, only about 20% of the articles had theoretical framing for their teaching strategies. Besides using self-developed theories, the authors also used established theories such as the Community of Inquiry Theory by Garrison et. al. ( 2010 ), the Interaction Framework for Distance Education by Moore ( 1989 ), self-regulated learning by Zimmerman ( 2002 ) and the 5E model of Bybee et. al. ( 2006 ). Different types of shifting online outcomes were reported in the articles. The majority documented the positive and negative experiences associated with synchronous or asynchronous online learning activities, online learning technology and infrastructure, or online assessment. A quarter of the articles reported data on student learning outcomes and attendance/completion rates, while a minority also described teaching workload effects. Table 2 shows other successes and challenges associated with shifting online. Of the articles that examined online learning experiences, over a quarter reported clear successes in terms of positive experiences while about half reported mixed experiences. Majority of the articles examining technology and infrastructure experiences or assessment experiences either reported challenging or mixed experiences. All the articles examining learning outcomes reported apparent successes but only half of those investigating attendance/completion rates found these to be acceptable. Only challenges were reported for teaching workload.

Teaching strategies and outcomes

Lecturers used five teaching strategies to shift online during the pandemic (see Table 3 ).

Online practical skills training

Lecturers had to create online practical skills training . With limited access to clinical, field-based, or laboratory settings, lecturers taught only the conceptual aspects of practical skills through online guest lectures, live skill demonstration sessions, video recordings of field trips, conceptual application exercises, or by substituting skills practice with new theoretical topics (Chan et al., 2020 ; de Luca et al., 2021 ; Dietrich et al., 2020 ; Dodson & Blinn, 2021 ; Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Xiao et al., 2020 ). Only in three studies about forest operations, ecology, and nursing was it possible to practice hand skills in alternative locations such as public parks and students’ homes (Dodson & Blinn, 2021 ; Gerhart et al., 2021 ; Palmer et al., 2021 ).

Outcomes : Online practical skills training had different effects on learning experiences, test scores, and attendance/completion rates. Students can attain expected test scores through conceptual learning of practical skills (Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Xiao et al., 2020 ). However, not all students had positive learning experiences as some appreciated deeper conceptual learning, but others felt disconnected from peers, anxious about losing hand skills proficiency, and could not maintain class attendance (de Luca et al., 2021 ; Dietrich et al., 2020 ; Gomez et al., 2020 ). Positive learning experiences, reasonable course attendance/completion rates, and higher confidence in content mastery were more achievable when students had opportunities to practice hand skills in alternative locations (Gerhart et al., 2021 ).

Online assessment integrity

Lecturers had to devise strategies to maintain online assessment integrity , primarily through different ways of preventing cheating (see Reedy et al., 2021 ). Pass/Fail grading, reducing examination weightage through a higher emphasis on daily work and class participation, and asking students to make academic integrity declarations were some changes to examination policies (e.g. Ali et al., 2020 ; Dicks et al., 2020 ). Randomising and scrambling questions, administering different versions of examination papers, using proctoring software, open-book examinations, and replacing multiple choice with written questions were other ways of preventing cheating during online examinations (Hall et al., 2021 ; Jaap et al., 2021 ; Reedy et al., 2021 ).

Outcomes : There was concern that shifting to online assessment had detrimental effects on learning outcomes, but several studies reported otherwise (Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Hall et al., 2021 ; Jaap et al., 2021 ; Lapitan et al., 2021 ). Nevertheless, there were mixed assessment experiences. When lecturers changed multiple-choice to written critical thinking questions, it made students perceive that examinations have become harder (Garcia-Alberti et al., 2021 ; Khan et al., 2022 ). Some students were anxious about encountering technical problems during online examinations, while others felt less nervous taking examinations at home (Jaap et al., 2021 ). Students also became less confident about the integrity of assessment processes when lecturers failed to set clear rules for open-book examinations (Reedy et al., 2021 ). While Pass/Fail grading alleviated students’ test performance anxiety, some lecturers felt that this lowered academic standards (Dicks et al., 2020 ; Khan et al., 2022 ). More emphasis on daily work alleviated student anxiety as examination weightage was reduced, but students also perceived a corresponding increase in course workload as they had more assignments to complete (e.g. Dietrich et al., 2020 ; Swanson et al., 2021 ).

Classroom replication

Lecturers used classroom replication strategies to foster regularity, primarily through substituting classroom sessions with video conferencing under pre-pandemic timetables (Palmer et al., 2021 ; Simon et al., 2020 ; Zhu et al., 2021 ). Lecturers also annotated their presentation materials and decorated their teaching locations with content-related backdrops to emulate the ‘chalk and talk’ of physical classrooms (e.g. Chan et al., 2020 ; Dietrich et al., 2020 ; Xiao et al., 2020 ).

Outcomes : Regular video conferencing classes helped students to maintain course attendance/completion rates (e.g. Ahmed & Opoku, 2021 ; Garcia-Alberti et al., 2021 ; Gerhart et al., 2021 ). Student engagement improved when lecturers annotated on Powerpoint™ or digital whiteboards during video conferencing (Hew et al., 2020 ). However, screen fatigue commonly affected concentration, and lecturers had challenges assessing social cues effectively, especially when students turned off their cameras (Khan et al., 2022 ; Lapitan et al., 2021 ; Marshalsey & Sclater, 2020 ). Lecturers tried to shorten class duration with asynchronous activities, only to find students failing to complete their assigned tasks (Grimmer et al., 2020 ).

Learning access equity

Lecturers implemented learning access equity strategies so that those without stable network connections or conducive home environments could continue studying (Abou-Khalil et al., 2021 ; Ahmed & Opoku, 2021 ; Dodson & Blinn, 2021 ; Garcia-Alberti et al., 2021 ; Grimmer et al., 2020 ; Kapasia et al., 2020 ; Khan et al., 2022 ; Marshalsey & Sclater, 2020 ; Pagoto et al., 2021 ; Swanson et al., 2021 ; Yeung & Yau, 2021 ). They equalised learning access by making lecture recordings available, using chat to communicate during live classes, and providing supplementary asynchronous activities (e.g. Gerhart et al., 2021 ; Grimmer et al., 2020 ). Some lecturers only delivered lessons asynchronously through pre-recorded lectures and online resources (e.g. de Luca et al., 2021 ; Dietrich et al., 2020 ). In developing countries, lecturers created access opportunities by sending learning materials through both learning management systems and WhatsApp™ (Kapasia et al., 2020 ).

