online learning during pandemic essay spm

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Open Access

Peer-reviewed

Research Article

COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis

Roles Data curation, Formal analysis, Methodology, Writing – review & editing

¶ ‡ JZ and YD are contributed equally to this work as first authors.

Affiliation School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliations School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China, Hangzhou Zhongce Vocational School Qiantang, Hangzhou, Zhejiang, China

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

Affiliation Faculty of Education, Shenzhen University, Shenzhen, Guangdong, China

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected] (JH); [email protected] (YZ)

• Junyi Zhang, 
• Yigang Ding, 
• Xinru Yang, 
• Jinping Zhong, 
• XinXin Qiu, 
• Zhishan Zou, 
• Yujie Xu, 
• Xiunan Jin, 
• Xiaomin Wu, 

• Published: August 23, 2022
• https://doi.org/10.1371/journal.pone.0273016
• Reader Comments

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students’ online learning behavior before and after the outbreak. We collected review data from China’s massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses’ interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic’s role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

Citation: Zhang J, Ding Y, Yang X, Zhong J, Qiu X, Zou Z, et al. (2022) COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis. PLoS ONE 17(8): e0273016. https://doi.org/10.1371/journal.pone.0273016

Editor: Heng Luo, Central China Normal University, CHINA

Received: April 20, 2022; Accepted: July 29, 2022; Published: August 23, 2022

Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study were downloaded from https://www.icourse163.org/ and are now shared fully on Github ( https://github.com/zjyzhangjunyi/dataset-from-icourse163-for-SNA ). These data have no private information and can be used for academic research free of charge.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The development of the mobile internet has spurred rapid advances in online learning, offering novel prospects for teaching and learning and a learning experience completely different from traditional instruction. Online learning harnesses the advantages of network technology and multimedia technology to transcend the boundaries of conventional education [ 1 ]. Online courses have become a popular learning mode owing to their flexibility and openness. During online learning, teachers and students are in different physical locations but interact in multiple ways (e.g., via online forum discussions and asynchronous group discussions). An analysis of online learning therefore calls for attention to students’ participation. Alqurashi [ 2 ] defined interaction in online learning as the process of constructing meaningful information and thought exchanges between more than two people; such interaction typically occurs between teachers and learners, learners and learners, and the course content and learners.

Massive open online courses (MOOCs), a 21st-century teaching mode, have greatly influenced global education. Data released by China’s Ministry of Education in 2020 show that the country ranks first globally in the number and scale of higher education MOOCs. The COVID-19 outbreak has further propelled this learning mode, with universities being urged to leverage MOOCs and other online resource platforms to respond to government’s “School’s Out, But Class’s On” policy [ 3 ]. Besides MOOCs, to reduce in-person gatherings and curb the spread of COVID-19, various online learning methods have since become ubiquitous [ 4 ]. Though Lederman asserted that the COVID-19 outbreak has positioned online learning technologies as the best way for teachers and students to obtain satisfactory learning experiences [ 5 ], it remains unclear whether the COVID-19 pandemic has encouraged interaction in online learning, as interactions between students and others play key roles in academic performance and largely determine the quality of learning experiences [ 6 ]. Similarly, it is also unclear what impact the COVID-19 pandemic has had on the scale of online learning.

Social constructivism paints learning as a social phenomenon. As such, analyzing the social structures or patterns that emerge during the learning process can shed light on learning-based interaction [ 7 ]. Social network analysis helps to explain how a social network, rooted in interactions between learners and their peers, guides individuals’ behavior, emotions, and outcomes. This analytical approach is especially useful for evaluating interactive relationships between network members [ 8 ]. Mohammed cited social network analysis (SNA) as a method that can provide timely information about students, learning communities and interactive networks. SNA has been applied in numerous fields, including education, to identify the number and characteristics of interelement relationships. For example, Lee et al. also used SNA to explore the effects of blogs on peer relationships [ 7 ]. Therefore, adopting SNA to examine interactions in online learning communities during the COVID-19 pandemic can uncover potential issues with this online learning model.

Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, focusing on learners’ interaction characteristics before, during, and after the COVID-19 outbreak. We visually assessed changes in the scale of network interaction before, during, and after the outbreak along with the characteristics of interaction in Gephi. Examining students’ interactions in different courses revealed distinct interactive network characteristics, the pandemic’s impact on online courses, and relevant suggestions. Findings are expected to promote effective interaction and deep learning among students in addition to serving as a reference for the development of other online learning communities.

2. Literature review and research questions

Interaction is deemed as central to the educational experience and is a major focus of research on online learning. Moore began to study the problem of interaction in distance education as early as 1989. He defined three core types of interaction: student–teacher, student–content, and student–student [ 9 ]. Lear et al. [ 10 ] described an interactivity/ community-process model of distance education: they specifically discussed the relationships between interactivity, community awareness, and engaging learners and found interactivity and community awareness to be correlated with learner engagement. Zulfikar et al. [ 11 ] suggested that discussions initiated by the students encourage more students’ engagement than discussions initiated by the instructors. It is most important to afford learners opportunities to interact purposefully with teachers, and improving the quality of learner interaction is crucial to fostering profound learning [ 12 ]. Interaction is an important way for learners to communicate and share information, and a key factor in the quality of online learning [ 13 ].

Timely feedback is the main component of online learning interaction. Woo and Reeves discovered that students often become frustrated when they fail to receive prompt feedback [ 14 ]. Shelley et al. conducted a three-year study of graduate and undergraduate students’ satisfaction with online learning at universities and found that interaction with educators and students is the main factor affecting satisfaction [ 15 ]. Teachers therefore need to provide students with scoring justification, support, and constructive criticism during online learning. Some researchers examined online learning during the COVID-19 pandemic. They found that most students preferred face-to-face learning rather than online learning due to obstacles faced online, such as a lack of motivation, limited teacher-student interaction, and a sense of isolation when learning in different times and spaces [ 16 , 17 ]. However, it can be reduced by enhancing the online interaction between teachers and students [ 18 ].

Research showed that interactions contributed to maintaining students’ motivation to continue learning [ 19 ]. Baber argued that interaction played a key role in students’ academic performance and influenced the quality of the online learning experience [ 20 ]. Hodges et al. maintained that well-designed online instruction can lead to unique teaching experiences [ 21 ]. Banna et al. mentioned that using discussion boards, chat sessions, blogs, wikis, and other tools could promote student interaction and improve participation in online courses [ 22 ]. During the COVID-19 pandemic, Mahmood proposed a series of teaching strategies suitable for distance learning to improve its effectiveness [ 23 ]. Lapitan et al. devised an online strategy to ease the transition from traditional face-to-face instruction to online learning [ 24 ]. The preceding discussion suggests that online learning goes beyond simply providing learning resources; teachers should ideally design real-life activities to give learners more opportunities to participate.

As mentioned, COVID-19 has driven many scholars to explore the online learning environment. However, most have ignored the uniqueness of online learning during this time and have rarely compared pre- and post-pandemic online learning interaction. Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, centering on student interaction before and after the pandemic. Gephi was used to visually analyze changes in the scale and characteristics of network interaction. The following questions were of particular interest:

• (1) Can the COVID-19 pandemic promote the expansion of online learning?
• (2a) What are the characteristics of online learning interaction during the pandemic?
• (2b) What are the characteristics of online learning interaction after the pandemic?
• (3) How do interaction characteristics differ between social science courses and natural science courses?

3. Methodology

3.1 research context.

We selected several courses with a large number of participants and extensive online interaction among hundreds of courses on the icourse.163 MOOC platform. These courses had been offered on the platform for at least three semesters, covering three periods (i.e., before, during, and after the COVID-19 outbreak). To eliminate the effects of shifts in irrelevant variables (e.g., course teaching activities), we chose several courses with similar teaching activities and compared them on multiple dimensions. All course content was taught online. The teachers of each course posted discussion threads related to learning topics; students were expected to reply via comments. Learners could exchange ideas freely in their responses in addition to asking questions and sharing their learning experiences. Teachers could answer students’ questions as well. Conversations in the comment area could partly compensate for a relative absence of online classroom interaction. Teacher–student interaction is conducive to the formation of a social network structure and enabled us to examine teachers’ and students’ learning behavior through SNA. The comment areas in these courses were intended for learners to construct knowledge via reciprocal communication. Meanwhile, by answering students’ questions, teachers could encourage them to reflect on their learning progress. These courses’ successive terms also spanned several phases of COVID-19, allowing us to ascertain the pandemic’s impact on online learning.

3.2 Data collection and preprocessing

To avoid interference from invalid or unclear data, the following criteria were applied to select representative courses: (1) generality (i.e., public courses and professional courses were chosen from different schools across China); (2) time validity (i.e., courses were held before during, and after the pandemic); and (3) notability (i.e., each course had at least 2,000 participants). We ultimately chose 15 courses across the social sciences and natural sciences (see Table 1 ). The coding is used to represent the course name.

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https://doi.org/10.1371/journal.pone.0273016.t001

To discern courses’ evolution during the pandemic, we gathered data on three terms before, during, and after the COVID-19 outbreak in addition to obtaining data from two terms completed well before the pandemic and long after. Our final dataset comprised five sets of interactive data. Finally, we collected about 120,000 comments for SNA. Because each course had a different start time—in line with fluctuations in the number of confirmed COVID-19 cases in China and the opening dates of most colleges and universities—we divided our sample into five phases: well before the pandemic (Phase I); before the pandemic (Phase Ⅱ); during the pandemic (Phase Ⅲ); after the pandemic (Phase Ⅳ); and long after the pandemic (Phase Ⅴ). We sought to preserve consistent time spans to balance the amount of data in each period ( Fig 1 ).

https://doi.org/10.1371/journal.pone.0273016.g001

3.3 Instrumentation

Participants’ comments and “thumbs-up” behavior data were converted into a network structure and compared using social network analysis (SNA). Network analysis, according to M’Chirgui, is an effective tool for clarifying network relationships by employing sophisticated techniques [ 25 ]. Specifically, SNA can help explain the underlying relationships among team members and provide a better understanding of their internal processes. Yang and Tang used SNA to discuss the relationship between team structure and team performance [ 26 ]. Golbeck argued that SNA could improve the understanding of students’ learning processes and reveal learners’ and teachers’ role dynamics [ 27 ].

To analyze Question (1), the number of nodes and diameter in the generated network were deemed as indicators of changes in network size. Social networks are typically represented as graphs with nodes and degrees, and node count indicates the sample size [ 15 ]. Wellman et al. proposed that the larger the network scale, the greater the number of network members providing emotional support, goods, services, and companionship [ 28 ]. Jan’s study measured the network size by counting the nodes which represented students, lecturers, and tutors [ 29 ]. Similarly, network nodes in the present study indicated how many learners and teachers participated in the course, with more nodes indicating more participants. Furthermore, we investigated the network diameter, a structural feature of social networks, which is a common metric for measuring network size in SNA [ 30 ]. The network diameter refers to the longest path between any two nodes in the network. There has been evidence that a larger network diameter leads to greater spread of behavior [ 31 ]. Likewise, Gašević et al. found that larger networks were more likely to spread innovative ideas about educational technology when analyzing MOOC-related research citations [ 32 ]. Therefore, we employed node count and network diameter to measure the network’s spatial size and further explore the expansion characteristic of online courses. Brief introduction of these indicators can be summarized in Table 2 .

https://doi.org/10.1371/journal.pone.0273016.t002

To address Question (2), a list of interactive analysis metrics in SNA were introduced to scrutinize learners’ interaction characteristics in online learning during and after the pandemic, as shown below:

• (1) The average degree reflects the density of the network by calculating the average number of connections for each node. As Rong and Xu suggested, the average degree of a network indicates how active its participants are [ 33 ]. According to Hu, a higher average degree implies that more students are interacting directly with each other in a learning context [ 34 ]. The present study inherited the concept of the average degree from these previous studies: the higher the average degree, the more frequent the interaction between individuals in the network.
• (2) Essentially, a weighted average degree in a network is calculated by multiplying each degree by its respective weight, and then taking the average. Bydžovská took the strength of the relationship into account when determining the weighted average degree [ 35 ]. By calculating friendship’s weighted value, Maroulis assessed peer achievement within a small-school reform [ 36 ]. Accordingly, we considered the number of interactions as the weight of the degree, with a higher average degree indicating more active interaction among learners.
• (3) Network density is the ratio between actual connections and potential connections in a network. The more connections group members have with each other, the higher the network density. In SNA, network density is similar to group cohesion, i.e., a network of more strong relationships is more cohesive [ 37 ]. Network density also reflects how much all members are connected together [ 38 ]. Therefore, we adopted network density to indicate the closeness among network members. Higher network density indicates more frequent interaction and closer communication among students.
• (4) Clustering coefficient describes local network attributes and indicates that two nodes in the network could be connected through adjacent nodes. The clustering coefficient measures users’ tendency to gather (cluster) with others in the network: the higher the clustering coefficient, the more frequently users communicate with other group members. We regarded this indicator as a reflection of the cohesiveness of the group [ 39 ].
• (5) In a network, the average path length is the average number of steps along the shortest paths between any two nodes. Oliveres has observed that when an average path length is small, the route from one node to another is shorter when graphed [ 40 ]. This is especially true in educational settings where students tend to become closer friends. So we consider that the smaller the average path length, the greater the possibility of interaction between individuals in the network.
• (6) A network with a large number of nodes, but whose average path length is surprisingly small, is known as the small-world effect [ 41 ]. A higher clustering coefficient and shorter average path length are important indicators of a small-world network: a shorter average path length enables the network to spread information faster and more accurately; a higher clustering coefficient can promote frequent knowledge exchange within the group while boosting the timeliness and accuracy of knowledge dissemination [ 42 ]. Brief introduction of these indicators can be summarized in Table 3 .

https://doi.org/10.1371/journal.pone.0273016.t003

To analyze Question 3, we used the concept of closeness centrality, which determines how close a vertex is to others in the network. As Opsahl et al. explained, closeness centrality reveals how closely actors are coupled with their entire social network [ 43 ]. In order to analyze social network-based engineering education, Putnik et al. examined closeness centrality and found that it was significantly correlated with grades [ 38 ]. We used closeness centrality to measure the position of an individual in the network. Brief introduction of these indicators can be summarized in Table 4 .

https://doi.org/10.1371/journal.pone.0273016.t004

3.4 Ethics statement

This study was approved by the Academic Committee Office (ACO) of South China Normal University ( http://fzghb.scnu.edu.cn/ ), Guangzhou, China. Research data were collected from the open platform and analyzed anonymously. There are thus no privacy issues involved in this study.

4.1 COVID-19’s role in promoting the scale of online courses was not as important as expected

As shown in Fig 2 , the number of course participants and nodes are closely correlated with the pandemic’s trajectory. Because the number of participants in each course varied widely, we normalized the number of participants and nodes to more conveniently visualize course trends. Fig 2 depicts changes in the chosen courses’ number of participants and nodes before the pandemic (Phase II), during the pandemic (Phase III), and after the pandemic (Phase IV). The number of participants in most courses during the pandemic exceeded those before and after the pandemic. But the number of people who participate in interaction in some courses did not increase.

https://doi.org/10.1371/journal.pone.0273016.g002

In order to better analyze the trend of interaction scale in online courses before, during, and after the pandemic, the selected courses were categorized according to their scale change. When the number of participants increased (decreased) beyond 20% (statistical experience) and the diameter also increased (decreased), the course scale was determined to have increased (decreased); otherwise, no significant change was identified in the course’s interaction scale. Courses were subsequently divided into three categories: increased interaction scale, decreased interaction scale, and no significant change. Results appear in Table 5 .

https://doi.org/10.1371/journal.pone.0273016.t005

From before the pandemic until it broke out, the interaction scale of five courses increased, accounting for 33.3% of the full sample; one course’s interaction scale declined, accounting for 6.7%. The interaction scale of nine courses decreased, accounting for 60%. The pandemic’s role in promoting online courses thus was not as important as anticipated, and most courses’ interaction scale did not change significantly throughout.

No courses displayed growing interaction scale after the pandemic: the interaction scale of nine courses fell, accounting for 60%; and the interaction scale of six courses did not shift significantly, accounting for 40%. Courses with an increased scale of interaction during the pandemic did not maintain an upward trend. On the contrary, the improvement in the pandemic caused learners’ enthusiasm for online learning to wane. We next analyzed several interaction metrics to further explore course interaction during different pandemic periods.

4.2 Characteristics of online learning interaction amid COVID-19

4.2.1 during the covid-19 pandemic, online learning interaction in some courses became more active..

Changes in course indicators with the growing interaction scale during the pandemic are presented in Fig 3 , including SS5, SS6, NS1, NS3, and NS8. The horizontal ordinate indicates the number of courses, with red color representing the rise of the indicator value on the vertical ordinate and blue representing the decline.

https://doi.org/10.1371/journal.pone.0273016.g003

Specifically: (1) The average degree and weighted average degree of the five course networks demonstrated an upward trend. The emergence of the pandemic promoted students’ enthusiasm; learners were more active in the interactive network. (2) Fig 3 shows that 3 courses had increased network density and 2 courses had decreased. The higher the network density, the more communication within the team. Even though the pandemic accelerated the interaction scale and frequency, the tightness between learners in some courses did not improve. (3) The clustering coefficient of social science courses rose whereas the clustering coefficient and small-world property of natural science courses fell. The higher the clustering coefficient and the small-world property, the better the relationship between adjacent nodes and the higher the cohesion [ 39 ]. (4) Most courses’ average path length increased as the interaction scale increased. However, when the average path length grew, adverse effects could manifest: communication between learners might be limited to a small group without multi-directional interaction.

When the pandemic emerged, the only declining network scale belonged to a natural science course (NS2). The change in each course index is pictured in Fig 4 . The abscissa indicates the size of the value, with larger values to the right. The red dot indicates the index value before the pandemic; the blue dot indicates its value during the pandemic. If the blue dot is to the right of the red dot, then the value of the index increased; otherwise, the index value declined. Only the weighted average degree of the course network increased. The average degree, network density decreased, indicating that network members were not active and that learners’ interaction degree and communication frequency lessened. Despite reduced learner interaction, the average path length was small and the connectivity between learners was adequate.

https://doi.org/10.1371/journal.pone.0273016.g004

4.2.2 After the COVID-19 pandemic, the scale decreased rapidly, but most course interaction was more effective.

Fig 5 shows the changes in various courses’ interaction indicators after the pandemic, including SS1, SS2, SS3, SS6, SS7, NS2, NS3, NS7, and NS8.

https://doi.org/10.1371/journal.pone.0273016.g005

Specifically: (1) The average degree and weighted average degree of most course networks decreased. The scope and intensity of interaction among network members declined rapidly, as did learners’ enthusiasm for communication. (2) The network density of seven courses also fell, indicating weaker connections between learners in most courses. (3) In addition, the clustering coefficient and small-world property of most course networks decreased, suggesting little possibility of small groups in the network. The scope of interaction between learners was not limited to a specific space, and the interaction objects had no significant tendencies. (4) Although the scale of course interaction became smaller in this phase, the average path length of members’ social networks shortened in nine courses. Its shorter average path length would expedite the spread of information within the network as well as communication and sharing among network members.

Fig 6 displays the evolution of course interaction indicators without significant changes in interaction scale after the pandemic, including SS4, SS5, NS1, NS4, NS5, and NS6.

https://doi.org/10.1371/journal.pone.0273016.g006

Specifically: (1) Some course members’ social networks exhibited an increase in the average and weighted average. In these cases, even though the course network’s scale did not continue to increase, communication among network members rose and interaction became more frequent and deeper than before. (2) Network density and average path length are indicators of social network density. The greater the network density, the denser the social network; the shorter the average path length, the more concentrated the communication among network members. However, at this phase, the average path length and network density in most courses had increased. Yet the network density remained small despite having risen ( Table 6 ). Even with more frequent learner interaction, connections remained distant and the social network was comparatively sparse.

https://doi.org/10.1371/journal.pone.0273016.t006

In summary, the scale of interaction did not change significantly overall. Nonetheless, some course members’ frequency and extent of interaction increased, and the relationships between network members became closer as well. In the study, we found it interesting that the interaction scale of Economics (a social science course) course and Electrodynamics (a natural science course) course expanded rapidly during the pandemic and retained their interaction scale thereafter. We next assessed these two courses to determine whether their level of interaction persisted after the pandemic.

4.3 Analyses of natural science courses and social science courses

4.3.1 analyses of the interaction characteristics of economics and electrodynamics..

Economics and Electrodynamics are social science courses and natural science courses, respectively. Members’ interaction within these courses was similar: the interaction scale increased significantly when COVID-19 broke out (Phase Ⅲ), and no significant changes emerged after the pandemic (Phase Ⅴ). We hence focused on course interaction long after the outbreak (Phase V) and compared changes across multiple indicators, as listed in Table 7 .

https://doi.org/10.1371/journal.pone.0273016.t007

As the pandemic continued to improve, the number of participants and the diameter long after the outbreak (Phase V) each declined for Economics compared with after the pandemic (Phase IV). The interaction scale decreased, but the interaction between learners was much deeper. Specifically: (1) The weighted average degree, network density, clustering coefficient, and small-world property each reflected upward trends. The pandemic therefore exerted a strong impact on this course. Interaction was well maintained even after the pandemic. The smaller network scale promoted members’ interaction and communication. (2) Compared with after the pandemic (Phase IV), members’ network density increased significantly, showing that relationships between learners were closer and that cohesion was improving. (3) At the same time, as the clustering coefficient and small-world property grew, network members demonstrated strong small-group characteristics: the communication between them was deepening and their enthusiasm for interaction was higher. (4) Long after the COVID-19 outbreak (Phase V), the average path length was reduced compared with previous terms, knowledge flowed more quickly among network members, and the degree of interaction gradually deepened.

The average degree, weighted average degree, network density, clustering coefficient, and small-world property of Electrodynamics all decreased long after the COVID-19 outbreak (Phase V) and were lower than during the outbreak (Phase Ⅲ). The level of learner interaction therefore gradually declined long after the outbreak (Phase V), and connections between learners were no longer active. Although the pandemic increased course members’ extent of interaction, this rise was merely temporary: students’ enthusiasm for learning waned rapidly and their interaction decreased after the pandemic (Phase IV). To further analyze the interaction characteristics of course members in Economics and Electrodynamics, we evaluated the closeness centrality of their social networks, as shown in section 4.3.2.

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

The change in the closeness centrality of social networks in Economics was small, and no sharp upward trend appeared during the pandemic outbreak, as shown in Fig 7 . The emergence of COVID-19 apparently fostered learners’ interaction in Economics albeit without a significant impact. The closeness centrality changed in Electrodynamics varied from that of Economics: upon the COVID-19 outbreak, closeness centrality was significantly different from other semesters. Communication between learners was closer and interaction was more effective. Electrodynamics course members’ social network proximity decreased rapidly after the pandemic. Learners’ communication lessened. In general, Economics course showed better interaction before the outbreak and was less affected by the pandemic; Electrodynamics course was more affected by the pandemic and showed different interaction characteristics at different periods of the pandemic.

(Note: "****" indicates the significant distinction in closeness centrality between the two periods, otherwise no significant distinction).

https://doi.org/10.1371/journal.pone.0273016.g007

5. Discussion

We referred to discussion forums from several courses on the icourse.163 MOOC platform to compare online learning before, during, and after the COVID-19 pandemic via SNA and to delineate the pandemic’s effects on online courses. Only 33.3% of courses in our sample increased in terms of interaction during the pandemic; the scale of interaction did not rise in any courses thereafter. When the courses scale rose, the scope and frequency of interaction showed upward trends during the pandemic; and the clustering coefficient of natural science courses and social science courses differed: the coefficient for social science courses tended to rise whereas that for natural science courses generally declined. When the pandemic broke out, the interaction scale of a single natural science course decreased along with its interaction scope and frequency. The amount of interaction in most courses shrank rapidly during the pandemic and network members were not as active as they had been before. However, after the pandemic, some courses saw declining interaction but greater communication between members; interaction also became more frequent and deeper than before.

5.1 During the COVID-19 pandemic, the scale of interaction increased in only a few courses

The pandemic outbreak led to a rapid increase in the number of participants in most courses; however, the change in network scale was not significant. The scale of online interaction expanded swiftly in only a few courses; in others, the scale either did not change significantly or displayed a downward trend. After the pandemic, the interaction scale in most courses decreased quickly; the same pattern applied to communication between network members. Learners’ enthusiasm for online interaction reduced as the circumstances of the pandemic improved—potentially because, during the pandemic, China’s Ministry of Education declared “School’s Out, But Class’s On” policy. Major colleges and universities were encouraged to use the Internet and informational resources to provide learning support, hence the sudden increase in the number of participants and interaction in online courses [ 46 ]. After the pandemic, students’ enthusiasm for online learning gradually weakened, presumably due to easing of the pandemic [ 47 ]. More activities also transitioned from online to offline, which tempered learners’ online discussion. Research has shown that long-term online learning can even bore students [ 48 ].

Most courses’ interaction scale decreased significantly after the pandemic. First, teachers and students occupied separate spaces during the outbreak, had few opportunities for mutual cooperation and friendship, and lacked a sense of belonging [ 49 ]. Students’ enthusiasm for learning dissipated over time [ 50 ]. Second, some teachers were especially concerned about adapting in-person instructional materials for digital platforms; their pedagogical methods were ineffective, and they did not provide learning activities germane to student interaction [ 51 ]. Third, although teachers and students in remote areas were actively engaged in online learning, some students could not continue to participate in distance learning due to inadequate technology later in the outbreak [ 52 ].

5.2 Characteristics of online learning interaction during and after the COVID-19 pandemic

5.2.1 during the covid-19 pandemic, online interaction in most courses did not change significantly..

The interaction scale of only a few courses increased during the pandemic. The interaction scope and frequency of these courses climbed as well. Yet even as the degree of network interaction rose, course network density did not expand in all cases. The pandemic sparked a surge in the number of online learners and a rapid increase in network scale, but students found it difficult to interact with all learners. Yau pointed out that a greater network scale did not enrich the range of interaction between individuals; rather, the number of individuals who could interact directly was limited [ 53 ]. The internet facilitates interpersonal communication. However, not everyone has the time or ability to establish close ties with others [ 54 ].

In addition, social science courses and natural science courses in our sample revealed disparate trends in this regard: the clustering coefficient of social science courses increased and that of natural science courses decreased. Social science courses usually employ learning approaches distinct from those in natural science courses [ 55 ]. Social science courses emphasize critical and innovative thinking along with personal expression [ 56 ]. Natural science courses focus on practical skills, methods, and principles [ 57 ]. Therefore, the content of social science courses can spur large-scale discussion among learners. Some course evaluations indicated that the course content design was suboptimal as well: teachers paid close attention to knowledge transmission and much less to piquing students’ interest in learning. In addition, the thread topics that teachers posted were scarcely diversified and teachers’ questions lacked openness. These attributes could not spark active discussion among learners.

5.2.2 Online learning interaction declined after the COVID-19 pandemic.

Most courses’ interaction scale and intensity decreased rapidly after the pandemic, but some did not change. Courses with a larger network scale did not continue to expand after the outbreak, and students’ enthusiasm for learning paled. The pandemic’s reduced severity also influenced the number of participants in online courses. Meanwhile, restored school order moved many learning activities from virtual to in-person spaces. Face-to-face learning has gradually replaced online learning, resulting in lower enrollment and less interaction in online courses. Prolonged online courses could have also led students to feel lonely and to lack a sense of belonging [ 58 ].

The scale of interaction in some courses did not change substantially after the pandemic yet learners’ connections became tighter. We hence recommend that teachers seize pandemic-related opportunities to design suitable activities. Additionally, instructors should promote student-teacher and student-student interaction, encourage students to actively participate online, and generally intensify the impact of online learning.

5.3 What are the characteristics of interaction in social science courses and natural science courses?

The level of interaction in Economics (a social science course) was significantly higher than that in Electrodynamics (a natural science course), and the small-world property in Economics increased as well. To boost online courses’ learning-related impacts, teachers can divide groups of learners based on the clustering coefficient and the average path length. Small groups of students may benefit teachers in several ways: to participate actively in activities intended to expand students’ knowledge, and to serve as key actors in these small groups. Cultivating students’ keenness to participate in class activities and self-management can also help teachers guide learner interaction and foster deep knowledge construction.

As evidenced by comments posted in the Electrodynamics course, we observed less interaction between students. Teachers also rarely urged students to contribute to conversations. These trends may have arisen because teachers and students were in different spaces. Teachers might have struggled to discern students’ interaction status. Teachers could also have failed to intervene in time, to design online learning activities that piqued learners’ interest, and to employ sound interactive theme planning and guidance. Teachers are often active in traditional classroom settings. Their roles are comparatively weakened online, such that they possess less control over instruction [ 59 ]. Online instruction also requires a stronger hand in learning: teachers should play a leading role in regulating network members’ interactive communication [ 60 ]. Teachers can guide learners to participate, help learners establish social networks, and heighten students’ interest in learning [ 61 ]. Teachers should attend to core members in online learning while also considering edge members; by doing so, all network members can be driven to share their knowledge and become more engaged. Finally, teachers and assistant teachers should help learners develop knowledge, exchange topic-related ideas, pose relevant questions during course discussions, and craft activities that enable learners to interact online [ 62 ]. These tactics can improve the effectiveness of online learning.

As described, network members displayed distinct interaction behavior in Economics and Electrodynamics courses. First, these courses varied in their difficulty: the social science course seemed easier to understand and focused on divergent thinking. Learners were often willing to express their views in comments and to ponder others’ perspectives [ 63 ]. The natural science course seemed more demanding and was oriented around logical thinking and skills [ 64 ]. Second, courses’ content differed. In general, social science courses favor the acquisition of declarative knowledge and creative knowledge compared with natural science courses. Social science courses also entertain open questions [ 65 ]. Natural science courses revolve around principle knowledge, strategic knowledge, and transfer knowledge [ 66 ]. Problems in these courses are normally more complicated than those in social science courses. Third, the indicators affecting students’ attitudes toward learning were unique. Guo et al. discovered that “teacher feedback” most strongly influenced students’ attitudes towards learning social science courses but had less impact on students in natural science courses [ 67 ]. Therefore, learners in social science courses likely expect more feedback from teachers and greater interaction with others.

6. Conclusion and future work

Our findings show that the network interaction scale of some online courses expanded during the COVID-19 pandemic. The network scale of most courses did not change significantly, demonstrating that the pandemic did not notably alter the scale of course interaction. Online learning interaction among course network members whose interaction scale increased also became more frequent during the pandemic. Once the outbreak was under control, although the scale of interaction declined, the level and scope of some courses’ interactive networks continued to rise; interaction was thus particularly effective in these cases. Overall, the pandemic appeared to have a relatively positive impact on online learning interaction. We considered a pair of courses in detail and found that Economics (a social science course) fared much better than Electrodynamics (a natural science course) in classroom interaction; learners were more willing to partake in-class activities, perhaps due to these courses’ unique characteristics. Brint et al. also came to similar conclusions [ 57 ].

This study was intended to be rigorous. Even so, several constraints can be addressed in future work. The first limitation involves our sample: we focused on a select set of courses hosted on China’s icourse.163 MOOC platform. Future studies should involve an expansive collection of courses to provide a more holistic understanding of how the pandemic has influenced online interaction. Second, we only explored the interactive relationship between learners and did not analyze interactive content. More in-depth content analysis should be carried out in subsequent research. All in all, the emergence of COVID-19 has provided a new path for online learning and has reshaped the distance learning landscape. To cope with associated challenges, educational practitioners will need to continue innovating in online instructional design, strengthen related pedagogy, optimize online learning conditions, and bolster teachers’ and students’ competence in online learning.

• View Article
• Google Scholar
• PubMed/NCBI
• 30. Serrat O. Social network analysis. Knowledge solutions: Springer; 2017. p. 39–43. https://doi.org/10.1007/978-981-10-0983-9_9
• 33. Rong Y, Xu E, editors. Strategies for the Management of the Government Affairs Microblogs in China Based on the SNA of Fifty Government Affairs Microblogs in Beijing. 14th International Conference on Service Systems and Service Management 2017.
• 34. Hu X, Chu S, editors. A comparison on using social media in a professional experience course. International Conference on Social Media and Society; 2013.
• 35. Bydžovská H. A Comparative Analysis of Techniques for Predicting Student Performance. Proceedings of the 9th International Conference on Educational Data Mining; Raleigh, NC, USA: International Educational Data Mining Society2016. p. 306–311.
• 40. Olivares D, Adesope O, Hundhausen C, et al., editors. Using social network analysis to measure the effect of learning analytics in computing education. 19th IEEE International Conference on Advanced Learning Technologies 2019.
• 41. Travers J, Milgram S. An experimental study of the small world problem. Social Networks: Elsevier; 1977. p. 179–197. https://doi.org/10.1016/B978-0-12-442450-0.50018–3
• 43. Okamoto K, Chen W, Li X-Y, editors. Ranking of closeness centrality for large-scale social networks. International workshop on frontiers in algorithmics; 2008; Springer, Berlin, Heidelberg: Springer.
• 47. Ding Y, Yang X, Zheng Y, editors. COVID-19’s Effects on the Scope, Effectiveness, and Roles of Teachers in Online Learning Based on Social Network Analysis: A Case Study. International Conference on Blended Learning; 2021: Springer.
• 64. Boys C, Brennan J., Henkel M., Kirkland J., Kogan M., Youl P. Higher Education and Preparation for Work. Jessica Kingsley Publishers. 1988. https://doi.org/10.1080/03075079612331381467

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

• Published: 28 May 2021
• Volume 26 , pages 7321–7338, ( 2021 )

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• Jessie S. Barrot   ORCID: orcid.org/0000-0001-8517-4058 1 ,
• Ian I. Llenares 1 &
• Leo S. del Rosario 1  

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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.

Adarkwah, M. A. (2021). “I’m not against online teaching, but what about us?”: ICT in Ghana post Covid-19. Education and Information Technologies, 26 (2), 1665–1685.

Article   Google Scholar  

Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25 , 5261–5280.

Araujo, T., Wonneberger, A., Neijens, P., & de Vreese, C. (2017). How much time do you spend online? Understanding and improving the accuracy of self-reported measures of Internet use. Communication Methods and Measures, 11 (3), 173–190.

Barrot, J. S. (2016). Using Facebook-based e-portfolio in ESL writing classrooms: Impact and challenges. Language, Culture and Curriculum, 29 (3), 286–301.

Barrot, J. S. (2018). Facebook as a learning environment for language teaching and learning: A critical analysis of the literature from 2010 to 2017. Journal of Computer Assisted Learning, 34 (6), 863–875.

Barrot, J. S. (2020). Scientific mapping of social media in education: A decade of exponential growth. Journal of Educational Computing Research .  https://doi.org/10.1177/0735633120972010 .

Barrot, J. S. (2021). Social media as a language learning environment: A systematic review of the literature (2008–2019). Computer Assisted Language Learning . https://doi.org/10.1080/09588221.2021.1883673 .

Bergen, N., & Labonté, R. (2020). “Everything is perfect, and we have no problems”: Detecting and limiting social desirability bias in qualitative research. Qualitative Health Research, 30 (5), 783–792.

Birks, M., & Mills, J. (2011). Grounded theory: A practical guide . Sage.

Boelens, R., De Wever, B., & Voet, M. (2017). Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review, 22 , 1–18.

Buehler, M. A. (2004). Where is the library in course management software? Journal of Library Administration, 41 (1–2), 75–84.

Carter, R. A., Jr., Rice, M., Yang, S., & Jackson, H. A. (2020). Self-regulated learning in online learning environments: Strategies for remote learning. Information and Learning Sciences, 121 (5/6), 321–329.

Cavanaugh, C. S., Barbour, M. K., & Clark, T. (2009). Research and practice in K-12 online learning: A review of open access literature. The International Review of Research in Open and Distributed Learning, 10 (1), 1–22.

Cicchetti, D., Bronen, R., Spencer, S., Haut, S., Berg, A., Oliver, P., & Tyrer, P. (2006). Rating scales, scales of measurement, issues of reliability: Resolving some critical issues for clinicians and researchers. The Journal of Nervous and Mental Disease, 194 (8), 557–564.

Copeland, W. E., McGinnis, E., Bai, Y., Adams, Z., Nardone, H., Devadanam, V., & Hudziak, J. J. (2021). Impact of COVID-19 pandemic on college student mental health and wellness. Journal of the American Academy of Child & Adolescent Psychiatry, 60 (1), 134–141.

Day, T., Chang, I. C. C., Chung, C. K. L., Doolittle, W. E., Housel, J., & McDaniel, P. N. (2021). The immediate impact of COVID-19 on postsecondary teaching and learning. The Professional Geographer, 73 (1), 1–13.

Donitsa-Schmidt, S., & Ramot, R. (2020). Opportunities and challenges: Teacher education in Israel in the Covid-19 pandemic. Journal of Education for Teaching, 46 (4), 586–595.

Drane, C., Vernon, L., & O’Shea, S. (2020). The impact of ‘learning at home’on the educational outcomes of vulnerable children in Australia during the COVID-19 pandemic. Literature Review Prepared by the National Centre for Student Equity in Higher Education. Curtin University, Australia.

Elaish, M., Shuib, L., Ghani, N., & Yadegaridehkordi, E. (2019). Mobile English language learning (MELL): A literature review. Educational Review, 71 (2), 257–276.

Fawaz, M., Al Nakhal, M., & Itani, M. (2021). COVID-19 quarantine stressors and management among Lebanese students: A qualitative study.  Current Psychology , 1–8.

Franchi, T. (2020). The impact of the Covid-19 pandemic on current anatomy education and future careers: A student’s perspective. Anatomical Sciences Education, 13 (3), 312–315.

Garcia, R., Falkner, K., & Vivian, R. (2018). Systematic literature review: Self-regulated learning strategies using e-learning tools for computer science. Computers & Education, 123 , 150–163.

Gonzalez, T., De La Rubia, M. A., Hincz, K. P., Comas-Lopez, M., Subirats, L., Fort, S., & Sacha, G. M. (2020). Influence of COVID-19 confinement on students’ performance in higher education. PLoS One, 15 (10), e0239490.

Hew, K. F., Jia, C., Gonda, D. E., & Bai, S. (2020). Transitioning to the “new normal” of learning in unpredictable times: Pedagogical practices and learning performance in fully online flipped classrooms. International Journal of Educational Technology in Higher Education, 17 (1), 1–22.

Huang, Q. (2019). Comparing teacher’s roles of F2F learning and online learning in a blended English course. Computer Assisted Language Learning, 32 (3), 190–209.

John Hopkins University. (2021). Global map . https://coronavirus.jhu.edu/

Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., & Chouhan, P. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal. India . Children and Youth Services Review, 116 , 105194.

Kebritchi, M., Lipschuetz, A., & Santiague, L. (2017). Issues and challenges for teaching successful online courses in higher education: A literature review. Journal of Educational Technology Systems, 46 (1), 4–29.

Khalil, R., Mansour, A. E., Fadda, W. A., Almisnid, K., Aldamegh, M., Al-Nafeesah, A., & Al-Wutayd, O. (2020). The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: A qualitative study exploring medical students’ perspectives. BMC Medical Education, 20 (1), 1–10.

Matsumoto, K. (1994). Introspection, verbal reports and second language learning strategy research. Canadian Modern Language Review, 50 (2), 363–386.

Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59 (3), 1065–1078.

Pham, T., & Nguyen, H. (2020). COVID-19: Challenges and opportunities for Vietnamese higher education. Higher Education in Southeast Asia and beyond, 8 , 22–24.

Google Scholar  

Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144 , 103701.

Recker, M. M., Dorward, J., & Nelson, L. M. (2004). Discovery and use of online learning resources: Case study findings. Educational Technology & Society, 7 (2), 93–104.

Roblek, V., Mesko, M., Dimovski, V., & Peterlin, J. (2019). Smart technologies as social innovation and complex social issues of the Z generation. Kybernetes, 48 (1), 91–107.

Seplaki, C. L., Agree, E. M., Weiss, C. O., Szanton, S. L., Bandeen-Roche, K., & Fried, L. P. (2014). Assistive devices in context: Cross-sectional association between challenges in the home environment and use of assistive devices for mobility. The Gerontologist, 54 (4), 651–660.

Simbulan, N. (2020). COVID-19 and its impact on higher education in the Philippines. Higher Education in Southeast Asia and beyond, 8 , 15–18.

Singh, K., Srivastav, S., Bhardwaj, A., Dixit, A., & Misra, S. (2020). Medical education during the COVID-19 pandemic: a single institution experience. Indian Pediatrics, 57 (7), 678–679.

Singh, V., & Thurman, A. (2019). How many ways can we define online learning? A systematic literature review of definitions of online learning (1988–2018). American Journal of Distance Education, 33 (4), 289–306.

Spector, P. (1994). Using self-report questionnaires in OB research: A comment on the use of a controversial method. Journal of Organizational Behavior, 15 (5), 385–392.