Outcomes : Learning access strategies maintained some level of student equity through asynchronous learning but created challenging student learning experiences. There is evidence that students could achieve expected test scores through asynchronous learning (Garcia-Alberti et al., 2021 ) but maintaining learning consistency was a challenge, especially for freshmen (e.g. Grimmer et al., 2020 ; Khan et al., 2022 ). Some students found it hard to understand difficult concepts without in-person lectures but they also did not actively attend the live question-and-answer sessions organised by lecturers (Ali et al., 2020 ; Dietrich et al., 2020 ; Gomez et al., 2020 ). Poorly designed lecture recordings and unclear online learning instructions from lecturers compounded these problems (Gomez et al., 2020 ; Yeung & Yau, 2021 ).

Student engagement

Lecturers used two kinds of student engagement strategies, one of which was through active learning. Hew et. al. ( 2020 ) fostered active learning through 5E activities (Bybee et al., 2006 ) that encouraged students to Engage, Explore, Explain, Elaborate, and Evaluate. Lapitan et. al. ( 2021 ) implemented active learning through their DLPCA process, where students Discover, Learn and Practice outside of class with content resources and Collaborate in class before Assessment. Chan et. al. ( 2020 ) used their Theory of Change to support active learning through shared meaning-making. Other studies emphasised active learning but did not reference theoretical frameworks (e.g. Martinelli & Zaina, 2021 ). Many described how lecturers used interactive tools such as Nearpod™, and Padlet™, online polling, and breakout room discussions to encourage active learning (e.g. Ali et al., 2020 ; Gomez et al., 2020 ).

Another student engagement strategy was through regular communication and support, where lecturers sent emails, announcements, and reminders to keep students in pace with assignments (e.g. Abou-Khalil et al., 2021 ). Support was also provided through virtual office hours, social media contact after class hours and uploading feedback over shared drives (e.g. Khan et al., 2022 ; Xiao et al., 2020 ).

Outcomes : Among the student engagement strategies, success in test scores tends to be associated with the use of active learning (Garcia-Alberti et al., 2021 ; Gomez et al., 2020 ; Hew et al., 2020 ; Lapitan et al., 2021 ; Lau et al., 2020 ; Xiao et al., 2020 ). On the other hand, positive learning experiences were more often reported when lecturers emphasised care and empathy through their communication (e.g. Chan et al., 2020 ; Conklin & Dikkers, 2021 ). Students felt this more strongly when lecturers used humour, conversational and friendly tone, provided assurance, set clear expectations, exercised flexibility, engaged their feedback to improve online lessons, and responded swiftly to their questions (e.g. Chan et al., 2020 ; Swanson et al., 2021 ). These interactions fostered the social presence of Garrison et. al.’s ( 2010 ) Community of Inquiry Theory (Conklin & Dikkers, 2021 ). However, keeping up with multiple communication channels increased teaching workload, especially when support requests arrived through social media after work hours (Garcia-Alberti et al., 2021 ; Khan et al. 2022 ; Marshalsey & Sclater, 2020 ).

Learning strategies and outcomes

Students used three learning strategies during the pandemic (see Table 4 ).

Online access

Students had to maintain online access , as institutional support for data and technology was rarely reported (Ahmed & Opoku, 2021 ; Laher et al., 2021 ). Students did so by switching to more reliable internet service providers, purchasing more data, borrowing computing equipment, or switching off webcams during class (Kapasia et al., 2020 ; Mahmud & German, 2021 ).

Outcomes : Unstable internet connections, noisy home environments, tight study spaces, and disruptions from family duties were challenges often reported in students’ learning environments (e.g. Castelli & Sarvary, 2021 ; Yeung & Yau, 2021 ). The power supply was unstable in developing countries and students also had limited financial resources to purchase data. To keep studying, these students relied on materials shared through WhatsApp™ groups or Google Drive™ and learnt using mobile phones even though their small screen sizes affected students’ learning quality (Kapasia et al., 2020 ).

Online participation

Students had to maintain online participation by redesigning study routines according to when lecturers posted lecture recordings, identifying personal productive hours, changing work locations at home to improve focus and concentration, and devising study strategies to use online resources effectively, such as through note-taking (e.g. Abou-Khalil et al., 2021 ; Mahmud & German, 2021 ; Marshalsey & Sclater, 2020 ). Students also adjusted their online communication style by taking the initiative to contact lecturers through email, discussion forums, or chat for support, and learning new etiquette for video conferencing (Abou-Khalil et al., 2021 ; Dietrich et al., 2020 ; Mahmud & German, 2021 ; Simon et al., 2020 ; Yeung & Yau, 2021 ). Students recognised the need for active online participation (Yeung & Yau, 2021 ) but most tended to switch off webcams and avoided speaking up during class (Ahmed & Opoku, 2021 ; Castelli & Sarvary, 2021 ; Dietrich et al., 2020 ; Khan et al., 2022 ; Lapitan et al., 2021 ; Marshalsey & Sclater, 2020 ; Munoz et al., 2021 ; Rajab & Soheib, 2021 ).

Outcomes : Mahmud and German ( 2021 ) found that students lack the confidence to plan their study strategies, seek help, and manage time. Students also lacked confidence and switched off webcams out of privacy concerns or because they felt self-conscious about their appearances and home environments (Marshalsey & Sclater, 2020 ; Rajab & Soheib, 2021 ). Too many turned off webcams and this became a group norm (Castelli & Sarvary, 2021 ). Classes eventually became dominated by more vocal students, making the quieter ones feel left out (Dietrich et al., 2020 ).

Positive coping

Students’ positive coping strategies included family support, rationalising their situation, focusing on their future, self-motivation, and making virtual social connections with classmates (Ando, 2021 ; Laher et al., 2021 ; Mahmud & German, 2021 ; Reedy et al., 2021 ; Simon et al., 2020 ).