Suryaman, M., Cahyono, Y., Muliansyah, D., Bustani, O., Suryani, P., Fahlevi, M., & Munthe, A. P. (2020). COVID-19 pandemic and home online learning system: Does it affect the quality of pharmacy school learning? Systematic Reviews in Pharmacy, 11 , 524–530.

Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw, S. M., & Liu, X. (2006). Teaching courses online: A review of the research. Review of Educational Research, 76 (1), 93–135.

Tang, T., Abuhmaid, A. M., Olaimat, M., Oudat, D. M., Aldhaeebi, M., & Bamanger, E. (2020). Efficiency of flipped classroom with online-based teaching under COVID-19.  Interactive Learning Environments , 1–12.

Usher, M., & Barak, M. (2020). Team diversity as a predictor of innovation in team projects of face-to-face and online learners. Computers & Education, 144 , 103702.

Varea, V., & González-Calvo, G. (2020). Touchless classes and absent bodies: Teaching physical education in times of Covid-19.  Sport, Education and Society , 1–15.

Wallace, R. M. (2003). Online learning in higher education: A review of research on interactions among teachers and students. Education, Communication & Information, 3 (2), 241–280.

World Health Organization (2020). Coronavirus . https://www.who.int/health-topics/coronavirus#tab=tab_1

Xue, E., Li, J., Li, T., & Shang, W. (2020). China’s education response to COVID-19: A perspective of policy analysis.  Educational Philosophy and Theory , 1–13.

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Online education in the post-COVID era

• Barbara B. Lockee 1  

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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.

Mackey, J., Gilmore, F., Dabner, N., Breeze, D. & Buckley, P. J. Online Learn. Teach. 8 , 35–48 (2012).

Google Scholar  

Sands, T. & Shushok, F. The COVID-19 higher education shove. Educause Review https://go.nature.com/3o2vHbX (16 October 2020).

Hodges, C., Moore, S., Lockee, B., Trust, T. & Bond, M. A. The difference between emergency remote teaching and online learning. Educause Review https://go.nature.com/38084Lh (27 March 2020).

Beatty, B. J. (ed.) Hybrid-Flexible Course Design Ch. 1.4 https://go.nature.com/3o6Sjb2 (EdTech Books, 2019).

Skinner, B. F. Science 128 , 969–977 (1958).

Article   Google Scholar  

Keller, F. S. J. Appl. Behav. Anal. 1 , 79–89 (1968).

Darling-Hammond, L. et al. Restarting and Reinventing School: Learning in the Time of COVID and Beyond (Learning Policy Institute, 2020).

Fulton, C. Information Learn. Sci . 121 , 579–585 (2020).

Pennisi, E. Science 369 , 239–240 (2020).

Silva, E. & White, T. Change The Magazine Higher Learn. 47 , 68–72 (2015).

McIsaac, M. S. & Gunawardena, C. N. in Handbook of Research for Educational Communications and Technology (ed. Jonassen, D. H.) Ch. 13 (Simon & Schuster Macmillan, 1996).

Irvine, V. The landscape of merging modalities. Educause Review https://go.nature.com/2MjiBc9 (26 October 2020).

Stein, J. & Graham, C. Essentials for Blended Learning Ch. 1 (Routledge, 2020).

Maloy, R. W., Trust, T. & Edwards, S. A. Variety is the spice of remote learning. Medium https://go.nature.com/34Y1NxI (24 August 2020).

Lockee, B. J. Appl. Instructional Des . https://go.nature.com/3b0ddoC (2020).

Dunlap, J. & Lowenthal, P. Open Praxis 10 , 79–89 (2018).

Johnson, N., Veletsianos, G. & Seaman, J. Online Learn. 24 , 6–21 (2020).

Vaughan, N. D., Cleveland-Innes, M. & Garrison, D. R. Assessment in Teaching in Blended Learning Environments: Creating and Sustaining Communities of Inquiry (Athabasca Univ. Press, 2013).

Conrad, D. & Openo, J. Assessment Strategies for Online Learning: Engagement and Authenticity (Athabasca Univ. Press, 2018).

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Original research article, impact of the covid-19 pandemic on online learning in higher education: a bibliometric analysis.

• 1 Faculty of Public Administration, University of Ljubljana, Ljubljana, Slovenia
• 2 Department of Primary Level Education, University of the Aegean, Rhodes, Greece

The outbreak of the COVID-19 pandemic significantly disrupted higher education by forcing the transition to online learning, which became a mandatory teaching process during the lockdowns. Although the epidemiological situation has gradually improved since then, online learning is becoming ever more popular as it provides new learning opportunities. Therefore, the paper aims to present recent research trends concerning online learning in higher education during the COVID-19 pandemic by using selected bibliometric approaches. The bibliometric analysis is based on 8,303 documents from the Scopus database published between January 2020 and March 2022, when repeated lockdowns meant most countries were experiencing constant disruptions to the educational process. The results show that the COVID-19 pandemic increased interest in online learning research, notably in English-speaking and Asian countries, with most research being published in open-access scientific journals. Moreover, the topics most frequently discussed in the online learning research during the COVID-19 pandemic were ICT and pedagogy, technology-enhanced education, mental health and well-being, student experience and curriculum and professional development. Finally, the COVID-19 pandemic encouraged explorations of emergency remote learning approaches like e-learning, distance learning and virtual learning, which are intended to limit physical contact between teachers and students, where the specific requirements of a given field of study often guide which online learning approach is the most suitable. The findings add to the existing body of scientific knowledge and support the evidence-based policymaking needed to ensure sustainable higher education in the future.

1. Introduction

The outbreak of the COVID-19 pandemic significantly disrupted higher education by forcing the transition to online learning, which became a mandatory teaching process during the lockdowns ( Aristovnik et al., 2020a ). Despite the educational process saw disruptions on all levels of education, i.e., primary, secondary and tertiary ( Tang, 2023 ), as well as in adult education ( James and Thériault, 2020 ), worker education ( Dedeilia et al., 2023 ) and lifelong education ( Waller et al., 2020 ), higher education students proved to be one of the worst affected groups because the social distancing measures, on top of their education, challenged their financial and housing situation ( Aristovnik et al., 2020a ). Challenges arising from the density of students in educational facilities (e.g., campuses, faculties, dormitories etc.) meant higher education institutions were forced to offer education relying on various information and communication technologies (ICTs) and tried to ensure education comparable in quality to traditional learning, noting that the quality of online learning delivery holds important implications for student satisfaction and student performance ( Keržič et al., 2021 ). Nevertheless, the lockdown periods were devastating for many students also in terms of their emotional functioning ( Raccanello et al., 2022 ). The COVID-19 pandemic eventually grew more predictable and manageable, allowing higher education institutions to gradually shift back to traditional learning approaches. Although the epidemiological situation has improved over time, online learning is becoming increasingly popular as it provides new learning opportunities, especially when combined with traditional learning.

The rapid, yet from the health protection point of view necessary ( Aristovnik et al., 2020b ), shift from traditional learning to online learning considerably affected teaching and learning. The transition to online learning was made without adequate consideration of whether the study materials and teaching methods were suitable for this mode of higher education delivery. This was an ad hoc shift in a situation of great uncertainty for both teachers and students. The transition to online learning has also brought to the surface gaps in higher education providers’ preparedness and their lack of ICT infrastructure, resulting in unequal access to quality education for all, particularly students from rural areas and regions with lower socio-economic development. It is important to note here that the rapid shift to an online learning environment in emergency circumstances should not be confused with properly planned online education equipped with appropriate infrastructure that enables and supports pedagogical work and study in an online environment ( Hodges et al., 2020 ; Fuchs, 2022 ; Misiejuk et al., 2023 ). Apart from the changes in teaching and learning, the social aspect of students’ lives has been affected as well. The most worrying consequence has been social isolation leading to a lack of crucial social interaction for students ( Elmer et al., 2020 ; Bonsaksen et al., 2021 ; Fried et al., 2021 ; Van der Graaf et al., 2021 ) and in some cases also in coronavirus-related post-traumatic stress syndrome (PTSD) ( Ochnik et al., 2021 ). According to Gavriluţă et al. (2022) , three dimensions affected students during the COVID-19 pandemic: educational, social, and emotional. The transition from traditional to online learning entailed a significant transformation in education, requiring changes in teaching practices and new learning approaches. Further, the social aspect of the COVID-19 pandemic and associated lockdowns is evident in the absence of relational, economic and professional problems (in)directly affecting the transition to adulthood. The new reality changed attitudes to various aspects of life and, in turn, also affected emotional responsiveness. Briefly, substantial changes to everyday student lives were made during the COVID-19 pandemic that may hold far-reaching effects of currently unknown scope in the near and distant future ( Campos et al., 2022 ; Gao et al., 2022 ; Keržič et al., 2022 ; Rasli et al., 2022 ).

Therefore, the educational community requires greater insights into different aspects of the COVID-19 pandemic’s impact on online learning, e.g., students, teachers, pedagogy, ICT technology, online learning approaches and implications for various fields of study. In the context of higher education, some bibliometric studies (e.g., Gurcan et al., 2022 ; Saqr et al., 2023 ) have already sought to address issues involving online learning during the pandemic. Yet, they relied on a limited and narrow bibliographic dataset of peer-reviewed literature or lacked a qualitative synthesis of the results beyond the metrics, thereby neglecting some general comprehensive outlines of the global research into the topic ( Saqr et al., 2023 ). Moreover, despite some bibliometric studies focusing on technical aspects (e.g., Navarro-Espinosa et al., 2021 ; Bozkurt, 2022 ; Tlili et al., 2022 ), the identification of the most effective ICT tools for specific online learning approaches remains unclear. Finally, there are also some bibliometric studies that attempt to determine the effectiveness of online learning in providing higher education ( Brika et al., 2021 ; Baber et al., 2022 ; Bilal et al., 2022 ; Bozkurt, 2022 ; Fauzi, 2022 ; Küçük-Avci et al., 2022 ; Yan et al., 2022 ), however, they often overlook the specific requirements of individual fields of study, thereby neglecting the crucial aspect of tailoring online learning provision to different disciplines.

The bibliometric study presented in the paper accordingly aims to fill the presented gaps in the literature. Specifically, it aims to present a global overview of the recent research trends in online learning in higher education using a comprehensive dataset of literature encompassing different varieties of online learning approaches that can facilitate online learning during the COVID-19 pandemic, provide some relevant qualitative synthesis of the results beyond the metrics and examine the relationships between ICT tools, online learning approaches and fields of study. Thus, the present bibliometric study, focusing on higher education, tries to answer the following three research questions:

• RQ1: What is the current state of the online learning research by conducting a descriptive overview and identifying top-cited documents?

• RQ2: What is the scientific production of online learning research across countries and sources?

• RQ3: Which are the main research hotspots and concepts in online learning research?

The remainder of the paper is structured as follows. The next section provides a literature review of recent bibliometric studies. The following section outlines the materials and methods applied in the study before the results of the present bibliometric analysis are described in the next section. At the end, the final section provides a discussion and conclusion while summarizing the main findings and implications.

2. Literature review

The outbreak of the COVID-19 pandemic led many governments to expand the use of online learning approaches as a solution to the global health challenge. Researchers thus showed rising interest in investigating the field of online learning, its dimensions, and its trends on all levels of education, particularly higher education. Such research relied heavily on bibliometric approaches to analyzing scientific research in the higher education context. Pham et al. (2022) concluded based on the 414 articles that although in the decades prior, there was an increase in the number of articles touching on the components of e-learning, such as the learning management system, this rise was accelerated during the pandemic in both developed and developing countries. This may be attributed to the attention of governmental policies that considered the topic of e-learning to be critical and worthy of priority. Similarly, Fauzi (2022) investigated 1,496 articles and concluded that the research focused on a few specific topics. The first is the delivery factor, which refers to selecting the appropriate learning practices. The second is the health and safety factor that relates to minimizing any risk that e-learning could bring to the mental and physical health of learners or teachers, such as stress, anxiety or even depression. The third topic refers to the field of study and the impact of e-learning. In areas like medical education, where clinical activities and labs have to be attended in person, some online learning approaches might be less appropriate than when used in other areas, such as social studies, where the requirements are less complex or different. Zhang et al. (2022) confirmed this finding after performing bibliometric research on 1,061 articles published between January 2020 and August 2021. They explained that theorists and researchers showed a growing interest in ways to respond to crises, such as the pandemic, and how to develop the best practices to ensure the quality and efficiency of e-learning. Examples of such practices might be inquiry-oriented learning and hands-on activities. This could derive from the already existing tendency of education researchers to respond to unprecedented global challenges or changes. The authors explain that this conclusion addresses interest in e-learning practices holistically.

In the same context, Yan et al. (2022) employed a bibliometric approach and identified that various digital tools are used in e-learning in the field of health studies. After investigating 132 studies, they concluded that selecting appropriate tools depends on many factors, including the field of a given course, the aims, and their effectiveness. They add that these findings can be significant for groups of people such as experts or trainee teachers. Okoro et al. (2022) researched 1,722 articles published between 2012 and 2021 and detected a surge in interest in the mental health of postgraduate students, as revealed by the research trends discussed in these articles. Still, they describe this surge as having been greater between 2020 and 2021, which may be attributed to the COVID-19 restrictions and their implications. Moreover, they believe that this research focus will likely continue soon.

After looking at 2,307 articles published between 2017 and 2021, Baber et al. (2022) detected an increasing trend in researching digital literacy. While this was underway before the pandemic, the latter caused a statistically significant further surge. Digital literacy is approached in the studied articles through parameters like instruction, teachers, learners, ICT and its applications, content knowledge, competencies, skills, perceptions, and higher education. It is also associated with acquiring the qualities required to deal with topics such as misinformation, fake news, technological content knowledge, health literacy, COVID-19, and distance education. The authors state that their study identified dynamics hidden in these research trends, which will likely continue in the next few years.

In higher education specifically, based on 602 articles, Brika et al. (2021) corroborated the growing trend of publishing articles on e-learning during the pandemic and outlined certain sub-topics of it, namely: motivation and students’ attitudes; blended and virtual learning comparison; types of online assessment; stress, anxiety and mental health; strategies to improve learners’ skills; quality; performance of the education delivered; challenges; and the potential of technology to lead to change and reform of higher education syllabi or curricula. The scope of those articles was to paint a bigger picture of how higher education communities and institutions use and treat online learning. This is expected to help with efficient decision-making in the future in order to have better results and functions in higher education and appropriate response to crises.

The bibliometric studies carried out during the pandemic identified a trend among researchers in higher education institutions to investigate more the technology factor and how the progress of the Internet, along with information and communication technologies generally, can further assist new modes of learning, such as online learning and distance learning. This might be attributed to a vision for a better means for new types of learning, as Küçük-Avci et al. (2022) claimed after carrying out a bibliometric analysis of 1,547 articles published between 2020 and 2021. The authors detected certain trends regarding distance learning in higher education. A main finding of their study, along with the increase in studies on distance education and e-learning in higher education, is that before the pandemic, the fact that these approaches were not so mandatory meant there was greater efficiency, probably due to the learners’ motivation. The authors further claim that researchers show a stronger interest in the technological means that can assist these types of learning. In addition, while researching 1,986 articles, Bozkurt (2022) established an increase in the implementation of blended learning by researchers who also aim to investigate the relationship between technological applications and learning institutions. Within these tendencies, researchers consider four thematic fields: a comparison of online and onsite learning with regard to effectiveness and efficiency; the experience, impressions and attitudes of stakeholders and learning community members with respect to blended learning; teacher training and curriculum development that will assure the appropriate and challenge-free implementation of blended learning; and the use of mostly a quantitative approach to research of blended learning.

Bilal et al. (2022) also examined research trends concerned with e-learning in higher education during the COVID-19 period by researching 1,595 studies published between 2020 and 2021. The four main trends they identified were supplementary to those mentioned by other authors: the first is about the challenges regarding online learning or blended learning along with the appropriate strategies in response; the second is student-centered collaborative learning and appropriate curriculum design; the third concerns home-based learning through a type of laboratory and the general conditions surrounding it; and the fourth addresses teachers’ background, training, professional competencies and interdisciplinary learning.

Tlili et al. (2022) focused on mapping COVID-19’s impact on Massive Open Online Courses (MOOCs). The overall finding from the 108 articles they considered is that there has been growing interest in these courses generally, and more specifically in research around their function and quality. This interest encompasses the main features of such courses, which provide easy accessibility and flexibility. However, they noted that this interest followed another trend among researchers in the context. In other words, the countries that published on MOOCs before the pandemic are the same countries that published during the period under study. Moreover, they stated that there is interest in the technical characteristics and requirements of such courses. Finally, the authors concluded that although most MOOCs were ICT courses, research has escalated into courses that refer to business, personal development or the humanities.

Several conclusions can be drawn from the above bibliometric studies. First, the series of bibliometric studies conducted during the pandemic demonstrates the rise of interest in online learning in higher education during the pandemic. Of course, there was a tendency toward e-learning before the pandemic, but between 2020 and 2022, this seems to have accelerated. The phenomenon is more intense in countries such as the USA, Canada, Australia, the UK, India and China. Concerning the area of study, the focus of researchers appears to be greater in fields such as Engineering, Sciences, and Health Sciences, albeit all fields seem to be investigated ( Djeki et al., 2022 ; Pham et al., 2022 ; Vaicondam et al., 2022 ; Zhang et al., 2022 ). Various studies have focused on determining the effectiveness of e-learning classes and courses or pointing out parameters that influence their effectiveness. These could be the appropriate conditions or subtopics like motivation, blended learning, learning tools, teacher training, cooperation between different institutions or efficient practices ( Brika et al., 2021 ; Baber et al., 2022 ; Bilal et al., 2022 ; Bozkurt, 2022 ; Fauzi, 2022 ; Küçük-Avci et al., 2022 ; Yan et al., 2022 ). A specific trend of authors is to examine virtual classes and laboratories ( Kartimi et al., 2022 ; Rojas-Sánchez et al., 2022 ; Zhang et al., 2022 ). Finally, there is a focus on the technology factor. Namely, researchers have concentrated on technical issues and conditions related to e-learning courses and their proper functioning ( Navarro-Espinosa et al., 2021 ; Bozkurt, 2022 ; Tlili et al., 2022 ).

3. Materials and methods

Comprehensive bibliometric data on online learning research during the COVID-19 pandemic were retrieved on 1 March 2022 from Scopus, a world-leading bibliographic database of peer-reviewed literature. The Scopus database was preferred because it has a broader coverage of scientific research than other databases such as Web of Science ( Falagas et al., 2008 ). This was confirmed by an initial search using the same search query in each database, revealing that Scopus provided more relevant documents than Web of Science. Moreover, compared to the Scopus database, the Web of Science has been found to be a database that significantly underrepresents the scientific disciplines of the Social Sciences and the Arts and Humanities ( Mongeon and Paul-Hus, 2016 ). Although English dominates in both Scopus and Web of Science, Scopus generally offers wider coverage of non-English documents, given that the titles, abstracts, and keywords are in English ( Vera-Baceta et al., 2019 ). According to the basic statistical theory, which can also be applied in the context of bibliometric analysis, larger samples lead to analytical outcomes that are likely to be more accurate ( Rogers et al., 2020 ). Therefore, Scopus appears to be a more relevant bibliographic database meeting the specifics of online learning research during the COVID-19 pandemic.

The search strategy was based on title, abstract, and keywords search using the advanced search engine and the search query covered keywords related to different online learning types (using the Boolean operator ‘OR’) and the COVID-19 pandemic (using the Boolean operator ‘AND’). The search was further limited to the period 2020–2022 (using the Boolean operator ‘AND’) to capture documents published between January 2020 and March 2022, when most countries were experiencing constant disruptions in the educational process imposed by repeated lockdowns. As the search query had no language restrictions, the full text of the obtained documents can be in any language, provided that the titles, abstracts, and keywords are in English. Therefore, the language has no impact on the results, as the bibliometric analysis is conducted solely based on the titles, abstracts, and keywords of the documents. According to the presented search query, 9,921 documents were obtained. After further revising the obtained documents, it was identified that some of them are not explicitly related to the context of higher education. By machine screening of documents by title, abstract, and keywords, those related to lower levels of education (i.e., primary and secondary education), as well as adult and worker education (i.e., lifelong education), were excluded from the database. There were 1,618 or 16% of such documents. The remaining 8,303 documents were identified as eligible for further bibliometric examination of online learning research during the COVID-19 pandemic. The bibliometric analysis utilized several bibliometric approaches ( Figure 1 ).

Figure 1 . Bibliometric approaches used in the bibliometric analysis. Own elaboration.

First, a descriptive overview was conducted to examine particular general bibliometric items, including timespan, number of (all, cited, single-authored) documents, authors, sources and author keywords and authors, references, and citations per document as well as to identify the most relevant documents. Scientific production was also examined to determine the most relevant countries and sources. Finally, network analysis was performed to identify the research hotspots according to the keyword co-occurrence network and examine the relationship between the main concepts based on a three-field plot analysis. The presented bibliometric approaches required the use of several different software tools. The descriptive overview was conducted using the Python Data Analysis Library Pandas ( McKinney, 2012 ), scientific production was visualized by the Python Visualization Library Matplotlib ( Hunter, 2007 ), while network analysis was performed using VOSviewer (keyword co-occurrence) ( Van Eck and Waltman, 2010 ) and the Python Visualization Library Plotly (a three-field plot) ( Pandey and Panchal, 2020 ). Specifically, the calculation for the three-field plot analysis included the following steps. Suppose that C 1 , C 2 , … , C m are analysed concepts where each concept C i is defined by a set of keywords and represented by binary indicators W i 1 , W i 2 , … , W i k i , expressed as C i = max j = 1 , … , k i W i j for i = 1 , … , m (matrix column). Using this notation, the relationship between C i and C j can be defined as C 1 T ∗ C j (matrix multiplication) where i and j are from three different sets (ICT tools, online learning approaches, fields of study).

The descriptive overview presented in Table 1 shows the main characteristics of online learning and COVID-19 research in the higher education context. This research area covers a total of 8,303 documents (of which 7,922 (95%) have the full text in English) published in 2,447 sources between January 2020 and March 2022. Slightly less than half (46%) of these documents have at least one citation, while a relatively small number (15%) were written by a single author. The average number of references per document in this research area is 31.39, which is below the general scientific area of Educational Research (44.00) ( Patience et al., 2017 ), suggesting that online learning research during the COVID-19 pandemic is grounded on fewer existing studies than general research. Finally, 3.50 citations per document can be observed for this research area. Due to the potential benefits of online learning, especially when combined with the traditional learning approaches and hence the development of the blended learning environment, this research is expected to further develop and be extended in the ensuing years ( Fauzi, 2022 ). Further, upon analyzing the documents, it is evident that the average year of references is 2014.03, with an h-index of 60 (indicating at least 60 papers with 60 or more citations each) and a g-index of 94 (denoting that the top 94 publications have accumulated citations equal to or greater than the square of 94). Finally, it was found that within the examined dataset, a total of 1,334 documents (16%) have achieved a minimum of 5 citations (C5), while 691 documents (8%) have attained at least 10 citations (C10), 302 documents (4%) have obtained a minimum of 20 citations (C20), 79 documents (1%) have acquired at least 50 citations (C50), and 31 documents (0.4%) have obtained more than 100 citations (C100).

Table 1 . Descriptive overview of online learning and COVID-19 research (2020–2022).

The most relevant (top-10) highly cited documents in online learning and COVID-19 research in the context of higher education are shown in Table 2 . The overview of the most relevant documents reveals several important topics that were intensively discussed. The first most relevant topic concerns ICT. The COVID-19 pandemic has created significant challenges for higher education, especially for medical and surgical education, which requires personal attendance in clinical activities and labs. Accordingly, several innovative ICT tools (i.e., videoconferencing, social media, and telemedicine) and online learning approaches (i.e., flipped classroom or blended learning and virtual learning) were proposed to address this challenge. It is also stressed that by using appropriately established ICT solutions, online learning can lead to more sustainable education ( Adedoyin and Soykan, 2020 ; Chick et al., 2020 ; Dedeilia et al., 2020 ).

Table 2 . Most relevant documents in online learning and COVID-19 research (2020–2022).

The next top-cited topic relates to pedagogy. The disruption of education around the world due to the COVID-19 pandemic required teachers to possess specific pedagogical content knowledge related to designing and organizing better learning experiences with digital technologies. At the same time, challenges for online assessment and post-pandemic pedagogy are also highlighted ( García Peñalvo et al., 2020 ; Iyer et al., 2020 ; Murphy, 2020 ; Rapanta et al., 2020 ). Finally, life and work is another of the most cited topics. Namely, the COVID-19 pandemic has considerably reshaped education and other aspects of life and work, often also through the perspective of mental health or emotional well-being ( Dwivedi et al., 2020 ; Kapasia et al., 2020 ; Aristovnik et al., 2020a ).

Furthermore, it is noteworthy that all of the highly cited documents were published in 2020. However, it is also evident that there are notable and highly relevant publications that emerged in the second year of the COVID-19 pandemic. Accordingly, there are two documents with a minimum of 100 citations published in 2021. In the COVID-19 pandemic context, Watermeyer et al. (2021) , with 148 citations, examined the implications of digital disruption in universities within the United Kingdom, highlighting the challenges and opportunities arising from the emergency shift to online learning. Meanwhile, Pokhrel and Chhetri (2021) conducted a literature review to assess the impact of the COVID-19 pandemic on teaching and learning.

The scientific production across countries and sources is presented in terms of the number of documents and citations, whereby additional information is provided by a circle’s size, revealing the h-index as a measure of the scientific impact ( Harzing and Van Der Wal, 2009 ) and by its color, presenting the time dimension in scientific production. The most relevant (top-10) highly cited countries in online learning and COVID-19 research are shown in Figure 2 . While the United States of America stands out among all countries, the United Kingdom, China and India have a relatively large number of documents and citations. The findings are similar to those of other bibliometric studies on this topic ( Saqr et al., 2023 ).

Figure 2 . Most relevant countries in online learning and COVID-19 research (2020–2022). Own elaboration based on the Scopus database.

The most relevant (top-10) highly cited sources in online learning and COVID-19 research in the context of higher education are presented in Figure 3 . Despite conference proceedings being prominent in terms of the relatively high number of documents, the most prominent journals, considering the number of citations, are Journal of Chemical Education, with the highest number of citations as well as documents, followed by Sustainability, International Journal of Environmental Research and Public Health, and Education Sciences. More specifically, the most relevant journals address different topics. First, Journal of Chemical Education covers the attempts, successes and failures of distance learning during the COVID-19 pandemic in chemistry education. It covers various topics, including the development of at-home practical activities ( Schultz et al., 2020 ), student engagement and learning ( Perets et al., 2020 ), online assessments ( Nguyen et al., 2020 ) and virtual reality labs ( Williams et al., 2021 ). Further, Sustainability is focused on student and teacher perceptions of e-learning and related challenges ( Khan et al., 2020 ; Aristovnik et al., 2020a ) and sustainability in education during the COVID-19 pandemic ( Sobaih et al., 2020 ) to improve online learning and sustain higher education during uncertain times. Further, the International Journal of Environmental Research and Public Health covers various topics like the health and psychological implications of the COVID-19 pandemic ( Sundarasen et al., 2020 ), including well-being and changes in behavior and habits. Finally, Education Sciences publishes some general research on the challenges and opportunities for online learning ( Almazova et al., 2020 ), including student and teacher experiences ( García-Alberti et al., 2021 ; Müller et al., 2021 ).

Figure 3 . Most relevant sources in online learning and COVID-19 research (2020–2022). Own elaboration based on the Scopus database.

The keyword co-occurrence network is presented in Figure 4 . Note that the nodes indicate keywords and the links the relations of co-occurrence between them. The node size is proportional to the number of keyword occurrences, showing the research intensity (node degree), while the link width is proportional to the co-occurrences between keywords (edge weight). In addition, the node color indicates the cluster to which a particular keyword belongs ( Wang et al., 2020 ; Ravšelj et al., 2022 ). The keyword co-occurrence analysis reveals five research hotspots in online learning in higher education research during the COVID-19 pandemic. These are ICT and pedagogy (red cluster), technology-enhanced education (green cluster), mental health and well-being (blue cluster), student experience (yellow cluster) and curriculum and professional development (purple cluster).

Figure 4 . Keyword co-occurrence network in online learning and COVID-19 research (2020–2022). Own elaboration based on the Scopus database.

A detailed synopsis of the research hotspots, including representative (the most frequent) keywords and documents (with several representative keywords), is presented in Table 3 . The first research hotspot highlights the relevance of ICT and pedagogy in higher education during the COVID-19 pandemic. The most representative documents looked at the quality of online learning mechanisms ( Gritsova and Tissen, 2021 ), active learning activities ( Yan et al., 2021 ) and the role of e-learning departments in controlling the quality of academic processes ( Hamdan et al., 2021 ). The second research hotspot refers to technology-enhanced education from different perspectives, such as opportunities to incorporate technological and curricular innovations ( Shapiro and Reza, 2021 ), the adoption of different virtual experiences such as telehealth and virtual learning ( Kahwash et al., 2021 ), and the utilization of social media to reach higher education students ( Leighton et al., 2021 ). The third research hotspot emphasizes the problem of mental health and well-being issues that became a prevalent topic of discussion during the COVID-19 pandemic. Namely, several studies showed an increase in depression, anxiety and stress levels among higher education students in response to the COVID-19 pandemic ( Abu Kwaik et al., 2021 ; Keskin, 2021 ; Yaghi, 2022 ). The fourth cluster is about student experience during the COVID-19 pandemic with specific focus on the between interaction and online learning satisfaction ( Bawa'aneh, 2021 ; Bismala and Manurung, 2021 ; She et al., 2021 ). The fifth research hotspot underscores the relevance of curriculum and professional development. Several studies described the ways in which courses were adapted to online learning during the COVID-19 pandemic as well as the related challenges and strategies ( Chen et al., 2020 ; Gonzalez and Knecht, 2020 ; Rhile, 2020 ).

Table 3 . Research hotspots based on the author keyword co-occurrence network in online learning and COVID-19 research (2020–2022).

Finally, the three-field plot analysis of the relationship between the main concepts (i.e., ICT tools, online learning approaches, fields of study) is presented in a Sankey diagram shown in Figure 5 . The size of a rectangle corresponds to the number of documents for each theme, while the edge width reflects the inclusion index for connected themes ( Wang et al., 2020 ; Ravšelj et al., 2022 ). These three concepts have been proven to be relevant in the context of online learning. Namely, ICT tools are a precondition for delivering course content through different online learning approaches, while the choice of online learning approaches may depend on the field of study ( Ferri et al., 2020 ). During the COVID-19 pandemic, most attention was devoted to exploring e-learning (a combination of asynchronous and synchronous learning), distance learning (pre-recorded online lectures), followed by virtual learning (real-time online lectures). Since all these online learning approaches limit physical contact between teachers and students, they have been referred to as emergency remote learning approaches ( Hodges et al., 2020 ; Fauzi, 2022 ; Fuchs, 2022 ), while other online learning approaches (computer-based learning, blended learning, m-learning) do not necessarily take place in an online learning environment. The emergency remote learning approaches were primarily supported by several ICT tools, particularly by social media (e.g., Facebook), gamification/simulation and virtual reality (integration of game-like elements into online learning platforms, mobile applications, or virtual reality simulations), Zoom and other videoconferencing platforms, as well as telehealth (for educating health professionals). Regarding the fields of study, e-learning, distance learning and virtual learning were mostly addressed in the context of medical/health education, while computer-based learning (i.e., specific engineering software programs etc.) was examined in the context of engineering education. This implies that the specific requirements of a given field of study often guide the selection of the most suitable online learning approaches ( Fauzi, 2022 ).

Figure 5 . Three-field plot showing the network between ICT tools (left), online approaches (middle), and fields of study (right) (2020–2022). Own elaboration based on the Scopus database.

5. Conclusion

The presented bibliometric study provides several important insights arising from research into online learning during the COVID-19 pandemic. In this period, a large volume of scientific knowledge was produced in the context of education that considered a range of aspects ( Saqr et al., 2023 ). Therefore, a combination of selected bibliometric approaches was utilized to extract some general comprehensive outlines of the global research. The bibliometric analysis revealed the following.

As suggested by the descriptive overview of the state of Educational Research ( Patience et al., 2017 ), the research into online learning during the COVID-19 pandemic is characterized by greater cooperation between authors, which coincides with the general observation that (international) scientific collaboration grew significantly during the pandemic ( Duan and Xia, 2021 ). Further, online learning research during the COVID-19 pandemic is grounded on fewer studies than Educational Research ( Patience et al., 2017 ), which may be explained by the absence of COVID-19-related literature at the time these documents were published. Nevertheless, noting the potential benefits of online learning approaches also when the epidemiological conditions are favorable, this line of research is expected to further develop and be extended in the ensuing years ( Fauzi, 2022 ). The potential benefits refer especially to the development of a blended learning environment, which combines online and traditional learning approaches ( Rasheed et al., 2020 ). The overview of the most relevant documents revealed three topics that were intensively discussed in the academic community, i.e., ICT, pedagogy, and life and work. The COVID-19 pandemic highlighted the importance and role of reliable ICT infrastructure for ensuring effective pedagogy in the online environment, as was needed to prevent the spread of the virus and to protect public health. Apart from the devastating health consequences for those directly affected by the virus and the disrupted educational process, the COVID-19 pandemic also dramatically affected students’ social life and work ( Aristovnik et al., 2020a ). The educational community is increasingly interested in finding ways to respond to crises like the COVID-19 pandemic and develop effective pedagogical practices that assure high-quality and efficient education in the online learning environment ( Zhang et al., 2022 ).

The scientific production of online learning during the COVID-19 pandemic was geographically uneven. The greatest scientific production in terms of citations and number of documents can be observed in the United States, followed by the United Kingdom, China and India. Besides developed English-speaking countries, emerging Asian economies also seem to have played a crucial role in online learning research. Similar findings also emerged from other bibliometric studies on this topic ( Saqr et al., 2023 ). Moreover, despite conference proceedings being prominent in terms of the relatively high number of documents, the most prominent journals, considering the number of citations, are Journal of Chemical Education, Sustainability, International Journal of Environmental Research and Public Health and Education Sciences, indicating that online learning research at the time of the COVID-19 pandemic was primarily published in open-access journals, as already observed in other research ( Zhang et al., 2022 ).

The network analysis revealed five research hotspots in online learning research during the COVID-19 pandemic in the context of higher education: (1) ICT and pedagogy, focused on the quality of online learning mechanisms ( Gritsova and Tissen, 2021 ), active learning activities ( Yan et al., 2021 ) and the role of e-learning departments in controlling the quality of academic processes ( Hamdan et al., 2021 ); technology-enhanced education concentrated on opportunities to incorporate technological and curricular innovations ( Shapiro and Reza, 2021 ), the adoption of different virtual experiences such as telehealth and virtual learning ( Kahwash et al., 2021 ), and the utilization of social media to reach higher education students ( Leighton et al., 2021 ); (2) mental health and well-being issues facing higher education students, including depression, anxiety, and stress levels ( Abu Kwaik et al., 2021 ; Keskin, 2021 ; Yaghi, 2022 ); student experience with specific focus on the between interaction and online learning satisfaction ( Bawa'aneh, 2021 ; Bismala and Manurung, 2021 ; She et al., 2021 ) and (3) curriculum and professional development, focused on the ways in which courses were adapted to online learning during the COVID-19 pandemic as well as the related challenges and strategies ( Chen et al., 2020 ; Gonzalez and Knecht, 2020 ; Rhile, 2020 ).

Further, the COVID-19 pandemic led to the exploration of emergency remote learning approaches such as e-learning, distance learning and virtual learning, which are intended to limit physical contact between teachers and students. These approaches were chiefly supported by several ICT tools, including social media, gamification/simulation, virtual reality, videoconferencing platforms, and telehealth. While computer-based learning, blended learning and m-learning do not necessarily occur in an online learning environment, they may still be suitable for certain fields of study, especially in the post-COVID-19 pandemic period. This implies that the determination of which online learning approach is the most suitable is often guided by the specific requirements of a given field of study ( Fauzi, 2022 ).

Before generalizing these conclusions, it is important to note the limitations of the paper. First, the bibliometric analysis relied on documents indexed in the Scopus database, which might not cover the entire collection of research. Namely, documents that are published in journals indexed in other databases such as Web of Science, Education Research Index, Educational Resources Information Centre, etc. are not included in the analysis. However, to achieve the comparability of bibliometric metrics across documents, the bibliometric metrics are obtained from the single and, in general, broader Scopus database. Given the substantial overlap of documents across different databases of peer-reviewed literature, this limitation might not significantly affect the general observations on global research trends. Nevertheless, to check the robustness of the findings, it is still valuable to consider other bibliometric databases for future research. Second, the bibliometric analysis is conducted the bibliometric is based on a short time period (January 2020 – March 2022), which may also impact the metrics of documents published in closed-access (subscription-based) journals, placing them at a disadvantage compared to documents published in open-access journals. While it is not possible to overcome this limitation at present, conducting a bibliometric study with a longer time span would provide further time-dimensional insights. This would also be beneficial in terms of achieving better comparability between documents published in closed-access and open-access journals. Finally, despite the detailed search queries, some other relevant keywords may have been overlooked in the document search. Finally, the bibliometric method, as a method based on big data analysis, may miss certain highlights from the scientific literature that a systematic literature review would otherwise capture. Therefore it would be beneficial for future bibliometric studies also to incorporate a systematic literature review methodology, as the combined approach can provide a more comprehensive and nuanced understanding of the implications of the COVID-19 pandemic on online learning in higher education.

The bibliometric study provides some possible avenues for future research. First, in future bibliometric studies, it would be beneficial to conduct in-depth analyses of the relevant contexts that have emerged as highly significant in online learning during the pandemic. These include ICT and innovation, mental health and well-being, online learning and engagement, and curriculum and professional development. Examining these contexts more comprehensively can provide valuable insights into the specific dynamics and trends within each area, contributing to a deeper understanding of the implications of online learning during the pandemic. Second, it would be beneficial to conduct separate bibliometric analyses and comparisons to examine the differences between developed and developing countries. This approach can shed light on the unique research trends, contributions, and challenges faced by each group of countries in the context of online learning during the pandemic. This can provide a more nuanced understanding of the global landscape and identify potential areas for collaboration and knowledge sharing between developed and developing countries. Finally, it would be valuable to investigate the long-term impact of rapid publishing in open-access journals on the recognition and dissemination of scholarly findings in the field of online learning in higher education during the pandemic.

From the practical perspective, the COVID-19 pandemic has significantly disrupted higher education, but at the same time, it also accelerated the use of online learning tools in the educational process. Although the COVID-19 pandemic has gradually subsided over time, online learning approaches developed during this period continue to hold relevance and value for future education. Therefore, higher education institutions should prioritize leveraging ICT tools and innovative solutions in their educational delivery, which proved effective during the pandemic. Moreover, higher education institutions should also prioritize adapting appropriate online learning approaches and curricula to align with modern realities and the corresponding fields of study. This adaptation is crucial for enhancing student engagement and ensuring that educational programs remain relevant and responsive to the evolving needs of students in various disciplines.

The findings may help not only the scientific community in detecting research gaps in online learning research during the COVID-19 pandemic but also evidence-based policymaking by assisting in identifying appropriate educational practices in emergency circumstances. Specifically, the findings may help higher education policymakers to address the underlying shortcomings of the existing educational framework exposed by the COVID-19 pandemic and to design proactive mechanisms to deal effectively with such disruptions, thereby enabling them to create a more resilient and adaptable education system that can successfully navigate unforeseen challenges and ensure the continuity of quality higher education in the future.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Author contributions

AA contributed to the design of the study. DR and LU assisted with the data identification, cleaning, and analysis. DR and KK wrote the manuscript in consultation with AA. All authors contributed to the manuscript’s revision and read and approved the submitted version.

This research and the APC were funded by the Slovenian Research Agency under grant numbers P5-0093 and Z5-4569.

Acknowledgments

The authors acknowledge the financial support from the Slovenian Research Agency (research core funding no. P5-0093 and project no. Z5-4569). A preliminary version of the paper was presented at the International Conference on Information, Communication Technologies in Education (ICICTE) in July 2022. The authors are grateful to colleagues who attended the presentation and provided interesting comments and suggestions. Further, they wish to thank the reviewers for their valuable suggestions and comments.