Outcomes : Positive coping strategies helped students to improve learning experiences, maintain attendance/completion rates, and avoid academic integrity violations during online examinations (Ando, 2021 ; Reedy et al., 2021 ; Simon et al., 2020 ). However, these strategies cannot circumvent technology and infrastructure challenges (Mahmud & German, 2021 ), while the realities of economic, family, and health pressures during the pandemic threatened their educational continuity and caused some to manifest negative coping behaviours such as despondency and overeating (Laher et al., 2021 ).

Higher education online competencies

This systematic review outlined eight teaching and learning strategies for shifting online during the pandemic. Online teaching competency frameworks published before the pandemic advocate active learning, social interaction, and prompt feedback as critical indicators of online teaching quality (e.g. Bigatel et al., 2012 ; Crews et al., 2015 ). The findings suggest that lecturers’ student engagement strategies aligned with these standards, but they also needed to adjust practical skills training, assessment, learning access channels, and classroom teaching strategies. Students’ online participation and positive coping strategies reflected how online learners could effectively manage routines, schedules and their sense of isolation (Roper, 2007 ). Since most students had no choice over online learning during the pandemic (Dodson & Blinn, 2021 ), those lacking personal motivation or adequate infrastructure had to develop online participation and online access strategies to cope with the situation.

The eight teaching and learning strategies effectively maintained test scores and attendance/completion rates, but many challenges surfaced during teaching, learning, and assessment. Turnbull et. al. ( 2021 ) attribute lecturers’ and students’ pandemic challenges to online competency gaps, particularly in digital literacy or competencies for accessing information, analysing data, and communicating with technology (Blayone et al., 2018 ). However, the study findings show that digital literacy may not be enough for students to overcome infrastructure and home environment challenges in their learning environment. Lecturers can try helping students mitigate these challenges by providing asynchronous resource access through access equity strategies. Yet, students may not successfully learn asynchronously unless they can effectively self-direct learning. Lecturers may have pedagogical knowledge to create engaging active online learning experiences. How these strategies effectively counteract students’ inhibitions to turn on webcams and speak up during class remains challenging. Lectures may also have the skills to set up different online communication channels, but students may not actively engage if care and empathy are perceived to be lacking. Furthermore, lecturers’ online assessment strategies may not always balance academic integrity with test validity.

These findings show that online competencies are not just standardised technical or pedagogical skills (e.g. Goodyear et al., 2001 ) but “socially situated” (Alvarez et al., 2009 , p. 322) abilities for manoeuvring strategies according to situation and context (Hatano & Inagaki, 1986 ). It encompasses “dexterity” or finesse with skill performance (Merriam-Webster, n.d.). The pandemic demands one to be “flexible and adaptable” (Ally, 2019 , p. 312) amidst shifting national, institutional and learning contexts. Online dexterity is needed in several areas. Online learning during the pandemic is rarely unimodal. Establishing the appropriate synchronous-asynchronous blend is a critical pedagogical decision for lecturers. They need dexterity across learning modalities to create the “right” blend in different student, content, and technological contexts (Baran et al., 2013 ; Martin et al., 2019 ). Lecturers also need domain-related dexterity to preserve authentic learning experiences while converting subject content online (Fayer, 2014 ). Especially when teaching skill-based content under different social distancing requirements, competencies to maintain learning authenticity through simulations, alternative locations, or equipment may be critical (e.g. Schirmel, 2021 ). Dexterity with online assessment is also essential. Besides preventing cheating, lecturers need to ensure that online assessments retain test validity, improve learning processes and are effective for performance evaluation (AERA, 2014 ; Sadler & Reimann, 2018 ). Another area is the dexterity to engage in online communication that appropriately manifests care and empathy (Baran et al., 2013 ). Since online teaching increases lecturers’ workload (Watermeyer et al., 2021 ), dexterity to balance student care and self-care without compromising learning quality is also crucial.

Access to conducive learning environments critically affects students’ online learning success (Kapasia et al., 2020 ). While some infrastructure challenges cannot be prevented, students should have the dexterity to mitigate their effects. For example, when disconnected from class because of bandwidth fluctuations, students should be able to find alternative ways of catching up with the lecturer rather than remaining passive and frustrated (Ezra et al., 2021 ). Self-direction is critical during online learning because it is the ability to set learning goals, self-manage learning processes, self-monitor, self-motivate, and adjust learning strategies (Garrison, 1997 ). Students need the dexterity to manage self-direction processes across different courses, learning modalities, and learning schedules. Dexterity to create an active learning presence through using appropriate learning etiquette and optimising the affordances of text, audio, video, and shared documents during class is also essential. This can support students' cognitive, social, and emotional engagement across synchronous and asynchronous modalities, individually or in groups (Zilvinskis et al., 2017 ).

Future directions

Online learning is highly diverse and increasingly dynamic, making it challenging to cover all published work for review. In this study, we have analysed pandemic-related teaching and learning strategies and their outcomes but recognise that a third of the studies were from the United States and close to half from natural or health science programmes. The findings cannot fully elucidate the strategies implemented in unrepresented countries or disciplines. Recognising these limitations, we propose the following as future directions for higher education:

Validate post-pandemic relevance of online teaching and learning strategies

The eight strategies can be validated through longitudinal empirical studies, theoretical analyses or meta-synthesis of literature to establish their relevance for post-pandemic teaching and learning. Studies outside the United States and the natural and health science disciplines are especially needed. This could address the paucity of theoretical framing in the articles reviewed, even with theories developed before the pandemic (e.g. Garrison et al., 2010 ; Moore, 1989 ; Zimmerman, 2002 ).

Demarcate post-pandemic online competencies

The plethora of descriptive studies in the articles reviewed is inadequate for understanding the online competencies driving lecturers’ pedagogical decision-making and students’ learning processes. In situ studies adopting qualitative methods such as grounded theory or phenomenology can better demarcate lecturers’ and students’ competencies for “why and under which conditions certain methods have to be used, or new methods have to be devised” (Bohle Carbonell et al., 2014 , p. 15). A longitudinal comparison of these studies can provide a better understanding of relevant post-pandemic competencies.

Develop dexterity with respect to application of online competencies

Higher education institutions use technology workshops, mentoring, and instructional consultation to develop competencies in technology-enhanced learning (e.g. Baran, 2016 ). However, dexterity to manoeuvre contextual differences may be better fostered through exploration, discovery, and exposure to varied contexts of practice (Mylopoulos et al., 2018 ). Innovative ways of developing dexterity with respect to how online competencies can be applied and the efficacy of these methodologies are areas for further research.