Conflict of interest

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

Publisher’s note

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

Abu Kwaik, A., Saleh, R., Danadneh, M., and Kateeb, E. (2021). Stress, anxiety and depression among dental students in times of covid-19 lockdown. Int. J. Dentist. Oral Sci. 8, 1560–1564. doi: 10.19070/2377-8075-21000310

CrossRef Full Text | Google Scholar

Adedoyin, O. B., and Soykan, E. (2020). COVID-19 pandemic and online learning: the challenges and opportunities. Interact. Learn. Environ. 31, 863–875. doi: 10.1080/10494820.2020.1813180

Almazova, N., Krylova, E., Rubtsova, A., and Odinokaya, M. (2020). Challenges and opportunities for Russian higher education amid COVID-19: teachers’ perspective. Educ. Sci. 10:368. doi: 10.3390/educsci10120368

Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., and Umek, L. (2020a). Impacts of the COVID-19 pandemic on life of higher education students: a global perspective. Sustainability 12:8438. doi: 10.3390/su12208438

Aristovnik, A., Ravšelj, D., and Umek, L. (2020b). A bibliometric analysis of COVID-19 across science and social science research landscape. Sustainability 12:9132. doi: 10.3390/su12219132

Baber, H., Fanea-Ivanovici, M., Lee, Y. T., and Tinmaz, H. (2022). A bibliometric analysis of digital literacy research and emerging themes pre-during COVID-19 pandemic. Inform. Learn. Sci. 123, 214–232. doi: 10.1108/ILS-10-2021-0090

Bawa'aneh, M. S. (2021). Distance learning during COVID-19 pandemic in UAE public schools: student satisfaction, attitudes and challenges. Contemp. Educ. Technol. 13:10872. doi: 10.30935/cedtech/10872

Bilal, H., Hysa, E., Akbar, A., Yasmin, F., Rahman, A. u., and Li, S. (2022). Virtual learning during the COVID-19 pandemic: a bibliometric review and future research agenda. Risk Manag. Healthcare Policy 15, 1353–1368. doi: 10.2147/RMHP.S355895

PubMed Abstract | CrossRef Full Text | Google Scholar

Bismala, L., and Manurung, Y. H. (2021). Student satisfaction in E-learning along the COVID-19 pandemic with importance performance analysis. Int. J. Eval. Res. Educ. 10, 753–759. doi: 10.11591/ijere.v10i3.21467

Bonsaksen, T., Leung, J., Schoultz, M., Thygesen, H., Price, D., Ruffolo, M., et al. (2021). Cross-National Study of worrying, loneliness, and mental health during the COVID-19 pandemic: a comparison between individuals with and without infection in the family. Healthcare 9:903. doi: 10.3390/healthcare9070903

Bozkurt, A. (2022). A retro perspective on blended/hybrid learning: systematic review, mapping and visualization of the scholarly landscape. J. Interact. Media Educ. 2022, 1–15. doi: 10.5334/jime.751

Brika, S. K. M., Chergui, K., Algamdi, A., Musa, A. A., and Zouaghi, R. (2021). E-learning research trends in higher education in light of COVID-19: a bibliometric analysis. Front. Psychol. 12:762819. doi: 10.3389/fpsyg.2021.762819

Campos, E., Daruich, S. D. N., Escamilla, J., and Hosseini, S. (2022). Educational model transition: student evaluation of teaching amid the COVID-19 pandemic. Front. Educ. 7:991654. doi: 10.3389/feduc.2022.991654

Chen, K., Chen, Y., Ling, Y., and Lin, J. (2020). The individual experience of online chemistry teacher education in China: coping with COVID-19 pandemic. J. Chem. Educ. 97, 3265–3270. doi: 10.1021/acs.jchemed.0c00581

Chick, R. C., Clifton, G. T., Peace, K. M., Propper, B. W., Hale, D. F., Alseidi, A. A., et al. (2020). Using technology to maintain the education of residents during the COVID-19 pandemic. J. Surg. Educ. 77, 729–732. doi: 10.1016/j.jsurg.2020.03.018

Dedeilia, A., Papapanou, M., Papadopoulos, A. N., Karela, N. R., Androutsou, A., Mitsopoulou, D., et al. (2023). Health worker education during the COVID-19 pandemic: global disruption, responses and lessons for the future—a systematic review and meta-analysis. Hum. Resour. Health 21, 13–35. doi: 10.1186/s12960-023-00799-4

Dedeilia, A., Sotiropoulos, M. G., Hanrahan, J. G., Janga, D., Dedeilias, P., and Sideris, M. (2020). Medical and surgical education challenges and innovations in the COVID-19 era: a systematic review. In Vivo 34, 1603–1611. doi: 10.21873/invivo.11950

Djeki, E., Dégila, J., Bondiombouy, C., and Alhassan, M. H. (2022). E-learning bibliometric analysis from 2015 to 2020. J. Comput. Educ. 9, 727–754. doi: 10.1007/s40692-021-00218-4

Duan, D., and Xia, Q. (2021). Evolution of scientific collaboration on COVID-19: a bibliometric analysis. Learn. Publish. 34, 429–441. doi: 10.1002/leap.1382

Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., et al. (2020). Impact of COVID-19 pandemic on information management research and practice: transforming education, work and life. Int. J. Inf. Manag. 55:102211. doi: 10.1016/j.ijinfomgt.2020.102211

Elmer, T., Mepham, K., and Stadtfeld, C. (2020). Students under lockdown: comparisons of students' social networks and mental health before and during the COVID-19 crisis in Switzerland. PLoS One 15:e0236337. doi: 10.1371/journal.pone.0236337

Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., and Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses. FASEB J. 22, 338–342. doi: 10.1096/fj.07-9492LSF

Fauzi, M. A. (2022). E-learning in higher education institutions during COVID-19 pandemic: current and future trends through bibliometric analysis. Heliyon 8:e09433. doi: 10.1016/j.heliyon.2022.e09433

Ferri, F., Grifoni, P., and Guzzo, T. (2020). Online learning and emergency remote teaching: opportunities and challenges in emergency situations. Societies 10:86. doi: 10.3390/soc10040086

Fried, E. I., Papanikolaou, F., and Epskamp, S. (2021). Mental health and social contact during the COVID-19 pandemic: an ecological momentary assessment study. Clin. Psychol. Sci. 10, 340–354. doi: 10.1177/21677026211017839

Fuchs, K. (2022). The difference between emergency remote teaching and e-learning. Front. Educ. 7:921332. doi: 10.3389/feduc.2022.921332

Gao, Y., Wong, S. L., Khambari, M. N., and Noordin, N. (2022). A bibliometric analysis of the scientific production of e-learning in higher education (1998-2020). Int. J. Inform. Educ. Technol. 12, 390–399. doi: 10.18178/ijiet.2022.12.5.1632

García Peñalvo, F. J., Corell Almuzara, A., Abella García, V., and Grande de Prado, M. (2020). La evaluación online en la educación superior en tempos’ de la COVID-19. Educ. Knowl. Soc. 21:23086. doi: 10.14201/eks.23086

García-Alberti, M., Suárez, F., Chiyón, I., and Mosquera Feijoo, J. C. (2021). Challenges and experiences of online evaluation in courses of civil engineering during the lockdown learning due to the COVID-19 pandemic. Educ. Sci. 11:59. doi: 10.3390/educsci11020059

Gavriluţă, C., Dalban, C. M., and Ioan, B. G. (2022). Educational, emotional, and social impact of the emergency state of COVID-19 on Romanian university students. Int. J. Environ. Res. Public Health 19:3990. doi: 10.3390/ijerph19073990

Gonzalez, C., and Knecht, L. D. (2020). Strategies employed in transitioning multi-instructor, multisection introductory general and organic chemistry courses from face-to-face to online learning. J. Chem. Educ. 97, 2871–2877. doi: 10.1021/acs.jchemed.0c00670

Gritsova, O. A., and Tissen, E. V. (2021). Quality assessment of online learning in regional higher education systems. Econ. Regions 17, 929–943. doi: 10.17059/ekon.reg.2021-3-15

Gurcan, F., Dalveren, G. G. M., and Derawi, M. (2022). COVID-19 and E-learning: an exploratory analysis of research topics and interests in E-learning during the pandemic. IEEE Access 10, 123349–123357. doi: 10.1109/ACCESS.2022.3224034

Hamdan, R., Ashour, W., and Daher, W. (2021). The role of the e-learning departments in controlling the quality of electronic assessments in Palestinian universities during the COVID-19 pandemic. Sustainability 13:12021. doi: 10.3390/su132112021

Harzing, A. W., and Van Der Wal, R. (2009). A Google scholar h-index for journals: an alternative metric to measure journal impact in economics and business. J. Am. Soc. Inf. Sci. Technol. 60, 41–46. doi: 10.1002/asi.20953

Hodges, C. B., Moore, S., Lockee, B. B., Trust, T., and Bond, M. A. (2020). The difference between emergency remote teaching and online learning. Virginia Tech Online Available online at: http://hdl.handle.net/10919/104648 (Accessed February 15, 2023).

Google Scholar

Hunter, J. D. (2007). Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95. doi: 10.1109/MCSE.2007.55

Iyer, P., Aziz, K., and Ojcius, D. M. (2020). Impact of COVID-19 on dental education in the United States. J. Dent. Educ. 84, 718–722. doi: 10.1002/jdd.12163

James, N., and Thériault, V. (2020). Adult education in times of the COVID-19 pandemic: inequalities, changes, and resilience. Stud. Educ. Adults 52, 129–133. doi: 10.1080/02660830.2020.1811474

Kahwash, B. M., Deshpande, D. R., Guo, C., Panganiban, C. M., Wangberg, H., and Craig, T. J. (2021). Allergy/immunology trainee experiences during the COVID-19 pandemic: AAAAI work group report of the fellows-in-training committee. J. Allergy Clin. Immunol. Pract. 9, 1–6.e1. doi: 10.1016/j.jaip.2020.09.036

Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., et al. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Child Youth Serv. Rev. 116:105194. doi: 10.1016/j.childyouth.2020.105194

Kartimi, K., Yunita, Y., Addiin, I., and Shidiq, A. S. (2022). A bibliometric analysis on chemistry virtual laboratory. Educación Química 33:194. doi: 10.22201/fq.18708404e.2022.2.80579

Keržič, D., Alex, J. K., Pamela Balbontín Alvarado, R., da Silva Bezerra, D., Cheraghi, M., Dobrowolska, B., et al. (2021). Academic student satisfaction and perceived performance in the e-learning environment during the COVID-19 pandemic: evidence across ten countries. PLoS One 16:e0258807. doi: 10.1371/journal.pone.0258807

Keržič, D., Umek, L., Tomaževič, N., and Aristovnik, A. (2022). E-learning acceptance of public administration students in Slovenia in comparison with Europe: differences between the first and second waves of the COVID-19 pandemic. EGPA 2022 Conference, 6–9 September 2022, Lisbon, Portugal.

Keskin, G. (2021). Self-report measurement of depression, anxiety, and stress caused by covid-19 pandemic in senior undergraduate dental students. Pesquisa Brasileira em Odontopediatria e Clínica Integrada 21:102. doi: 10.1590/pboci.2021.102

Khan, M. A., Nabi, M. K., Khojah, M., and Tahir, M. (2020). Students’ perception towards e-learning during COVID-19 pandemic in India: an empirical study. Sustainability 13:57. doi: 10.3390/su13010057

Küçük-Avci, Ş., Topal, M., and İstanbullu, A. (2022). The effects of the COVID-19 pandemic on distance education in higher education: a bibliometric analysis study. Croatian J. Educ. 24, 457–488. doi: 10.15516/cje.v24i2.4534

Leighton, K., Kardong-Edgren, S., Schneidereith, T., and Foisy-Doll, C. (2021). Using social media and snowball sampling as an alternative recruitment strategy for research. Clin. Simul. Nurs. 55, 37–42. doi: 10.1016/j.ecns.2021.03.006

McKinney, W. (2012). Python for data analysis: Data wrangling with pandas, NumPy, and IPython . Sebastopol, CA: O'Reilly Media, Inc.

Misiejuk, K., Ness, I. J., Gray, R., and Wasson, B. (2023). Changes in online course designs: before, during, and after the pandemic. Front. Educ. 7:996006. doi: 10.3389/feduc.2022.996006

Mongeon, P., and Paul-Hus, A. (2016). The journal coverage of web of science and Scopus: a comparative analysis. Scientometrics 106, 213–228. doi: 10.1007/s11192-015-1765-5

Müller, A. M., Goh, C., Lim, L. Z., and Gao, X. (2021). COVID-19 emergency elearning and beyond: experiences and perspectives of university educators. Educ. Sci. 11:19. doi: 10.3390/educsci11010019

Murphy, M. P. (2020). COVID-19 and emergency eLearning: consequences of the securitization of higher education for post-pandemic pedagogy. Contemp. Security Policy 41, 492–505. doi: 10.1080/13523260.2020.1761749

Navarro-Espinosa, J. A., Vaquero-Abellán, M., Perea-Moreno, A.-J., Pedrós-Pérez, G., Aparicio-Martínez, P., and Martínez-Jiménez, M. P. (2021). The influence of technology on mental well-being of STEM teachers at university level: COVID-19 as a stressor. Int. J. Environ. Res. Public Health 18:9605. doi: 10.3390/ijerph18189605

Nguyen, J. G., Keuseman, K. J., and Humston, J. J. (2020). Minimize online cheating for online assessments during COVID-19 pandemic. J. Chem. Educ. 97, 3429–3435. doi: 10.1021/acs.jchemed.0c00790

Ochnik, D., Rogowska, A. M., Kuśnierz, C., Jakubiak, M., Wierzbik-Strońska, M., Schütz, A., et al. (2021). Exposure to COVID-19 during the first and the second wave of the pandemic and coronavirus-related PTSD risk among university students from six countries-a repeated cross-sectional study. J. Clin. Med. 10:5564. doi: 10.3390/jcm10235564

Okoro, C., Owojori, O. M., and Umeokafor, N. (2022). The developmental trajectory of a decade of research on mental health and well-being amongst graduate students: a bibliometric analysis. Int. J. Environ. Res. Public Health 19:4929. doi: 10.3390/ijerph19094929

Pandey, K., and Panchal, R. (2020). A study of real world data visualization of COVID-19 dataset using Python. Int. J. Manag. Human. 4, 104–107. doi: 10.35940/ijmh.H0834.044820

Patience, G. S., Patience, C. A., Blais, B., and Bertrand, F. (2017). Citation analysis of scientific categories. Heliyon 3:e00300. doi: 10.1016/j.heliyon.2017.e00300

Perets, E. A., Chabeda, D., Gong, A. Z., Huang, X., Fung, T. S., Ng, K. Y., et al. (2020). Impact of the emergency transition to remote teaching on student engagement in a non-STEM undergraduate chemistry course in the time of COVID-19. J. Chem. Educ. 97, 2439–2447. doi: 10.1021/acs.jchemed.0c00879

Pham, P. T., Lien, D. T. H., Kien, H. C., Chi, N. H., Tinh, P. T., Do, T., et al. (2022). Learning management system in developing countries: a bibliometric analysis between 2005 and 2020. Europ. J. Educ. Res. 11, 1363–1377. doi: 10.12973/eu-jer.11.3.1363

Pokhrel, S., and Chhetri, R. (2021). A literature review on impact of COVID-19 pandemic on teaching and learning. High. Educ. Future 8, 133–141. doi: 10.1177/234763112098348

Raccanello, D., Balbontín-Alvarado, R., da Silva Bezerra, D., Burro, R., Cheraghi, M., Dobrowolska, B., et al. (2022). Higher education students’ achievement emotions and their antecedents in e-learning amid COVID-19 pandemic: a multi-country survey. Learn. Instr. 80:101629. doi: 10.1016/j.learninstruc.2022.101629

Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., and Koole, M. (2020). Online university teaching during and after the COVID-19 crisis: refocusing teacher presence and learning activity. Postdigital Sci. Educ. 2, 923–945. doi: 10.1007/s42438-020-00155-y

Rasheed, R. A., Kamsin, A., and Abdullah, N. A. (2020). Challenges in the online component of blended learning: a systematic review. Comput. Educ. 144:103701. doi: 10.1016/j.compedu.2019.103701

Rasli, A., Tee, M., Ling, L. Y., Cheak, T. Z., and Hui, S. E. (2022). Post COVID-19 strategies for higher education institutions in dealing with unknown and uncertainties. Front. Educ. 7:992063. doi: 10.3389/feduc.2022.992063

Ravšelj, D., Umek, L., Todorovski, L., and Aristovnik, A. (2022). A review of digital era governance research in the first two decades: a bibliometric study. Future Internet 14:126. doi: 10.3390/fi14050126

Rhile, I. J. (2020). Course redesign for college general chemistry during the COVID-19 pandemic. J. Chem. Educ. 97, 2857–2862. doi: 10.1021/acs.jchemed.0c00618

Rogers, G., Szomszor, M., and Adams, J. (2020). Sample size in bibliometric analysis. Scientometrics 125, 777–794. doi: 10.1007/s11192-020-03647-7

Rojas-Sánchez, M. A., Palos-Sánchez, P. R., and Folgado-Fernández, J. A. (2022). Systematic literature review and bibliometric analysis on virtual reality and education. Educ. Inf. Technol. 28, 155–192. doi: 10.1007/s10639-022-11167-5

Saqr, M., Raspopovic Milic, M., Pancheva, K., Jovic, J., Peltekova, E. V., and Conde, M. Á. (2023). A multimethod synthesis of COVID-19 education research: the tightrope between covidization and meaningfulness. Univ. Access Inf. Soc. 1-14, 1–14. doi: 10.1007/s10209-023-00989-w

Schultz, M., Callahan, D. L., and Miltiadous, A. (2020). Development and use of kitchen chemistry home practical activities during unanticipated campus closures. J. Chem. Educ. 97, 2678–2684. doi: 10.1021/acs.jchemed.0c00620

Shapiro, H., and Reza, N. (2021). Cardiovascular medical education during the coronavirus disease 2019 pandemic: challenges, adaptations, and considerations for the future. US Cardiol. 15:25. doi: 10.15420/usc.2020.25

She, L., Ma, L., Jan, A., Sharif Nia, H., and Rahmatpour, P. (2021). Online learning satisfaction during COVID-19 pandemic among Chinese university students: the serial mediation model. Front. Psychol. 12:743936. doi: 10.3389/fpsyg.2021.743936

Sobaih, A. E. E., Hasanein, A. M., and Abu Elnasr, A. E. (2020). Responses to COVID-19 in higher education: social media usage for sustaining formal academic communication in developing countries. Sustainability 12:6520. doi: 10.3390/su12166520x

Sundarasen, S., Chinna, K., Kamaludin, K., Nurunnabi, M., Baloch, G. M., Khoshaim, H. B., et al. (2020). Psychological impact of COVID-19 and lockdown among university students in Malaysia: implications and policy recommendations. Int. J. Environ. Res. Public Health 17:6206. doi: 10.3390/ijerph17176206

Tang, K. H. D. (2023). Impacts of COVID-19 on primary, secondary and tertiary education: a comprehensive review and recommendations for educational practices. Educ. Res. Policy Prac. 22, 23–61. doi: 10.1007/s10671-022-09319-y

Tlili, A., Altınay, F., Altınay, Z., Aydın, C. H., Huang, R., and Sharma, R. (2022). Reflections on massive open online courses (Moocs) during the COVID-19 pandemic: a bibliometric mapping analysis. Turk. Online J. Dist. Educ. 23, 1–17. doi: 10.17718/tojde.1137107

Vaicondam, Y., Sikandar, H., Irum, S., Khan, N., and Qureshi, M. I. (2022). Research landscape of digital learning over the past 20 years: a bibliometric and visualisation analysis. Int. J. Online Biomed. Eng. 18, 4–22. doi: 10.3991/ijoe.v18i08.31963

Van der Graaf, L., Dunajeva, J., Siarova, H., and Bankauskaite, R. (2021). Research for CULT committee – Education and youth in post-COVID-19 Europe – Crisis effects and policy recommendations. European Parliament, Policy Department for Structural and Cohesion Policies, Brussels. Available at: http://www.europarl.europa.eu/thinktank/en/document.html?reference=IPOL_STU(2021)690872 (Accessed February 15, 2023).

Van Eck, N., and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523–538. doi: 10.1007/s11192-009-0146-3

Vera-Baceta, M. A., Thelwall, M., and Kousha, K. (2019). Web of science and Scopus language coverage. Scientometrics 121, 1803–1813. doi: 10.1007/s11192-019-03264-z

Waller, R., Hodge, S., Holford, J., Milana, M., and Webb, S. (2020). Lifelong education, social inequality and the COVID-19 health pandemic. Int. J. Lifelong Educ. 39, 243–246. doi: 10.1080/02601370.2020.1790267

Wang, C., Lim, M. K., Zhao, L., Tseng, M. L., Chien, C. F., and Lev, B. (2020). The evolution of omega-the international journal of management science over the past 40 years: a bibliometric overview. Omega 93:102098. doi: 10.1016/j.omega.2019.08.005

Watermeyer, R., Crick, T., Knight, C., and Goodall, J. (2021). COVID-19 and digital disruption in UK universities: afflictions and affordances of emergency online migration. High. Educ. 81, 623–641. doi: 10.1007/s10734-020-00561-y

Williams, N. D., Gallardo-Williams, M. T., Griffith, E. H., and Bretz, S. L. (2021). Investigating meaningful learning in virtual reality organic chemistry laboratories. J. Chem. Educ. 99, 1100–1105. doi: 10.1021/acs.jchemed.1c00476

Yaghi, A. (2022). Impact of online education on anxiety and stress among undergraduate public affairs students: a longitudinal study during the COVID-19 pandemic. J. Public Affairs Educ. 28, 91–108. doi: 10.1080/15236803.2021.1954469

Yan, Y., Cheng, X., Zhou, C., Yang, X., and Li, Y. Q. (2021). The perceptions of anatomy teachers for different majors during the COVID-19 pandemic: a national Chinese survey. Med. Educ. Online 26:1897267. doi: 10.1080/10872981.2021.1897267

Yan, H., Rahgozar, A., Sethuram, C., Karunananthan, S., Archibald, D., Bradley, L., et al. (2022). Natural language processing to identify digital learning tools in postgraduate family medicine: protocol for a scoping review. JMIR Res. Protocols 11:e34575. doi: 10.2196/34575

Zhang, L., Carter, R. A. Jr., Qian, X., Yang, S., Rujimora, J., and Wen, S. (2022). Academia’s responses to crisis: a bibliometric analysis of literature on online learning in higher education during COVID-19. Br. J. Educ. Technol. 53, 620–646. doi: 10.1111/bjet.13191

Keywords: online learning, e-learning, higher education, bibliometrics, mapping, visualization, VOSviewer, COVID-19

Citation: Aristovnik A, Karampelas K, Umek L and Ravšelj D (2023) Impact of the COVID-19 pandemic on online learning in higher education: a bibliometric analysis. Front. Educ . 8:1225834. doi: 10.3389/feduc.2023.1225834

Received: 19 May 2023; Accepted: 14 July 2023; Published: 03 August 2023.

Reviewed by:

Copyright © 2023 Aristovnik, Karampelas, Umek and Ravšelj. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Aleksander Aristovnik, [email protected] ; Dejan Ravšelj, [email protected]

This article is part of the Research Topic

Increased Quality Education Through Cross-Campus Learning Environments

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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• 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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New World Bank report: Remote Learning during the pandemic: Lessons from today, principles for tomorrow 

WASHINGTON, D.C., Nov. 18, 2021— Education systems around the world reacted to COVID-19 by closing schools and rolling out remote learning options for their students as an emergency response.  New World Bank analysis of early evidence reveals that while remote learning has not been equally effective everywhere, hybrid learning is here to stay.

Going forward, for remote learning to deliver on its potential, the analysis shows the need to ensure strong alignment between three complementary components: effective teaching, suitable technology, and engaged learners.

“Hybrid learning – which combines in-person and remote learning – is here to stay. The challenge will be the art of combining technology and the human factor to make hybrid learning a tool to expand access to quality education for all,” emphasized Jaime Saavedra, World Bank Global Director for Education .   “Information technology is only a complement, not a substitute, for the conventional teaching process – particularly among preschool and elementary school students. The importance of teachers, and the recognition of education as essentially a human interaction endeavor, is now even clearer.”

The twin reports, Remote Learning During the Global School Lockdown: Multi-Country Lessons and Remote Learning During COVID-19: Lessons from Today, Principles for Tomorrow , stress that three components are critical for remote learning to be effective:

• Prioritizing effective teachers: a teacher with high subject content knowledge, skills to use technology, and appropriate pedagogical tools and support is more likely to be effective at remote instruction.
• Adopting suitable technology: availability of technology is a necessary but not sufficient condition for effective remote learning.
• Ensuring learners are engaged: for students to be engaged, contextual factors such as the home environment, family support, and motivation for learning must be well aligned.

The reports found that many countries struggled to ensure take-up and some even found themselves in a remote learning paradox: choosing a distance learning approach unsuited to the access and capabilities of a majority of their teachers and students.

“Emerging evidence on the effectiveness of remote learning during COVID-19 is mixed at best,” said Cristóbal Cobo, World Bank Senior Education and Technology Specialist, and co-author of the two reports . “Some countries provided online digital learning solutions, although a majority of students lacked digital devices or connectivity, thus resulting in uneven participation, which further exacerbated existing inequalities. Other factors leading to low student take-up are unconducive home environments; challenges in maintaining children’s engagement, especially that of younger children; and low digital literacy of students, teachers, and/or parents.”

“While pre-pandemic access to technology and capabilities to use it differed widely within and across countries, limited parental engagement and support for children from poor families has generally hindered their ability to benefit from remote learning,” stressed Saavedra .

Despite these challenges with remote learning, this can be an unprecedented opportunity to leverage its potential to reimagine learning and to build back more effective and equitable education systems. Hybrid learning is part of the solution for the future to make the education process more effective and resilient. 

The reports offer the following five principles to guide country efforts going forward:

• Ensure remote learning is fit-for-purpose. Countries should choose modes of remote learning that are suitable to the access and utilization of technology among both teachers and students, including digital skills, and that teachers have opportunities to develop the technical and pedagogical competencies needed for effective remote teaching. 
• Use technology to enhance the effectiveness of teachers. Teacher professional development should develop the skills and support needed to be an effective teacher in a remote setting.
• Establish meaningful two-way interactions. Using the most appropriate technology for the local context, it is imperative to enable opportunities for students and teachers to interact with each other with suitable adaptations to the delivery of the curriculum.
• Engage and support parents as partners in the teaching and learning process. It is imperative that parents (families) are engaged and supported to help students access remote learning and to ensure both continuity of learning and protect children’s socioemotional well-being.
• Rally all actors to cooperate around learning . Cooperation across all levels of government; as well as partnerships between the public and private sector, and between groups of teachers and school principals; is vital to the effectiveness of remote learning and to ensure that the system continues to adapt, learn, and improve in an ever-changing remote learning landscape.

World Bank Education Response to COVID-19

In response to the deepening education crisis, the World Bank has rapidly ramped up its support to developing countries, with projects reaching at least 432 million students and 26 million teachers – one-third of the student population and nearly a quarter of the teacher workforce in current client countries. The World Bank is the largest source of external financing for education in developing countries. In the last two fiscal years, our support to education has reached $11.5 billion.

World Bank Group Response to COVID-19

Since the start of the COVID-19 pandemic, the World Bank Group has deployed over $157 billion to fight the health, economic, and social impacts of the pandemic, the fastest and largest crisis response in its history. The financing is helping more than 100 countries strengthen pandemic preparedness, protect the poor and jobs, and jump start a climate-friendly recovery. The Bank is also supporting over 60 low- and middle-income countries , more than half of which are in Africa, with the purchase and deployment of COVID-19 vaccines, and is making available $20 billion in financing for this purpose until the end of 2022.

For more information on the twin reports, please visit their website .

For more information on the World Bank and Education, please visit:  www.worldbank.org/education

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What the Covid-19 Pandemic Revealed About Remote School

The unplanned experiment provided clear lessons on the value—and limitations—of online learning. Are educators listening?

Katherine Reynolds Lewis, Undark Magazine

The transition to online learning in the United States during the Covid-19 pandemic was, by many accounts, a failure. While there were some  bright   spots  across the country, the transition was messy and uneven — countless teachers had neither the materials nor training they needed to effectively connect with students remotely, while many of those students   were bored , isolated, and lacked the resources they needed to learn. The results were abysmal: low test scores, fewer children learning at grade level, increased inequity, and teacher burnout. With the public health crisis on top of deaths and job losses in many families, students experienced   increases  in depression, anxiety, and suicide risk.

Yet society very well may face new widespread calamities in the near future, from another pandemic to extreme weather, that will require a similarly quick shift to remote school. Success will hinge on big changes, from infrastructure to teacher training, several experts told Undark. “We absolutely need to invest in ways for schools to run continuously, to pick up where they left off. But man, it’s a tall order,” said Heather L. Schwartz, a senior policy researcher at RAND. “It’s not good enough for teachers to simply refer students to disconnected, stand-alone videos on, say, YouTube. Students need lessons that connect directly to what they were learning before school closed.”

More than three years after U.S. schools shifted to remote instruction on an emergency basis, the education sector is still largely unprepared for another long-term interruption of in-person school. The stakes are highest for those who need it most: low-income children and students of color, who are also most likely to be harmed in a future pandemic or live in communities  most affected  by climate change. But, given the abundance of research on what didn’t work during the pandemic, school leaders may have the opportunity to do things differently next time. Being ready would require strategic planning, rethinking the role of the teacher, and using new technology wisely, experts told Undark. And many problems with remote learning actually trace back not to technology, but to basic instructional quality. Effective remote learning won’t happen if schools aren’t already employing best practices in the physical classroom, such as creating a culture of learning from mistakes, empowering teachers to meet individual student needs, establishing high expectations, and setting clear goals supported by frequent feedback. While it’s ambitious to envision that every school district will create seamless virtual learning platforms — and, for that matter, overcome challenges in education more broadly — the lessons of the pandemic are there to be followed or ignored.

“We haven’t done anywhere near the amount of planning or the development of the instructional infrastructure needed to allow for a smooth transition next time schools need to close for prolonged periods of time,” Schwartz said. “Until we can reach that goal, I don’t have high confidence that the next prolonged school closure will be substantially more successful.”

Before the pandemic,  only 3 percent  of U.S. school districts offered virtual school, mostly for students with unique circumstances, such as a disability or those intensely pursuing a sport or the performing arts, according to a RAND  survey  Schwartz co-authored. For the most part, the educational technology companies and developers creating software for these schools promised to give students a personalized experience. But the research on these programs, which focused on virtual charter schools that only existed online, showed  poor outcomes . Their students were a year behind in math and nearly a half-year behind in reading, and courses offered less direct time with a teacher each week than regular schools have in a day.

The pandemic sparked growth in stand-alone virtual academies, in addition to the emergency remote learning that districts had to adopt in March 2020. Educators’ interest in online instructional materials exploded, too, according to Schwartz, “and it really put the foot on the gas to ramp them up, expand them, and in theory, improve them.” By June 2021, the number of school districts with a stand-alone virtual school rose to 26 percent. Of the remaining districts, another 23 percent were interested in offering an online school, the report found.

But the sheer magnitude of options for online learning didn’t necessarily mean it worked well, Schwartz said: “It’s the quality part that has to come up in order for this to be a really good, viable alternative to in person instruction.” And individualized, self-directed online learning proved to be a pipe dream — especially for younger children who needed support from a parent or other family member even to get online, much less stay focused.

“The notion that students would have personalized playlists and could curate their own education was proven to be problematic on a couple levels, especially for younger and less affluent students,” said Thomas Toch, director of FutureEd, an education think tank at Georgetown University’s McCourt School of Public Policy. “The social and emotional toll that isolation and those traumas took on students suggest that the social dimension of schooling is hugely important and was greatly undervalued, especially by proponents for an increased role of technology.”

Students also often didn’t have the materials they needed for online school, some lacking computers or internet access at home. Teachers didn’t have the right training for  online instruction , which has a unique pedagogy and best practices. As a result, many virtual classrooms attempted to replicate the same lessons over video that would’ve been delivered at school. The results were overwhelmingly bad, research shows. ​​For example, a  2022 study  found six consistent themes about how the pandemic affected learning, including a lack of interaction between students and with teachers, and disproportionate harm to low-income students. Numb from isolation and too many hours in front of a screen, students  failed to engage  in coursework and  suffered emotionally .

After some districts resumed in-person or hybrid instruction in the 2020 fall semester, it became clear that the longer students were remote,  the worse their learning delays . For example, national standardized test scores for the 2020-2021 school year showed that passing rates for math declined about 14 percentage points on average, more than three times the drop seen in districts that returned to in-person instruction the earliest, according to a  2021 National Bureau of Economic Research study . Even after most U.S. districts resumed in-person instruction, students who had been online the longest  continued to lag  behind their peers. The pandemic  hit cities hardest  and the effects disproportionately harmed low-income children and students of color in urban areas.

“What we did during the pandemic is not the optimal use of online learning in education for the future,” said Ashley Jochim, a researcher at the Center on Reinventing Public Education at Arizona State University’s Mary Lou Fulton Teachers College. “Online learning is not a full stop substitute for what kids need to thrive and be supported at school.”

Children also largely prefer in-person school. A  2022 Pew Research Center survey  suggested that 65 percent of students would rather be in a classroom, 9 percent would opt for online only, and the rest are unsure or prefer a hybrid model. “For most families and kids, full-time online school is actually not the educational solution they want,” Jochim said.

Virtual school felt meaningless to Abner Magdaleno, a 12th grader in Los Angeles. “I couldn’t really connect with it, because I’m more of, like, a social person. And that was stripped away from me when we went online,” recalled Magdaleno. Mackenzie Sheehy, 19, of Fond du Lac, Wisconsin, found there were too many distractions at home to learn. Her grades suffered, and she missed the one-on-one time with teachers. (Sheehy graduated from high school in 2022.)

Many teachers feel the same way. “Nothing replaces physical proximity, whatever the age,” said Ana Silva, a New York City English teacher. She enjoyed experimenting with interactive technology during online school, but is grateful to be back in person. “I like the casual way kids can come to my desk and see me. I like the dynamism — seeing kids in the cafeteria. Those interactions are really positive, and they were entirely missing during the online learning.”

During the 2022-2023 school year, many districts  initially planned  to continue online courses for snow days and other building closures. But they found that the teacher instruction, student experience, and demands on families were simply too different for in-person versus remote school, said Liz Kolb, an associate professor in the School of Education at the University of Michigan. “Schools are moving away from that because it’s too difficult to quickly transition and blend back and forth among the two without having strong structures in place,” Kolb said. “Most schools don’t have those strong structures.”

In addition, both families and educators grew sick of their screens. “They’re trying to avoid technology a little bit. There’s this fatigue coming out of remote learning and the pandemic,” said Mingyu Feng, a research director at WestEd, a nonprofit research agency. “If the students are on Zoom every day for like, six hours, that seems to be not quite right.”

Despite the bumpy pandemic rollout, online school can serve an important role in the U.S. education system. For one, online learning is a better alternative for some students. Garvey Mortley, 15, of Bethesda, Maryland, and her two sisters all switched to their district’s virtual academy during the pandemic to protect their own health and their grandmother’s. This year, Mortley’s sisters went back to in-person school, but she chose to stay online. “I love the flexibility about it,” she said, noting that some of her classmates prefer it because they have a disability or have demanding schedules. “I love how I can just roll out of bed in the morning, and I can sit down and do school.” Some educators also prefer teaching online, according to  reports  of virtual schools that were inundated with applications from teachers because they wanted to keep  working from home . Silva, the New York high school English teacher, enjoys online tutoring and academic coaching, because it facilitates one-on-one interaction.

And in rural districts and those with low enrollment, some access to online learning ensures students can take courses that could otherwise be inaccessible. “Because of the economies of scale in small rural districts, they needed to tap into online and shared service delivery arrangements in order to provide a full complement of coursework at the high school level,” said Jochim. Innovation in these districts, she added, will accelerate: “We’ll continue to see growth, scalability, and improvement in quality.”

There were also some schools that were largely successful at switching to online at the start of the pandemic, such as Vista Unified School District in California, which  pooled and shared innovative ideas  for adapting in March 2020; the school quickly put together an online portal so that principals and teachers could share ideas and the district could allot the necessary resources. Digging into examples like this could point the way to the future of online learning, said Chelsea Waite, a senior researcher at the Center on Reinventing Public Education, who was part of a collaborative project studying 70 schools and districts that pivoted successfully to online learning. The  project found  three factors that made the transition work: a focus on resilience, collaboration, and autonomy for both students and educators; a healthy culture that prioritized relationships; and strong yet flexible systems that were accustomed to adaptation.

“We investigated schools that did seem to be more prepared for the Covid disruption, not just with having devices in students’ hands or having an online curriculum already, but with a learning culture in the school that really prioritized agency and problem solving as skills for students and adults,” Waite said. “In these schools, kids are learning from a very young age to be a little bit more self-directed, to set goals, and pursue them and pivot when they need to.”

Similarly, many of the takeaways from the pandemic trace back to the basics of effective education, not technological innovation. A landmark report by the National Academies of Sciences called “How People Learn,” most recently updated in 2018, synthesized the body of educational research and identified four key features in the most successful learning environments. First, these schools are designed for, and adapt to, the specific students, building on what they bring to the classroom, such as skills and beliefs. Second, successful schools give their students clear goals, showing them what they need to learn and how they can get there. Third, they provide in-the-moment feedback that emphasizes understanding, not memorization. And finally, the most successful schools are community-centered, with a culture of collaboration and acceptance of mistakes.

“We as humans are social learners, yet some of the tech talk is driven by people who are strong individual learners,” said Jeremy Roschelle, executive director of Learning Sciences Research at Digital Promise, a global education nonprofit. “They’re not necessarily thinking about how most people learn, which is very social.”

Another powerful insight from pandemic-era remote schooling involves the evolving role of teachers, said Kim Kelly, a middle school math teacher at Northbridge Middle School in Massachusetts and a K-8 curriculum coach. Historically, a teacher’s role is the keeper of knowledge who delivers instruction. But in recent years, there has been a shift in approach, where teachers think of themselves as coaches who can intervene based on a student’s individual learning progress. Technology that assists with a coach-like role can be effective — but requires educators to be trained and comfortable interpreting data on student needs.

For example, with a digital learning platform called ASSISTments, teachers can assign math problems, students complete them — potentially receiving in-the-moment feedback on steps they’re getting wrong — and then the teachers can use data from individual students and the entire class to plan instruction and see where additional support is needed.

“A big advantage of these computer-driven products is they really try to diagnose where students are, and try to address their needs. It’s very personalized, individualized,” said WestEd’s Feng, who has  evaluated  ASSISTments and other educational technologies. She noted that some teachers feel frustrated “when you expect them to read the data and try to figure out what the students’ needs are.”

Teacher’s colleges don’t typically prepare educators to interpret data and change their practices, said Kelly, whose dissertation focused on self-regulated online learning. But professional development has helped her learn to harness technology to improve teaching and learning. “Schools are in data overload; we are oozing data from every direction, yet none of it is very actionable,” she said. Some technology, she added, provided student data that she could use regularly, which changed how she taught and assigned homework.

When students get feedback from the computer program during a homework session, the whole class doesn’t have to review the homework together, which can save time. Educators can move forward on instruction — or if they see areas of confusion, focus more on those topics. The ability of the programs to detect how well students are learning “is unreal,” said Kelly, “but it really does require teachers to be monitoring that data and interpreting.” She learned to accept that some students could drive their own learning and act on the feedback from homework, while others simply needed more teacher intervention. She now does more assessment at the beginning of a course to better support all students.

At the district or even national level, letting teachers play to their strengths can also help improve how their students learn, Toch, of FutureEd, said. For example, if a teacher is better at delivering instruction, they could give a lesson to a larger group of students online, while another teacher who is more comfortable in the coach role could work in smaller groups or one-on-one.

“One thing we saw during the pandemic are smart strategies for using technology to get outstanding teachers in front of more students,” Toch said, describing one effort that recruited exceptional teachers nationally and built a strong curriculum to be delivered online. “The local educators were providing support for their students in their classrooms.”

Remote schooling requires new technology, and already, educators are swamped with competing platforms and software choices — most of which have  insufficient evidence of efficacy . Traditional independent research on specific technologies is sparse, Roschelle said. Post-pandemic, the field is so diverse and there are so many technologies in use, it’s almost impossible to find a control group to design a randomized control trial, he added. However, there is qualitative research and evidence that give hints about the quality of technology and online learning, such as  case studies  and school recommendations.

Educational leaders should ask three key questions about technology before investing, recommended Ryan Baker, a professor of education at the University of Pennsylvania: Is there evidence it works to improve learning outcomes? Does the vendor provide support and training, or are teachers on their own? And does it work with the same types of students as are in their school or district? In other words, educators must look at a technology’s track record in the context of their own school’s demographics, geography, culture, and challenges. These decisions are complicated by the small universe of researchers and evaluators, who have many overlapping relationships. (Over his career, for example, Baker has worked with or consulted for many of the education technology firms that create the software he studies.)