The COVID-19 pandemic has significantly increased the adoption and utilisation of online learning. While the present review findings suggest that the strategies lecturers and students employed to shift online during the pandemic have contributed to maintaining educational continuity and test scores but many outstanding issues remained unresolved. These include failure for students to gain an enhanced learning experience, problems encountered in designing and implementing robust assessment and online examinations, cases of academic misconduct, inequitable access to digital technologies, and increased faculty workload. Lecturers and institutions need to tackle these issues to fully leverage the opportunities afforded by online teaching and learning. Further, our findings revealed that the level of online dexterity for both students and teachers need to be enhanced. Therefore, higher education institutions must understand and develop online dexterity institutional frameworks to ensure that pedagogical innovation through online learning can be continually sustained, both during the pandemic and beyond.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

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Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines

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Recently, the education system has faced an unprecedented health crisis that has shaken up its foundation. Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. Although many studies have investigated this area, limited information is available regarding the challenges and the specific strategies that students employ to overcome them. Thus, this study attempts to fill in the void. Using a mixed-methods approach, the findings revealed that the online learning challenges of college students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. The findings further revealed that the COVID-19 pandemic had the greatest impact on the quality of the learning experience and students’ mental health. In terms of strategies employed by students, the most frequently used were resource management and utilization, help-seeking, technical aptitude enhancement, time management, and learning environment control. Implications for classroom practice, policy-making, and future research are discussed.

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1 Introduction

Since the 1990s, the world has seen significant changes in the landscape of education as a result of the ever-expanding influence of technology. One such development is the adoption of online learning across different learning contexts, whether formal or informal, academic and non-academic, and residential or remotely. We began to witness schools, teachers, and students increasingly adopt e-learning technologies that allow teachers to deliver instruction interactively, share resources seamlessly, and facilitate student collaboration and interaction (Elaish et al., 2019 ; Garcia et al., 2018 ). Although the efficacy of online learning has long been acknowledged by the education community (Barrot, 2020 , 2021 ; Cavanaugh et al., 2009 ; Kebritchi et al., 2017 ; Tallent-Runnels et al., 2006 ; Wallace, 2003 ), evidence on the challenges in its implementation continues to build up (e.g., Boelens et al., 2017 ; Rasheed et al., 2020 ).

Recently, the education system has faced an unprecedented health crisis (i.e., COVID-19 pandemic) that has shaken up its foundation. Thus, various governments across the globe have launched a crisis response to mitigate the adverse impact of the pandemic on education. This response includes, but is not limited to, curriculum revisions, provision for technological resources and infrastructure, shifts in the academic calendar, and policies on instructional delivery and assessment. Inevitably, these developments compelled educational institutions to migrate to full online learning until face-to-face instruction is allowed. The current circumstance is unique as it could aggravate the challenges experienced during online learning due to restrictions in movement and health protocols (Gonzales et al., 2020 ; Kapasia et al., 2020 ). Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. To date, many studies have investigated this area with a focus on students’ mental health (Copeland et al., 2021 ; Fawaz et al., 2021 ), home learning (Suryaman et al., 2020 ), self-regulation (Carter et al., 2020 ), virtual learning environment (Almaiah et al., 2020 ; Hew et al., 2020 ; Tang et al., 2020 ), and students’ overall learning experience (e.g., Adarkwah, 2021 ; Day et al., 2021 ; Khalil et al., 2020 ; Singh et al., 2020 ). There are two key differences that set the current study apart from the previous studies. First, it sheds light on the direct impact of the pandemic on the challenges that students experience in an online learning space. Second, the current study explores students’ coping strategies in this new learning setup. Addressing these areas would shed light on the extent of challenges that students experience in a full online learning space, particularly within the context of the pandemic. Meanwhile, our nuanced understanding of the strategies that students use to overcome their challenges would provide relevant information to school administrators and teachers to better support the online learning needs of students. This information would also be critical in revisiting the typology of strategies in an online learning environment.

2 Literature review

2.1 education and the covid-19 pandemic.

In December 2019, an outbreak of a novel coronavirus, known as COVID-19, occurred in China and has spread rapidly across the globe within a few months. COVID-19 is an infectious disease caused by a new strain of coronavirus that attacks the respiratory system (World Health Organization, 2020 ). As of January 2021, COVID-19 has infected 94 million people and has caused 2 million deaths in 191 countries and territories (John Hopkins University, 2021 ). This pandemic has created a massive disruption of the educational systems, affecting over 1.5 billion students. It has forced the government to cancel national examinations and the schools to temporarily close, cease face-to-face instruction, and strictly observe physical distancing. These events have sparked the digital transformation of higher education and challenged its ability to respond promptly and effectively. Schools adopted relevant technologies, prepared learning and staff resources, set systems and infrastructure, established new teaching protocols, and adjusted their curricula. However, the transition was smooth for some schools but rough for others, particularly those from developing countries with limited infrastructure (Pham & Nguyen, 2020 ; Simbulan, 2020 ).

Inevitably, schools and other learning spaces were forced to migrate to full online learning as the world continues the battle to control the vicious spread of the virus. Online learning refers to a learning environment that uses the Internet and other technological devices and tools for synchronous and asynchronous instructional delivery and management of academic programs (Usher & Barak, 2020 ; Huang, 2019 ). Synchronous online learning involves real-time interactions between the teacher and the students, while asynchronous online learning occurs without a strict schedule for different students (Singh & Thurman, 2019 ). Within the context of the COVID-19 pandemic, online learning has taken the status of interim remote teaching that serves as a response to an exigency. However, the migration to a new learning space has faced several major concerns relating to policy, pedagogy, logistics, socioeconomic factors, technology, and psychosocial factors (Donitsa-Schmidt & Ramot, 2020 ; Khalil et al., 2020 ; Varea & González-Calvo, 2020 ). With reference to policies, government education agencies and schools scrambled to create fool-proof policies on governance structure, teacher management, and student management. Teachers, who were used to conventional teaching delivery, were also obliged to embrace technology despite their lack of technological literacy. To address this problem, online learning webinars and peer support systems were launched. On the part of the students, dropout rates increased due to economic, psychological, and academic reasons. Academically, although it is virtually possible for students to learn anything online, learning may perhaps be less than optimal, especially in courses that require face-to-face contact and direct interactions (Franchi, 2020 ).

2.2 Related studies

Recently, there has been an explosion of studies relating to the new normal in education. While many focused on national policies, professional development, and curriculum, others zeroed in on the specific learning experience of students during the pandemic. Among these are Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ) who examined the impact of COVID-19 on college students’ mental health and their coping mechanisms. Copeland et al. ( 2021 ) reported that the pandemic adversely affected students’ behavioral and emotional functioning, particularly attention and externalizing problems (i.e., mood and wellness behavior), which were caused by isolation, economic/health effects, and uncertainties. In Fawaz et al.’s ( 2021 ) study, students raised their concerns on learning and evaluation methods, overwhelming task load, technical difficulties, and confinement. To cope with these problems, students actively dealt with the situation by seeking help from their teachers and relatives and engaging in recreational activities. These active-oriented coping mechanisms of students were aligned with Carter et al.’s ( 2020 ), who explored students’ self-regulation strategies.

In another study, Tang et al. ( 2020 ) examined the efficacy of different online teaching modes among engineering students. Using a questionnaire, the results revealed that students were dissatisfied with online learning in general, particularly in the aspect of communication and question-and-answer modes. Nonetheless, the combined model of online teaching with flipped classrooms improved students’ attention, academic performance, and course evaluation. A parallel study was undertaken by Hew et al. ( 2020 ), who transformed conventional flipped classrooms into fully online flipped classes through a cloud-based video conferencing app. Their findings suggested that these two types of learning environments were equally effective. They also offered ways on how to effectively adopt videoconferencing-assisted online flipped classrooms. Unlike the two studies, Suryaman et al. ( 2020 ) looked into how learning occurred at home during the pandemic. Their findings showed that students faced many obstacles in a home learning environment, such as lack of mastery of technology, high Internet cost, and limited interaction/socialization between and among students. In a related study, Kapasia et al. ( 2020 ) investigated how lockdown impacts students’ learning performance. Their findings revealed that the lockdown made significant disruptions in students’ learning experience. The students also reported some challenges that they faced during their online classes. These include anxiety, depression, poor Internet service, and unfavorable home learning environment, which were aggravated when students are marginalized and from remote areas. Contrary to Kapasia et al.’s ( 2020 ) findings, Gonzales et al. ( 2020 ) found that confinement of students during the pandemic had significant positive effects on their performance. They attributed these results to students’ continuous use of learning strategies which, in turn, improved their learning efficiency.

Finally, there are those that focused on students’ overall online learning experience during the COVID-19 pandemic. One such study was that of Singh et al. ( 2020 ), who examined students’ experience during the COVID-19 pandemic using a quantitative descriptive approach. Their findings indicated that students appreciated the use of online learning during the pandemic. However, half of them believed that the traditional classroom setting was more effective than the online learning platform. Methodologically, the researchers acknowledge that the quantitative nature of their study restricts a deeper interpretation of the findings. Unlike the above study, Khalil et al. ( 2020 ) qualitatively explored the efficacy of synchronized online learning in a medical school in Saudi Arabia. The results indicated that students generally perceive synchronous online learning positively, particularly in terms of time management and efficacy. However, they also reported technical (internet connectivity and poor utility of tools), methodological (content delivery), and behavioral (individual personality) challenges. Their findings also highlighted the failure of the online learning environment to address the needs of courses that require hands-on practice despite efforts to adopt virtual laboratories. In a parallel study, Adarkwah ( 2021 ) examined students’ online learning experience during the pandemic using a narrative inquiry approach. The findings indicated that Ghanaian students considered online learning as ineffective due to several challenges that they encountered. Among these were lack of social interaction among students, poor communication, lack of ICT resources, and poor learning outcomes. More recently, Day et al. ( 2021 ) examined the immediate impact of COVID-19 on students’ learning experience. Evidence from six institutions across three countries revealed some positive experiences and pre-existing inequities. Among the reported challenges are lack of appropriate devices, poor learning space at home, stress among students, and lack of fieldwork and access to laboratories.

Although there are few studies that report the online learning challenges that higher education students experience during the pandemic, limited information is available regarding the specific strategies that they use to overcome them. It is in this context that the current study was undertaken. This mixed-methods study investigates students’ online learning experience in higher education. Specifically, the following research questions are addressed: (1) What is the extent of challenges that students experience in an online learning environment? (2) How did the COVID-19 pandemic impact the online learning challenges that students experience? (3) What strategies did students use to overcome the challenges?

2.3 Conceptual framework

The typology of challenges examined in this study is largely based on Rasheed et al.’s ( 2020 ) review of students’ experience in an online learning environment. These challenges are grouped into five general clusters, namely self-regulation (SRC), technological literacy and competency (TLCC), student isolation (SIC), technological sufficiency (TSC), and technological complexity (TCC) challenges (Rasheed et al., 2020 , p. 5). SRC refers to a set of behavior by which students exercise control over their emotions, actions, and thoughts to achieve learning objectives. TLCC relates to a set of challenges about students’ ability to effectively use technology for learning purposes. SIC relates to the emotional discomfort that students experience as a result of being lonely and secluded from their peers. TSC refers to a set of challenges that students experience when accessing available online technologies for learning. Finally, there is TCC which involves challenges that students experience when exposed to complex and over-sufficient technologies for online learning.

To extend Rasheed et al. ( 2020 ) categories and to cover other potential challenges during online classes, two more clusters were added, namely learning resource challenges (LRC) and learning environment challenges (LEC) (Buehler, 2004 ; Recker et al., 2004 ; Seplaki et al., 2014 ; Xue et al., 2020 ). LRC refers to a set of challenges that students face relating to their use of library resources and instructional materials, whereas LEC is a set of challenges that students experience related to the condition of their learning space that shapes their learning experiences, beliefs, and attitudes. Since learning environment at home and learning resources available to students has been reported to significantly impact the quality of learning and their achievement of learning outcomes (Drane et al., 2020 ; Suryaman et al., 2020 ), the inclusion of LRC and LEC would allow us to capture other important challenges that students experience during the pandemic, particularly those from developing regions. This comprehensive list would provide us a clearer and detailed picture of students’ experiences when engaged in online learning in an emergency. Given the restrictions in mobility at macro and micro levels during the pandemic, it is also expected that such conditions would aggravate these challenges. Therefore, this paper intends to understand these challenges from students’ perspectives since they are the ones that are ultimately impacted when the issue is about the learning experience. We also seek to explore areas that provide inconclusive findings, thereby setting the path for future research.