It may help to broaden the definition of evidence. The Center on Reinventing Public Education launched the  Canopy project  to collect examples of effective educational innovation around the U.S.

“What we wanted to do is build much better and more open and collective knowledge about where schools are challenging old assumptions and redesigning what school is and should be,” she added, noting that these educational leaders are reconceptualizing the skills they want students to attain. “They’re often trying to measure or communicate concepts that we don’t have great measurement tools for yet. So they end up relying on a lot of testimonials and evidence of student work.”

The moment is ripe for innovation in online and in-person education, said Julia Fallon, executive director of the State Educational Technology Directors Association, since the pandemic accelerated the rollout of devices and needed infrastructure. There’s an  opportunity  and need for technology that empowers teachers to improve learning outcomes and work more efficiently, said Roschelle. Online and hybrid learning are clearly here to stay — and likely will be called upon again during future temporary school closures.

Still, poorly-executed remote learning risks tainting the whole model; parents and students may be unlikely to give it a second chance. The pandemic showed the hard and fast limits on the potential for fully remote learning to be adopted broadly, for one, because in many communities, schools serve more than an educational function — they support children’s mental health, social needs, and nutrition and other physical health needs. The pandemic also highlighted the real challenge in training the entire U.S. teaching corps to be proficient in technology and data analysis. And the lack of a nimble shift to remote learning in an emergency will disproportionately harm low-income children and students of color. So the stakes are high for getting it right, experts told Undark, and summoning the political will.

“There are these benefits in online education, but there are also these real weaknesses we know from prior research and experience,” Jochim said. “So how do we build a system that has online learning as a complement to this other set of supports and experiences that kids benefit from?”

Katherine Reynolds Lewis is an award-winning journalist covering children, race, gender, disability, mental health, social justice, and science.

This article was originally published on Undark . Read the original article .

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Student Opinion

Is Online Learning Effective?

A new report found that the heavy dependence on technology during the pandemic caused “staggering” education inequality. What was your experience?

By Natalie Proulx

During the coronavirus pandemic, many schools moved classes online. Was your school one of them? If so, what was it like to attend school online? Did you enjoy it? Did it work for you?

In “ Dependence on Tech Caused ‘Staggering’ Education Inequality, U.N. Agency Says ,” Natasha Singer writes:

In early 2020, as the coronavirus spread, schools around the world abruptly halted in-person education. To many governments and parents, moving classes online seemed the obvious stopgap solution. In the United States, school districts scrambled to secure digital devices for students. Almost overnight, videoconferencing software like Zoom became the main platform teachers used to deliver real-time instruction to students at home. Now a report from UNESCO , the United Nations’ educational and cultural organization, says that overreliance on remote learning technology during the pandemic led to “staggering” education inequality around the world. It was, according to a 655-page report that UNESCO released on Wednesday, a worldwide “ed-tech tragedy.” The report, from UNESCO’s Future of Education division, is likely to add fuel to the debate over how governments and local school districts handled pandemic restrictions, and whether it would have been better for some countries to reopen schools for in-person instruction sooner. The UNESCO researchers argued in the report that “unprecedented” dependence on technology — intended to ensure that children could continue their schooling — worsened disparities and learning loss for hundreds of millions of students around the world, including in Kenya, Brazil, Britain and the United States. The promotion of remote online learning as the primary solution for pandemic schooling also hindered public discussion of more equitable, lower-tech alternatives, such as regularly providing schoolwork packets for every student, delivering school lessons by radio or television — and reopening schools sooner for in-person classes, the researchers said. “Available evidence strongly indicates that the bright spots of the ed-tech experiences during the pandemic, while important and deserving of attention, were vastly eclipsed by failure,” the UNESCO report said. The UNESCO researchers recommended that education officials prioritize in-person instruction with teachers, not online platforms, as the primary driver of student learning. And they encouraged schools to ensure that emerging technologies like A.I. chatbots concretely benefited students before introducing them for educational use. Education and industry experts welcomed the report, saying more research on the effects of pandemic learning was needed. “The report’s conclusion — that societies must be vigilant about the ways digital tools are reshaping education — is incredibly important,” said Paul Lekas, the head of global public policy for the Software & Information Industry Association, a group whose members include Amazon, Apple and Google. “There are lots of lessons that can be learned from how digital education occurred during the pandemic and ways in which to lessen the digital divide. ” Jean-Claude Brizard, the chief executive of Digital Promise, a nonprofit education group that has received funding from Google, HP and Verizon, acknowledged that “technology is not a cure-all.” But he also said that while school systems were largely unprepared for the pandemic, online education tools helped foster “more individualized, enhanced learning experiences as schools shifted to virtual classrooms.” ​Education International, an umbrella organization for about 380 teachers’ unions and 32 million teachers worldwide, said the UNESCO report underlined the importance of in-person, face-to-face teaching. “The report tells us definitively what we already know to be true, a place called school matters,” said Haldis Holst, the group’s deputy general secretary. “Education is not transactional nor is it simply content delivery. It is relational. It is social. It is human at its core.”

Students, read the entire article and then tell us:

What findings from the report, if any, surprised you? If you participated in online learning during the pandemic, what in the report reflected your experience? If the researchers had asked you about what remote learning was like for you, what would you have told them?

At this point, most schools have returned to in-person teaching, but many still use technology in the classroom. How much tech is involved in your day-to-day education? Does this method of learning work well for you? If you had a say, would you want to spend more or less time online while in school?

What are some of the biggest benefits you have seen from technology when it comes to your education? What are some of the biggest drawbacks?

Haldis Holst, UNESCO’s deputy general secretary, said: “The report tells us definitively what we already know to be true, a place called school matters. Education is not transactional nor is it simply content delivery. It is relational. It is social. It is human at its core.” What is your reaction to that statement? Do you agree? Why or why not?

As a student, what advice would you give to schools that are already using or are considering using educational technology?

Students 13 and older in the United States and Britain, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public and may appear in print.

Find more Student Opinion questions here. Teachers, check out this guide to learn how you can incorporate these prompts into your classroom.

Natalie Proulx joined The Learning Network as a staff editor in 2017 after working as an English language arts teacher and curriculum writer. More about Natalie Proulx

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The COVID-19 pandemic and E-learning: challenges and opportunities from the perspective of students and instructors

Abdelsalam m. maatuk.

1 Faculty of Information Technology, Benghazi University, Benghazi, Libya

Ebitisam K. Elberkawi

Shadi aljawarneh.

2 Faculty of Computer and Information Technology, Irbid, Jordan

Hasan Rashaideh

3 Department of Computer Science, Prince Abdullah Ben Ghazi Faculty of Information Technology and Communication Technology, Al-Balqa Applied University, Salt, 19117 Jordan

Hadeel Alharbi

4 Computer Science, Ha’il University, Ha’il, Saudi Arabia

The spread of COVID-19 poses a threat to humanity, as this pandemic has forced many global activities to close, including educational activities. To reduce the spread of the virus, education institutions have been forced to switch to e-learning using available educational platforms, despite the challenges facing this sudden transformation. In order to further explore the potentials challenges facing learning activities, the focus of this study is on e-learning from students’ and instructor’s perspectives on using and implementing e-learning systems in a public university during the COVID-19 pandemic. The study targets the society that includes students and teaching staff in the Information Technology (IT) faculty at the University of Benghazi. The descriptive-analytical approach was applied and the results were analyzed by statistical methods. Two types of questionnaires were designed and distributed, i.e., the student questionnaire and the instructor questionnaire. Four dimensions have been highlighted to reach the expected results, i.e., the extent of using e-learning during the COVID-19 pandemic, advantages, disadvantages and obstacles of implementing E-learning in the IT faculty. By analyzing the results, we achieved encouraging results that throw light on some of the issues, challenges and advantages of using e-learning systems instead of traditional education in higher education in general and during emergency periods.

Introduction

The unexpected closure of educational institutions as a result of the emergence of COVID-19 prompted the authorities to suggest adopting alternatives to traditional learning methods in emergencies to ensure that students are not left without studying and to prevent the epidemic from spreading.

The formal learning system with the help of electronic resources is known as e-learning. Whereas teaching can be inside (or outside) the classrooms, the use of computer technology and the Internet is the main component of e-learning (Aboagye et al. ( 2020 ). The traditional educational methods were replaced by e-learning when the COVID-19 virus appeared because social gatherings in educational institutions are considered an opportunity for the virus to spread. E-learning is the best option available to ensure that epidemics do not spread, as it guarantees spatial distancing despite the challenges and studied figures, which indicate that students are less likely to benefit from this type of education (Lizcano et al. ( 2020 ).

Information and communication technologies (ICTs) offer unique educational and training opportunities as they improve teaching and learning, and innovation and creativity for people and organizations. Furthermore, the use of ICT can promote the development of an educational policy that encourages creative and innovative educational institution environments (Abdullah et al. 2019 ; Altawaty et al. 2020 ; Selim, 2007 ). Therefore, attention is given widely to efforts and experiences related to this type of education. This technology is commonly used by most universities in several developing countries. In an educational environment, there are lots of learning-related processes involved, and great amounts of potential rich data are generated in educational institutions continuously in order to extract knowledge from those data for a better understanding of learning-related processes (Aljawarneh, 2020 ; Lara et al. 2020 ; Lizcano et al. 2020 ).

E-learning is playing a vital role in the existing educational setting, as it changes the entire education system and becomes one of the greatest preferred topics for academics (Samir et al. 2014 ). It is defined as the use of diverse kinds of ICT and electronic devices in teaching (Gaebel et al. ( 2014 ). Most students today want to study online and graduate from universities and colleges around the world, but they cannot go anywhere because they reside in isolated places without good communication services.

Because of e-learning, participants can save time and effort for living in distant places from universities where they are registered, so, many scholars support online courses (Ms & Toro, 2013 ).

Many users of e-learning platforms see that online learning helps ensure that e-learning can be easily managed, and the learner can easily access the teachers and teaching materials (Gautam, 2020 ; Mukhtar et al. 2020). It also helped reduce the effort and travel expenses and other expenses that accompany traditional learning. E-learning reduced significantly the administrative effort, preparation and lectures recording, attendance, and leaving classes. Teachers, as well as students, see that online learning methods encouraged pursuing lessons from anywhere and in difficult circumstances that prevent them from reaching universities and schools. The student becomes a self-directed learner and learns simultaneously and asynchronously at any time. However, there are many drawbacks of e-learning, the most important of which is getting knowledge only on a theoretical basis and when it comes to using everything that learners have learned without applied practical skills. The face-to-face learning experience is missing, which may interest many learners and educators. Other problems are related to the online assessments, which may be limited to objective questions. Issues related to the security of online learning programs and user reliability are among the challenges of e-learning in addition to other difficulties that are always related to the misuse of technology (Gautam, 2020 ; Mukhtar et al. 2020).

Web-based education, digital learning, interactive learning, computer-assisted teaching and internet-based learning are known as E-learning (Aljawarneh, 2020 ; Lara et al. 2020 ; Yengin et al. 2011 ). It is mainly a web-based education system that provides learners with information or expertise utilizing technology. The use of web-based technology for educational purposes has increased rapidly due to a drastic reduction in the cost of implementing these technologies. Nowadays, many universities have recognized the importance of E-learning as a core element of their learning system. Therefore, further research has been conducted to understand the difficulties, advantages, and challenges of e-learning in higher education. These issues have the potential to adversely affect instructors' quality in the delivery of educational material (Yengin et al. 2011 ).

Technology-based E-learning requires the use of the internet and other essential tools to generate educational materials, educate learners, and administer courses in an organization. E-learning is flexible when considering time, location, and health issues. It increases the effectiveness of knowledge and skills by enabling access to a massive amount of data, and enhances collaboration, and also strengthens learning-sustaining relationships. Although e-learning can enhance the quality of education, there is an argument about making E-learning materials available, which leads to improving learning outcomes only for specific types of collective evaluation. However, e-learning may result in the heavy use of certain websites. Moreover, it cannot support domains that require practical studies. The main drawback of using e-learning is the absence of crucial personal interactions, not only between students and teachers but also among fellow students (Somayeh et al. 2016 ). Compared to developed countries, it was found that developing countries face many challenges in applying e-learning, including poor internet connection, insufficient knowledge about the use of information and communication technology, and weak content development (Aung & Khaing, 2015 ). The provision of content such as video and advanced applications is still a new thing for many educators, even at the higher education level in developing countries (Aljawarneh, 2020 ; Lara et al. 2020 ; Lizcano et al. 2020 ).

This study aims to identify issues related to the use, advantages, disadvantages, and obstacles of e-learning programs in a public university by extrapolating the perspectives of students and educators who use this mode of learning in long-lasting unusual circumstances. The research population consisted of students and faculty members at the Faculty of IT at the University of Benghazi. Two types of questionnaires have been distributed to students and instructors. To achieve the expected results, four dimensions are defined, i.e., the extent to which E-learning is used and the benefits, drawbacks, and obstacles to the implementation of E-learning by the Faculty of IT. The descriptive-analytical method is used in the statistical analysis of the results. By evaluating the results, we have obtained promising findings that demonstrate some of the higher education sector's problems, obstacles, and advantages of using the E-learning method. Students believe that based on the study’s results, E-learning contributes to their learning. This reduces the instructor workload, however, and raises it for students. The teaching staff agrees that E-learning is beneficial in enhancing the skills of students, although it needs financial resources and the cost of implementing them is high. Despite the advantages of using E-learning, some of the obstacles to its implementation in Libya include the degradation of the Internet infrastructure that supports these education systems in Libya in general. The high cost of buying the electronic equipment needed and maintaining the equipment, which is unemployed.

The remainder of this paper is organized as follows. Section 2 gives some background and related work about E-learning. Section 3 describes the methodology. Statistical analysis is presented in Sect. 4. Section 5 explains the study outcomes. Finally, Sect. 6 discusses the conclusion of this work and provides some recommendations.

Related work

Several studies have addressed the opportunities and challenges associated with the transition to traditional learning instead of e-learning. One of the main reasons for faltering e-learning initiatives is the lack of well-preparedness for this experience.

A study that aims to examine student challenges about how to deal with e-learning in the outbreak of COVID-19 and to examine whether students are prepared to study online or not is presented in (Aboagye et al. 2020 ). The study concluded that a blended approach that combines traditional and e-teaching must be available for learners. Another study that aims to explore the e-learning process among students who are familiar with web-based technology to advance their self-study skills is described in (Radha et al. 2020 ). The study results show that e-learning has become popular among students in all educational institutions in the period of lockdown due to the COVID-19 pandemic.

A study that aims to investigate the characteristics, benefits, drawbacks and features that impact E-learning has been presented in (Ms & Toro, 2013 ). Some of the demographic features such as behaviors and cultural background impact student education in the E-learning domain. Therefore, for lecturers to design educational activities to make learning more effective, they should understand these features. The study is applied to students in Lebanon and England to assist instructors to understand what scholars expect from the learning management systems.

Analyzing the effectiveness of E-learning for students at the university level has been introduced in (Ali et al. 2018 ). A questionnaire was applied to a sample of 700 students, 94.9% of them are utilizing different e-learning techniques and tools. To measure the reliability and internal consistency of the factors, Cronbach’s alpha test is applied. To take out the variables and to calculate the factors loading in the study, the exploratory feature analysis is applied. The results demonstrate that students support that E-learning is easy to use, saves time, and affordable.

Various predictions of e-learning for educational purposes have been illustrated in (Samir et al. 2014 ). The study aims to show how to keep students motivated in e-learning. The evaluation of student motivations for online learning can be challenging because of the lack of face-to-face contact between learners and teachers. The study shows that one way to increase student’s motivation is by allowing them to complete an online assessment form on motivation. The study suggests five research hypotheses to be inspected to identify which hypothesis should be accepted and which should not.

The strength of the relationship between students’ motivation and e-learning is illustrated in (Harandi, 2015 ). Data was gathered from students at Tehran Alzahra University, and Pearson's correlation coefficient was utilized for data analysis. The outcomes of this study revealed that some points should be considered before using E-learning. However, this study was restricted to one culture, which can limit the generalization of its results.

The study described in (Oludare Jethro et al. 2012 ) showed that e-learning is a new atmosphere for scholars, as it illustrates how to make e-learning more effective in the educational field and the advantages of using e-learning. The outcome of the study showed that the students were willing to learn more with less social communication with other students or lecturers.

A study that aims to highlight and measure the four Critical Success Factors from student insights is described in (Selim, 2007 ). These factors are instructor and student characteristics, technology structure, and university support. The outcomes of the study showed that the instructor characteristics factor is the most critical one followed by IT infrastructure and university support in e-learning success. The least critical factor to the success of e-learning was student characteristics.

The work described in (GOYAL & S., 2012 ) has tried to emphasize the importance of e-learning in modern teaching and illustrates its advantages and disadvantages. Also, the comparison with Instructor Led Training (ILT) and the probability of applying E-learning instead of old classroom teaching was discussed. In addition, the study showed the major drawbacks of ILT in institutions and how using E-learning can assist in overcoming these problems.

The purpose of the study in (Gaebel et al. 2014 ) is to conduct a survey on the varieties of E-learning organizations, skills, and their anticipations for the forthcoming. Blended and online learning are taken into account. Some of the questions related to intra-institutional management, arrangements and services, and quality assurance. The outcomes of the survey showed that from 38 diverse countries and systems, there are 249 organizations broadly conceived the same causes for the increasing use of e-learning.

The study in (Yengin et al. 2011 ) illustrated that the most vital role in the e-learning design outlook is online lecturers. As a result, considering the issues impacting lecturers’ performance should be taken into the account. One of the features that impact the usability of the system and lecturers’ presentation is satisfaction. The results showed, to produce a simple model called the “E-learning Success Model for Instructors’ Satisfactions” that is related to public, logical and technical communications of instructors in the entire e-learning system, the features associated with teachers’ satisfaction in e-learning systems have been examined.

The comparison between different E-learning tools in terms of their goals, benefits and drawbacks are presented in (Aljawarneh et al. 2010 ). The comparison assists in providing when to use each tool. The outcomes show that instructors and students prefer to use MOODLE over Blackboard in the e-learning environment. One of the major challenges that face the E-learning environment is security issues since security is not combined into the active learning development process.

The effect of e-learning at the Payame Noor University of Hamedan, Iran on the innovation and material awareness of chemistry students was examined in (Zare et al. 2016 ). The research used a control group's pre-test/post-test experimental design. Data analysis findings using the independent t-test showed significantly better scores on calculated variables, information and innovation for the experimental group. Consequently, E-learning is beneficial for the acquisition of knowledge and innovation among chemistry students, and that a larger chance for E-learning should be given for broader audiences.

A study in (Arkorful & Abaidoo, 2015 ) aimed to explore the literature and provide the study with a theoretical context by reviewing some publications made by different academics and universities on the definition of E-learning, its use in education and learning in institutions of higher education. The general literature described the pros and cons of E-learning, which showed that it needs to be enforced in higher education for teachers, supervisors and students to experience the full advantages of acceptance and implementation.

Assessing the learning effectiveness of e-learning was studied in (Somayeh et al. 2016 ). This analysis study was conducted using the databases of Medline and CINAHL and the search engine of Google. The research used covered review articles and English language meta-analysis. 38 papers including journals, books, and websites are investigated and categorized from the results obtained. The general advantages of E-learning such as the promotion of learning and speed and process of learning due to individual needs were discussed. The study results indicated positive effects of E-learning on learning, so it is proposed that more use should be made of this education method, which needs the requisite grounds to be established.

It is important to focus on analyzing the learner and student characteristics and motivating students to ensure their involvement in e-learning. Also, it is necessary to focus on the impact and extent of teacher acceptance of e-learning. The age difference between the teachers and the students indicates that the teachers received most of their studies and teaching skills through traditional teaching and learning methods, which may make their acceptance of e-learning different from the student’s acceptance of modern methods of e-learning and education in general.

The methodology

The descriptive-analytical method was used for this study and the five-point Likert-scale range was calculated based on (1) Strongly disagreed, (2) Disagree, (3) Neutral, (4) Agree, and (5) Strongly agree, with the analysis of results using a statistical application called the Statistical Package for the Social Sciences (SPSS).

Study population

The study targets the sample society that includes teaching staff and undergraduate students of all departments in the IT Faculty at the University of Benghazi.

Study boundaries

• Scientific restrictions: Assessment of the extent of application of E-learning in higher education.
• Administrative Field: Faculty of IT, University of Benghazi, Libya.
• Period: The Year of 2020.
• Human Resources: Teaching staff and students in the faculty.