3 Material and methods

The present study adopted a descriptive, mixed-methods approach to address the research questions. This approach allowed the researchers to collect complex data about students’ experience in an online learning environment and to clearly understand the phenomena from their perspective.

3.1 Participants

This study involved 200 (66 male and 134 female) students from a private higher education institution in the Philippines. These participants were Psychology, Physical Education, and Sports Management majors whose ages ranged from 17 to 25 ( x̅  = 19.81; SD  = 1.80). The students have been engaged in online learning for at least two terms in both synchronous and asynchronous modes. The students belonged to low- and middle-income groups but were equipped with the basic online learning equipment (e.g., computer, headset, speakers) and computer skills necessary for their participation in online classes. Table 1 shows the primary and secondary platforms that students used during their online classes. The primary platforms are those that are formally adopted by teachers and students in a structured academic context, whereas the secondary platforms are those that are informally and spontaneously used by students and teachers for informal learning and to supplement instructional delivery. Note that almost all students identified MS Teams as their primary platform because it is the official learning management system of the university.

Informed consent was sought from the participants prior to their involvement. Before students signed the informed consent form, they were oriented about the objectives of the study and the extent of their involvement. They were also briefed about the confidentiality of information, their anonymity, and their right to refuse to participate in the investigation. Finally, the participants were informed that they would incur no additional cost from their participation.

3.2 Instrument and data collection

The data were collected using a retrospective self-report questionnaire and a focused group discussion (FGD). A self-report questionnaire was considered appropriate because the indicators relate to affective responses and attitude (Araujo et al., 2017 ; Barrot, 2016 ; Spector, 1994 ). Although the participants may tell more than what they know or do in a self-report survey (Matsumoto, 1994 ), this challenge was addressed by explaining to them in detail each of the indicators and using methodological triangulation through FGD. The questionnaire was divided into four sections: (1) participant’s personal information section, (2) the background information on the online learning environment, (3) the rating scale section for the online learning challenges, (4) the open-ended section. The personal information section asked about the students’ personal information (name, school, course, age, and sex), while the background information section explored the online learning mode and platforms (primary and secondary) used in class, and students’ length of engagement in online classes. The rating scale section contained 37 items that relate to SRC (6 items), TLCC (10 items), SIC (4 items), TSC (6 items), TCC (3 items), LRC (4 items), and LEC (4 items). The Likert scale uses six scores (i.e., 5– to a very great extent , 4– to a great extent , 3– to a moderate extent , 2– to some extent , 1– to a small extent , and 0 –not at all/negligible ) assigned to each of the 37 items. Finally, the open-ended questions asked about other challenges that students experienced, the impact of the pandemic on the intensity or extent of the challenges they experienced, and the strategies that the participants employed to overcome the eight different types of challenges during online learning. Two experienced educators and researchers reviewed the questionnaire for clarity, accuracy, and content and face validity. The piloting of the instrument revealed that the tool had good internal consistency (Cronbach’s α = 0.96).

The FGD protocol contains two major sections: the participants’ background information and the main questions. The background information section asked about the students’ names, age, courses being taken, online learning mode used in class. The items in the main questions section covered questions relating to the students’ overall attitude toward online learning during the pandemic, the reasons for the scores they assigned to each of the challenges they experienced, the impact of the pandemic on students’ challenges, and the strategies they employed to address the challenges. The same experts identified above validated the FGD protocol.

Both the questionnaire and the FGD were conducted online via Google survey and MS Teams, respectively. It took approximately 20 min to complete the questionnaire, while the FGD lasted for about 90 min. Students were allowed to ask for clarification and additional explanations relating to the questionnaire content, FGD, and procedure. Online surveys and interview were used because of the ongoing lockdown in the city. For the purpose of triangulation, 20 (10 from Psychology and 10 from Physical Education and Sports Management) randomly selected students were invited to participate in the FGD. Two separate FGDs were scheduled for each group and were facilitated by researcher 2 and researcher 3, respectively. The interviewers ensured that the participants were comfortable and open to talk freely during the FGD to avoid social desirability biases (Bergen & Labonté, 2020 ). These were done by informing the participants that there are no wrong responses and that their identity and responses would be handled with the utmost confidentiality. With the permission of the participants, the FGD was recorded to ensure that all relevant information was accurately captured for transcription and analysis.

3.3 Data analysis

To address the research questions, we used both quantitative and qualitative analyses. For the quantitative analysis, we entered all the data into an excel spreadsheet. Then, we computed the mean scores ( M ) and standard deviations ( SD ) to determine the level of challenges experienced by students during online learning. The mean score for each descriptor was interpreted using the following scheme: 4.18 to 5.00 ( to a very great extent ), 3.34 to 4.17 ( to a great extent ), 2.51 to 3.33 ( to a moderate extent ), 1.68 to 2.50 ( to some extent ), 0.84 to 1.67 ( to a small extent ), and 0 to 0.83 ( not at all/negligible ). The equal interval was adopted because it produces more reliable and valid information than other types of scales (Cicchetti et al., 2006 ).

For the qualitative data, we analyzed the students’ responses in the open-ended questions and the transcribed FGD using the predetermined categories in the conceptual framework. Specifically, we used multilevel coding in classifying the codes from the transcripts (Birks & Mills, 2011 ). To do this, we identified the relevant codes from the responses of the participants and categorized these codes based on the similarities or relatedness of their properties and dimensions. Then, we performed a constant comparative and progressive analysis of cases to allow the initially identified subcategories to emerge and take shape. To ensure the reliability of the analysis, two coders independently analyzed the qualitative data. Both coders familiarize themselves with the purpose, research questions, research method, and codes and coding scheme of the study. They also had a calibration session and discussed ways on how they could consistently analyze the qualitative data. Percent of agreement between the two coders was 86 percent. Any disagreements in the analysis were discussed by the coders until an agreement was achieved.