Study sample

The study involves two types of questionnaires to be prepared and developed: one questionnaire for students and another for instructors. The following details were obtained after the questionnaires were randomly distributed and collected individually. The study sample was selected based on the awareness of the size of the population:

• Student Questionnaire: The total number of distributed questionnaires was 140 copies, without invalid copies, and 5 copies were missing. Therefore, the copies being analyzed are 135.
• Teaching Staff Questionnaire: The total number of distributed questionnaires was 20 copies, while 20 legitimate copies were returned without invalid or missing copies.

Some of the demographic characteristics are shown in Table ​ Table1 1 .

Distribution of student study sample

Study dimensions

The study has emphasized four dimensions to achieve the expected results as follows:

• The extent of using E-learning in the Faculty of IT.
• Advantages of E-learning.
• Disadvantages of E-learning.
• Obstacles to implementing E-learning.

Statistical analysis

Data analysis.

The Means and Materiality statistical relations are used to analyze the results. By evaluating the findings, we gain crucial information based on these statistical relations according to the rank of inquiries as shown in Tables ​ Tables2 2 – 3 .

Descriptive statistics of students' perspective

Descriptive statistics of teaching staff perspective

The students' perspective

The analysis of data as a statistical relationship regarding the perspective of the students is shown in Table ​ Table2 2 .

Dimension 1: the extent of using E-learning in IT faculty.

Inquiries (6), (7) and (10) are of similar materiality and inquiry (6) is chosen because it has the lower standard deviation, which states that "E-learning technologies are used for scientific research purposes" with the materiality of 82.6% and a mean 4.13, while inquiry number (7), which states "Search engines are used to obtain curriculum needs". However, inquiry (2), which states that "the Internet is available to students at the faculty” has the lowest materiality of 40% and a mean 2.

Dimension 2: advantages of E-learning

Inquiry number (1) states that "E-learning contributes to raising your educational level" has the highest materiality of 88.2% and a mean of 4.41. However, inquiry number (7), which states that "E-learning reduces the burden because learning becomes a conversation between teaching staff and students instead of traditional learning", has the lowest materiality of 75.8% and a mean of 3.79.

Dimension 3: disadvantages of E-learning

Inquiries (5) and (6) are of similar materiality and inquiry number (5) is chosen because it has the lower standard deviation, which states that "E-learning reduces the burden of teaching staff and increases the burden of students” with the materiality of 75.4% and a mean of 3.77. Nevertheless, inquiry number (1), which states that "E-learning isolates you from the community by connecting you to your computer for long periods ", was the lowest materiality of 72.6% and a mean of 3.63.

Dimension 4: obstacles to E-learning

Inquiry number (3) states that "the lack of the Internet in the faculty to apply E-learning" has the highest materiality of 79% and a mean of 3.95. Yet, inquiries (4) and (5) are of similar materiality and inquiry number (5) has been chosen as it has the lower standard deviation, which notes that "Lack of experience of students with E-learning techniques” with the materiality of 71.8% and a mean of 3.59.

Teaching staff perspective

The analysis of data as a statistical relationship regarding the perspective of the teaching staff and the important analyzes of mean and materiality is given in Table ​ Table3 3

Dimension 1: the extent of using E-learning in IT faculty

Inquiry number (10), which was about that “Use email to communicate with colleagues”, has the highest materiality of 91% and a mean of 4.55. However, inquiry number (2), which states that "internet accessible always available to teaching staff in the faculty", has the least materiality as 41.8% and the mean is 2.09.

Dimension 2. advantages of E-learning

Inquiry number (4) which states that "E-learning contributes to increasing students' skills in using computers” has the highest materiality of 84.6% and a mean of 4.23. However, inquiry number (7), which states that "E-learning reduces the burden because learning becomes a conversation between teaching staff and students instead of traditional learning” with the lowest materiality of 68.2% and a mean of 3.41.

Inquiry number (6) which states that "E-learning needs financial capability compared to traditional education" has the maximum materiality of 79% and a mean of 3.95. Nevertheless, inquiry number (3), which reports that "students face a greater burden during the educational process while reducing the burden of teaching staff", has the lowest materiality of 58.2% and a mean of 2.91.

Inquiries (4) and (7) are of similar materiality and inquiry number (4) is chosen because it has the lower standard deviation, which states that "The lack of internet in the faculty to apply e-learning" with the materiality of 82.8% and a mean 4.14. Yet, inquiries (3) and (6) are of similar materiality and inquiry (6) is chosen, which states that "E-learning needs high costs" has the lowest materiality of 71.8% and a mean of 3.59.

Results and discussion

Students' perspective.

As shown in Table ​ Table4, 4 , we found the T-Test value = 8.733 and the P -Value = 0.00 to the extent of using E-learning during the pandemic. T-Test value = 22.86 and P -Value = 0.00 for the advantages of E-learning. The T-Test value = 12.786 and P -Value = 0.00 for the drawbacks of E-learning. The obstacles to E-learning in the last dimension are the T-Test value = 11.961 and the P -Value = 0.00. Accordingly, all T-Test values are greater than the T table value = 1.96. On the other side, all P -Values are smaller than the level of significance = 0.05. Thus, in each dimension of the four dimensions of the sample, there were statistically significant differences from the student's perspectives.

Statistical tests (students' perspective)

As shown in Table ​ Table5, 5 , the extent, to which E-learning is used are T-Test = 6.021 and P -Value = 0.00, the advantages of E-learning are T-Test = 9.015 and P -Value = 0.00, the disadvantages of E-learning are T-Test = 3.813 and P -Value = 0.001, and the obstacles to E-learning are T-Test = 6.505 and P -Value = 0.00 respectively. Depending on the T-Test values are higher than the T table value = 1.96, P -Values are less than the level of significance = 0.05. There were statistically significant differences from the teaching staff perspective in each dimension of the study's four dimensions.

Statistical tests (Teaching staff perspective)

The data analysis of the four dimensions is summarized as follows:

• The extent of the use of e-learning: the findings indicate that the student's approval of the use of e-learning and the teaching staff’s viewpoint is (Agreement), where the mean are (3.44) and (3.59) respectively.
• The advantages of e-learning: the results consider this dimension indicates the approval of the advantages of e-learning from the perspective of students and teaching staff was (Agreement), where the mean of the perspective of students was (4.13) and the mean of the perspective of the teaching staff was (3.99).
• The dimension that constituted the disadvantages of e-learning: This indicates that the student's acceptance drawbacks of e-learning are (Agreement) of the mean (3.78) and the teaching staff's opinion was (Undecided) of the mean (3.35).
• The factor defining obstacles to e-learning indicates that there were acceptance obstacles for e-learning from the perspective of both students and teaching staff (i.e., Agreement), where the mean was (3.75) and (3.82).

A comparison between the two perspectives

As shown in Fig.  1 , it is noticeable that the viewpoint of both the teaching staff and the students in all four dimensions of the study is identical. This demonstrates that they are almost standardized, with little variation in the third dimension of the data considered for the disadvantages of e-learning during the Covid-19 pandemic. This factor achieves the agreement from the teaching staff's perspective and is undecided from the students' perspective to achieve the agreement as to the outcomes.

A comparison of students' and teaching staff' perspectives

The study outcomes

The study outcomes could be summaries as follows:

Findings based on students' perspective

• The students believe that e-learning is used and that one of the most significant uses is a replica of the scientific method learned on electronic/multimedia forms.
• The students agree that e-learning is useful and that it helps them to be safe and improved their academic standards.
• The students claim that the introduction of e-learning is difficult and that the low-quality of internet services is the biggest obstacle to its application.
• The students demonstrate that there are limitations to e-learning and that the biggest downside is that it decreases the workload for teaching staff and raises the pressure on students.

Findings based on teaching staff perspective

• The teaching staff believes that e-learning is beneficial and that helping to develop students' technological skills is one of the most critical positive elements.
• The teaching staff agrees that the use of e-learning is common and that the possession of faculty members via e-mail and other e-services is the most significant use.
• The teaching staff agrees that there are barriers to the introduction of e-learning and that the high cost of its implementation is one of the main difficulties.
• The teaching staff accepts that e-learning has disadvantages and that the biggest downside is that, relative to traditional learning, it requires financial support.

Pedagogical aspects

Any e-learning strategy follows one of the commonly known learning theories, i.e., behaviorism, cognitivism, or constructivism (Mödritscher, 2006 ). Furthermore, each didactic strategy has a more or less strong impact on the factors that influence the learning process and the self-assessment of the characteristics of the learner. Therefore, based on what has been achieved through the opinions of teaching staff and students, we found that the certain characteristics of the learner, in particular, the motivation need to be analyzed. It is also necessary, as an appropriate pedagogical step, to choose an e-learning strategy that suits the characteristics of students and the electronic environment they are living in nowadays.

Conclusion and recommendations

This study aims to identify the major issues and challenges by extrapolating the opinions of students and faculty instructors on the use of e-learning systems in a public university during the Covid-19 pandemic. The study society sample consists of students and faculty members at the Faculty of IT, University of Benghazi. The descriptive-analytical approach has been applied with statistical analysis of the results. Two types of questionnaires have been distributed for students and instructors. Four dimensions have been determined to reach the expected results, i.e., the extent to which e-learning is used and the advantages, disadvantages and obstacles to the implementation of E-learning in the Faculty of IT. Learning and teaching in an electronic environment still provide many advantages, including, reducing expenses and affords. It was also a successful alternative for many students to return to study in educational institutions during the spread of the Covid-19 virus, despite facing many issues and challenges. By analyzing the results, we have achieved encouraging results to highlight some of the issues, challenges and benefits of using the e-learning system in the higher education sector.

Issues such as technical and financial support, training, improved working conditions, technological background, skills, copyright protections and professional development are always important in the implementation of e-learning in public universities. Based on the study results, students believe that e-learning contributes to their learning. However, it reduces the workload on faculty and increases it on students. The main obstacle to e-learning is the low-quality of Internet services in Libya during the pandemic period. Faculty members agree that e-learning is useful in increasing students' computer skills, although it requires significant financial resources. We can claim that it is important to highlight many of the recommendations, which could have a positive impact on the possibility of implementing e-learning. The university has to provide internet service to students and teaching staff members with enough computer devices to apply e-learning. A modern electronic library and dedicated classrooms with all types of equipment and tools needed are also necessary to apply e-learning instead of coming to the main campus. Conducting online training and seminars regularly is important, for teaching staff, in particular, to support the application of e-learning, in addition to constant attention to IT infrastructure and periodic maintenance of computers and supporting equipment. In addition to all of this, the role and importance of focusing on many things related to the characteristics of the learner, such as the characteristics of the student's background knowledge and how to motivate the students as one of the pedagogical impacts.

Biographies

is a professor, Software Engineering, at Benghazi University, Libya. He received his B.Sc. degree in Computer Science from Benghazi University, Libya, in 1995, and received his M.Sc. and Ph.D. degrees in 2004 and 2009, respectively from Northumbria University, UK. Dr. Maatuk returned to Omar Al Mukhtar University in Nov. 2009 as a lecturer. Dr. Maatuk joined Benghazi University in Aug. 2014, as an assistant professor in the Faculty of IT. He was appointed as Vice Dean of IT Faculty, Benghazi University in July 2015 to April 2019. Since May 2019, he is the Dean of IT Faculty, Benghazi University. Dr. Maatuk has published several articles in journals and conferences in the field of database systems and software engineering. His primary work and research interest spread over several research fields, e.g., object-based databases, database reengineering and software engineering.

is currently a lecturer in the Faculty of IT, University of Benghazi where she has been a faculty member since 2013. She received BSc in software engineering from Benghazi University and an MSc from the Academy of high studies, Libya in June 2010. From 2015-2018 she was the head of the information systems department at the IT Faculty, University of Benghazi. She has many publications and is presently workings on many more papers.

is a professor, Software Engineering, at the Jordan University of Science and Technology, Jordan. He holds a BSc degree in Computer Science from Jordan Yarmouk University, an MSc degree in Information Technology from Western Sydney University, and a Ph.D. in Software Engineering from Northumbria University-England. His research is centered on software engineering, web and network security, elearning, machine learning, cloud computing and ICT fields. Aljawarneh has presented at and been on the organizing committees for a number of international conferences and is a board member of the International Community for ACM, Jordan ACM Chapter, ACS, and IEEE. Multiple papers have been selected as “Best Papers” in conferences and journals. He is also the Associate Editor for IEEE ACCESS and Electrical and Computer Engineering.

is an associate professor, Computer Science, at Al-Balqa Applied University, Jordan. He received his BSc and MSc degrees in computer science and information technology from Yarmouk University in 1999 and 2002 respectively. In 2008 he obtained his Ph.D. degree in computer science from Saint Petersburg Electrotechnical State University-Russian Federation. Then, he joined the department of computer science at Prince Abdullah Bin Ghazi Faculty of ICT / Al-Balqa Applied University-Jordan as an assistant professor. He was appointed as Head of the department from July 2015 to July 2018. His research interests include machine learning, image processing, and computer vision, information retrieval, and optimization.

is an assistant professor, Computer Science at Hail University. She is head department for computer science and software engineering in the female branch, Hail University, Saudi Arabia. Her research interests include e-learning, recommendation systems, and information retrieval.

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Contributor Information

Abdelsalam M. Maatuk, Email: [email protected] .

Ebitisam K. Elberkawi, Email: [email protected] .

Shadi Aljawarneh, Email: oj.ude.tsuj@henrawajlaas .

Hasan Rashaideh, Email: oj.ude.uab@hediahsar .

Hadeel Alharbi, Email: [email protected] .

• Abdullah, F. M., Mohammed, A. A., Maatuk, A. M. and Elberkawi, E. K.: Application of electronic management system in governmental institutions: An empirical study on the Libyan civil registration. In Proceedings of 2nd International Conference on Data Science, E-Learning and Information Systems (DATA '19) . Dubai, UAE—December 02 - 05, 2019. ACM: New York, NY, USA, Article No. 19. Doi: 10.1145/3368691.3368710
• Aboagye E, Yawson JA, Appiah KN. COVID-19 and E-learning: The Challenges of Students in Tertiary Institutions. Social Education Research. 2020; 2 (1):1–8. doi: 10.37256/ser.212021422. [ CrossRef ] [ Google Scholar ]
• Ali M, Khaled Hossain SM, Ahmed T. Effectiveness of E-learning for university students: Evidence from Bangladesh. Asian Journal of Empirical Research. 2018; 8 (10):352–360. doi: 10.18488/journal.1007/2018.8.10/1007.10.352.360. [ CrossRef ] [ Google Scholar ]
• Aljawarneh SA. Reviewing and exploring innovative ubiquitous learning tools in higher education. Journal of Computing in Higher Education. 2020; 32 :57–73. doi: 10.1007/s12528-019-09207-0. [ CrossRef ] [ Google Scholar ]
• Aljawarneh, S., Muhsin, Z., Nsour, A., Alkhateeb, F. and AlMaghayreh, E. (2010). E-learning tools and technologies in education. In A Perspective. LINC Proceedings
• Aljawarneh, S., Maatuk, A. and Ali, A. 2012. Security Issues in Cloud Computing: A Perspective, In The Proceedings of the 9th International Conference on Electronics, Computer and Computation (ICECCO’12) , Ankara, Turkey.
• Altawaty, J. A., Benismail, A. and Maatuk, A. M.. 2020. Experts' opinion on the IT skills training needs among healthcare workers. In Proceedigs of International Conference on Engineering and Information Management Systems 2020 (ICEMIS'20) , Almaty, Kazakhstan, September 14–16. DOI: 10.1145/3410352.3410790
• Arkorful V, Abaidoo N. The role of e-learning, advantages and disadvantages of its adoption in higher education. International Journal of Instructional Technology and Distance Learning. 2015; 12 (1):29–42. [ Google Scholar ]
• Aung TN, Khaing SS. Challenges of implementing e-learning in developing countries: A review. In International Conference on Genetic and Evolutionary Computing . Springer, Cham; 2015. pp. 405–411.
• Benefits, E.-L. 2016. Nischal Guragain. February (2016).
• Eseroghene U, Ahmad A. The impact of E-learning on academic performance: Preliminary examination of King Khalid University. International Journal of Academic Research in Progressive Education and Development. 2018; 7 (71):83–96. doi: 10.6007/IJARPED/v7-i1/3903. [ CrossRef ] [ Google Scholar ]
• Gaebel, M., Kupriyanova, V., Morais, R. and Colucci, E. 2014. E-learning in European Higher Education Institutions November 2014 Results of a mapping survey conducted in October–December 2013 Michael Gaebel, Veronika Kupriyanova, Rita Morais, Elizabeth Colucci.
• Gaur P. Research trends in E-learning Poonam Gaur. Shanlax International Journal of Commerce. 2018; 6 (1):36–39. doi: 10.5281/zenodo.1438182. [ CrossRef ] [ Google Scholar ]
• Gautam, P. 2020. Advantages and disadvantages of online learning. In E-Learning Industry .
• Goyal S. E-learning: Future of education. Journal of Education and Learning (EduLearn). 2012; 6 (4):239. doi: 10.11591/edulearn.v6i4.168. [ CrossRef ] [ Google Scholar ]
• Harandi SR. Effects of e-learning on Students’ Motivation. Procedia—Social and Behavioral Sciences. 2015; 181 :423–430. doi: 10.1016/j.sbspro.2015.04.905. [ CrossRef ] [ Google Scholar ]
• Lara JA, Aljawarneh S, Pamplona S. Special issue on the current trends in E-learning Assessment. Journal of Computing in Higher Education. 2020; 32 :1–8. doi: 10.1007/s12528-019-09235-w. [ CrossRef ] [ Google Scholar ]
• Lizcano D, Lara JA, White B, et al. Blockchain-based approach to create a model of trust in open and ubiquitous higher education. Journal of Computing in Higher Education. 2020; 32 :109–134. doi: 10.1007/s12528-019-09209-y. [ CrossRef ] [ Google Scholar ]
• Mödritscher F. The Impact of an E-Learning Strategy on Pedagogical Aspects. International journal of instructional technology and distance learning. 2006; 3 (3):85–98. [ Google Scholar ]
• Mukhtar, K., Javed, K., Arooj, M., Sethi A. Advantages, limitations and recommendations for online learning during COVID-19 pandemic era. 2020;36 (COVID19-S4): COVID19-S27-S31. Doi: https://doi.org/10.12669/pjms.36.COVID19-S4.2785 [ PMC free article ] [ PubMed ]
• Ms P, Toro U. A review of literature on knowledge management using ICT. Higher Education. 2013; 4 (1):62–67. [ Google Scholar ]
• Oludare Jethro O, Moradeke Grace A, Kolawole Thomas A. E-learning and Its Effects on Teaching and Learning in a Global Age. International Journal of Academic Research in Business and Social Sciences. 2012; 2 (1):2222–6990. [ Google Scholar ]
• Radha R, Mahalakshmi K, Sathish V, Saravanakumar AR. E-learning during lockdown of Covid-19 pandemic: A Global Perspective. International Journal of Control and Automation. 2020; 13 (4):1088–1099. [ Google Scholar ]
• Samir M, El-Seoud A, Taj-Eddin IATF, Seddiek N, El-Khouly MM, Nosseir A. E-learning and Students’ Motivation: A Research Study on the Effect of E-learning on Higher Education. International Journal of Emerging Technologies in Learning. 2014; 9 (4):20–26. doi: 10.3991/ijet.v9i4.3465. [ CrossRef ] [ Google Scholar ]
• Selim HM. E-learning critical success factors: An exploratory investigation of student perceptions. International Journal of Technology Marketing. 2007; 2 (2):157. doi: 10.1504/ijtmkt.2007.014791. [ CrossRef ] [ Google Scholar ]
• Somayeh, M., Dehghani, M., Mozaffari, F., Ghasemnegad, S.M., Hakimi, H. and Samaneh, B. 2016. The effectiveness of E-learning in learning : A review of the literature Ph.D. of Nursing, Instructor, Department of nursing, Lahijan Branch, Islamic Azad University, Instructor, Department of Operating Room, Faculty of Medical Sciences, Birjand Un. International Journal of Medical Research & Health Sciences. 5(2): 86–91.
• Yengin I, Karahoca A, Karahoca D. An E-learning success model for instructors’ satisfaction in the perspective of interaction and usability outcomes. Procedia Computer Science. 2011; 3 (2011):1396–1403. doi: 10.1016/j.procs.2011.01.021. [ CrossRef ] [ Google Scholar ]
• Zare M, Sarikhani R, Salari M, Mansouri V. The impact of E-learning on university students’ academic achievement and creativity. Journal of Technical Education and Training. 2016; 8 (1):25–33. [ Google Scholar ]