This study investigated students’ online learning experience in higher education within the context of the pandemic. Specifically, we identified the extent of challenges that students experienced, how the COVID-19 pandemic impacted their online learning experience, and the strategies that they used to confront these challenges.

4.1 The extent of students’ online learning challenges

Table 2 presents the mean scores and SD for the extent of challenges that students’ experienced during online learning. Overall, the students experienced the identified challenges to a moderate extent ( x̅  = 2.62, SD  = 1.03) with scores ranging from x̅  = 1.72 ( to some extent ) to x̅  = 3.58 ( to a great extent ). More specifically, the greatest challenge that students experienced was related to the learning environment ( x̅  = 3.49, SD  = 1.27), particularly on distractions at home, limitations in completing the requirements for certain subjects, and difficulties in selecting the learning areas and study schedule. It is, however, found that the least challenge was on technological literacy and competency ( x̅  = 2.10, SD  = 1.13), particularly on knowledge and training in the use of technology, technological intimidation, and resistance to learning technologies. Other areas that students experienced the least challenge are Internet access under TSC and procrastination under SRC. Nonetheless, nearly half of the students’ responses per indicator rated the challenges they experienced as moderate (14 of the 37 indicators), particularly in TCC ( x̅  = 2.51, SD  = 1.31), SIC ( x̅  = 2.77, SD  = 1.34), and LRC ( x̅  = 2.93, SD  = 1.31).

Out of 200 students, 181 responded to the question about other challenges that they experienced. Most of their responses were already covered by the seven predetermined categories, except for 18 responses related to physical discomfort ( N  = 5) and financial challenges ( N  = 13). For instance, S108 commented that “when it comes to eyes and head, my eyes and head get ache if the session of class was 3 h straight in front of my gadget.” In the same vein, S194 reported that “the long exposure to gadgets especially laptop, resulting in body pain & headaches.” With reference to physical financial challenges, S66 noted that “not all the time I have money to load”, while S121 claimed that “I don't know until when are we going to afford budgeting our money instead of buying essentials.”

4.2 Impact of the pandemic on students’ online learning challenges

Another objective of this study was to identify how COVID-19 influenced the online learning challenges that students experienced. As shown in Table 3 , most of the students’ responses were related to teaching and learning quality ( N  = 86) and anxiety and other mental health issues ( N  = 52). Regarding the adverse impact on teaching and learning quality, most of the comments relate to the lack of preparation for the transition to online platforms (e.g., S23, S64), limited infrastructure (e.g., S13, S65, S99, S117), and poor Internet service (e.g., S3, S9, S17, S41, S65, S99). For the anxiety and mental health issues, most students reported that the anxiety, boredom, sadness, and isolation they experienced had adversely impacted the way they learn (e.g., S11, S130), completing their tasks/activities (e.g., S56, S156), and their motivation to continue studying (e.g., S122, S192). The data also reveal that COVID-19 aggravated the financial difficulties experienced by some students ( N  = 16), consequently affecting their online learning experience. This financial impact mainly revolved around the lack of funding for their online classes as a result of their parents’ unemployment and the high cost of Internet data (e.g., S18, S113, S167). Meanwhile, few concerns were raised in relation to COVID-19’s impact on mobility ( N  = 7) and face-to-face interactions ( N  = 7). For instance, some commented that the lack of face-to-face interaction with her classmates had a detrimental effect on her learning (S46) and socialization skills (S36), while others reported that restrictions in mobility limited their learning experience (S78, S110). Very few comments were related to no effect ( N  = 4) and positive effect ( N  = 2). The above findings suggest the pandemic had additive adverse effects on students’ online learning experience.

4.3 Students’ strategies to overcome challenges in an online learning environment

The third objective of this study is to identify the strategies that students employed to overcome the different online learning challenges they experienced. Table 4 presents that the most commonly used strategies used by students were resource management and utilization ( N  = 181), help-seeking ( N  = 155), technical aptitude enhancement ( N  = 122), time management ( N  = 98), and learning environment control ( N  = 73). Not surprisingly, the top two strategies were also the most consistently used across different challenges. However, looking closely at each of the seven challenges, the frequency of using a particular strategy varies. For TSC and LRC, the most frequently used strategy was resource management and utilization ( N  = 52, N  = 89, respectively), whereas technical aptitude enhancement was the students’ most preferred strategy to address TLCC ( N  = 77) and TCC ( N  = 38). In the case of SRC, SIC, and LEC, the most frequently employed strategies were time management ( N  = 71), psychological support ( N  = 53), and learning environment control ( N  = 60). In terms of consistency, help-seeking appears to be the most consistent across the different challenges in an online learning environment. Table 4 further reveals that strategies used by students within a specific type of challenge vary.

5 Discussion and conclusions

The current study explores the challenges that students experienced in an online learning environment and how the pandemic impacted their online learning experience. The findings revealed that the online learning challenges of students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. Based on the students’ responses, their challenges were also found to be aggravated by the pandemic, especially in terms of quality of learning experience, mental health, finances, interaction, and mobility. With reference to previous studies (i.e., Adarkwah, 2021 ; Copeland et al., 2021 ; Day et al., 2021 ; Fawaz et al., 2021 ; Kapasia et al., 2020 ; Khalil et al., 2020 ; Singh et al., 2020 ), the current study has complemented their findings on the pedagogical, logistical, socioeconomic, technological, and psychosocial online learning challenges that students experience within the context of the COVID-19 pandemic. Further, this study extended previous studies and our understanding of students’ online learning experience by identifying both the presence and extent of online learning challenges and by shedding light on the specific strategies they employed to overcome them.

Overall findings indicate that the extent of challenges and strategies varied from one student to another. Hence, they should be viewed as a consequence of interaction several many factors. Students’ responses suggest that their online learning challenges and strategies were mediated by the resources available to them, their interaction with their teachers and peers, and the school’s existing policies and guidelines for online learning. In the context of the pandemic, the imposed lockdowns and students’ socioeconomic condition aggravated the challenges that students experience.

While most studies revealed that technology use and competency were the most common challenges that students face during the online classes (see Rasheed et al., 2020 ), the case is a bit different in developing countries in times of pandemic. As the findings have shown, the learning environment is the greatest challenge that students needed to hurdle, particularly distractions at home (e.g., noise) and limitations in learning space and facilities. This data suggests that online learning challenges during the pandemic somehow vary from the typical challenges that students experience in a pre-pandemic online learning environment. One possible explanation for this result is that restriction in mobility may have aggravated this challenge since they could not go to the school or other learning spaces beyond the vicinity of their respective houses. As shown in the data, the imposition of lockdown restricted students’ learning experience (e.g., internship and laboratory experiments), limited their interaction with peers and teachers, caused depression, stress, and anxiety among students, and depleted the financial resources of those who belong to lower-income group. All of these adversely impacted students’ learning experience. This finding complemented earlier reports on the adverse impact of lockdown on students’ learning experience and the challenges posed by the home learning environment (e.g., Day et al., 2021 ; Kapasia et al., 2020 ). Nonetheless, further studies are required to validate the impact of restrictions on mobility on students’ online learning experience. The second reason that may explain the findings relates to students’ socioeconomic profile. Consistent with the findings of Adarkwah ( 2021 ) and Day et al. ( 2021 ), the current study reveals that the pandemic somehow exposed the many inequities in the educational systems within and across countries. In the case of a developing country, families from lower socioeconomic strata (as in the case of the students in this study) have limited learning space at home, access to quality Internet service, and online learning resources. This is the reason the learning environment and learning resources recorded the highest level of challenges. The socioeconomic profile of the students (i.e., low and middle-income group) is the same reason financial problems frequently surfaced from their responses. These students frequently linked the lack of financial resources to their access to the Internet, educational materials, and equipment necessary for online learning. Therefore, caution should be made when interpreting and extending the findings of this study to other contexts, particularly those from higher socioeconomic strata.

Among all the different online learning challenges, the students experienced the least challenge on technological literacy and competency. This is not surprising considering a plethora of research confirming Gen Z students’ (born since 1996) high technological and digital literacy (Barrot, 2018 ; Ng, 2012 ; Roblek et al., 2019 ). Regarding the impact of COVID-19 on students’ online learning experience, the findings reveal that teaching and learning quality and students’ mental health were the most affected. The anxiety that students experienced does not only come from the threats of COVID-19 itself but also from social and physical restrictions, unfamiliarity with new learning platforms, technical issues, and concerns about financial resources. These findings are consistent with that of Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ), who reported the adverse effects of the pandemic on students’ mental and emotional well-being. This data highlights the need to provide serious attention to the mediating effects of mental health, restrictions in mobility, and preparedness in delivering online learning.

Nonetheless, students employed a variety of strategies to overcome the challenges they faced during online learning. For instance, to address the home learning environment problems, students talked to their family (e.g., S12, S24), transferred to a quieter place (e.g., S7, S 26), studied at late night where all family members are sleeping already (e.g., S51), and consulted with their classmates and teachers (e.g., S3, S9, S156, S193). To overcome the challenges in learning resources, students used the Internet (e.g., S20, S27, S54, S91), joined Facebook groups that share free resources (e.g., S5), asked help from family members (e.g., S16), used resources available at home (e.g., S32), and consulted with the teachers (e.g., S124). The varying strategies of students confirmed earlier reports on the active orientation that students take when faced with academic- and non-academic-related issues in an online learning space (see Fawaz et al., 2021 ). The specific strategies that each student adopted may have been shaped by different factors surrounding him/her, such as available resources, student personality, family structure, relationship with peers and teacher, and aptitude. To expand this study, researchers may further investigate this area and explore how and why different factors shape their use of certain strategies.

Several implications can be drawn from the findings of this study. First, this study highlighted the importance of emergency response capability and readiness of higher education institutions in case another crisis strikes again. Critical areas that need utmost attention include (but not limited to) national and institutional policies, protocol and guidelines, technological infrastructure and resources, instructional delivery, staff development, potential inequalities, and collaboration among key stakeholders (i.e., parents, students, teachers, school leaders, industry, government education agencies, and community). Second, the findings have expanded our understanding of the different challenges that students might confront when we abruptly shift to full online learning, particularly those from countries with limited resources, poor Internet infrastructure, and poor home learning environment. Schools with a similar learning context could use the findings of this study in developing and enhancing their respective learning continuity plans to mitigate the adverse impact of the pandemic. This study would also provide students relevant information needed to reflect on the possible strategies that they may employ to overcome the challenges. These are critical information necessary for effective policymaking, decision-making, and future implementation of online learning. Third, teachers may find the results useful in providing proper interventions to address the reported challenges, particularly in the most critical areas. Finally, the findings provided us a nuanced understanding of the interdependence of learning tools, learners, and learning outcomes within an online learning environment; thus, giving us a multiperspective of hows and whys of a successful migration to full online learning.

Some limitations in this study need to be acknowledged and addressed in future studies. One limitation of this study is that it exclusively focused on students’ perspectives. Future studies may widen the sample by including all other actors taking part in the teaching–learning process. Researchers may go deeper by investigating teachers’ views and experience to have a complete view of the situation and how different elements interact between them or affect the others. Future studies may also identify some teacher-related factors that could influence students’ online learning experience. In the case of students, their age, sex, and degree programs may be examined in relation to the specific challenges and strategies they experience. Although the study involved a relatively large sample size, the participants were limited to college students from a Philippine university. To increase the robustness of the findings, future studies may expand the learning context to K-12 and several higher education institutions from different geographical regions. As a final note, this pandemic has undoubtedly reshaped and pushed the education system to its limits. However, this unprecedented event is the same thing that will make the education system stronger and survive future threats.

Availability of data and materials

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

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Jessie S. Barrot, Ian I. Llenares & Leo S. del Rosario

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Jessie Barrot led the planning, prepared the instrument, wrote the report, and processed and analyzed data. Ian Llenares participated in the planning, fielded the instrument, processed and analyzed data, reviewed the instrument, and contributed to report writing. Leo del Rosario participated in the planning, fielded the instrument, processed and analyzed data, reviewed the instrument, and contributed to report writing.

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Barrot, J.S., Llenares, I.I. & del Rosario, L.S. Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines. Educ Inf Technol 26 , 7321–7338 (2021). https://doi.org/10.1007/s10639-021-10589-x

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Received : 22 January 2021

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Published : 28 May 2021

Issue Date : November 2021

DOI : https://doi.org/10.1007/s10639-021-10589-x

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