research on online learning during covid 19

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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.

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research on online learning during covid 19

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Min Young Doo Kangwon National University http://orcid.org/0000-0003-3565-2159
Meina Zhu Wayne State University
Curtis J. Bonk Indiana University Bloomington

Since most schools and learners had no choice but to learn online during the pandemic, online learning became the mainstream learning mode rather than a substitute for traditional face-to-face learning. Given this enormous change in online learning, we conducted a systematic review of 191 of the most recent online learning studies published during the COVID-19 era. The systematic review results indicated that the themes regarding “courses and instructors” became popular during the pandemic, whereas most online learning research has focused on “learners” pre-COVID-19. Notably, the research topics “course and instructors” and “course technology” received more attention than prior to COVID-19. We found that “engagement” remained the most common research theme even after the pandemic. New research topics included parents, technology acceptance or adoption of online learning, and learners’ and instructors’ perceptions of online learning.

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

Affiliations.

1 Centre for Learning Analytics at Monash, Faculty of Information Technology Monash University Clayton VIC Australia.
2 Portfolio of the Deputy Vice-Chancellor (Education) Monash University Melbourne VIC Australia.
3 Department of Computer Science Jinan University Guangzhou China.
4 College of Cyber Security Jinan University Guangzhou China.
PMID: 34219755
PMCID: PMC8236971
DOI: 10.1111/bjet.13102

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

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

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Challenges of Online Learning During the COVID-19: What Can We Learn on Twitter?

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Wei Quan ORCID: orcid.org/0000-0003-2270-7376 8

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1485))

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International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability

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The COVID-19 pandemic is an ongoing global pandemic. With schools shut down abruptly in mid-March 2020, education has changed dramatically. With the phenomenal rise of online learning, teaching is undertaken remotely and on digital platforms, making schools, teachers, parents, and students face a steep learning curve. This unplanned and rapid move to online learning with little preparation results in a poor experience for everyone involved. Thus, this study explores how people perceive that online learning during the COVID-19 pandemic is challenging. We focus on tweets in English scraped from March to April 2020 with keywords related to the COVID-19 pandemic and online learning. We applied the latent Dirichlet allocation to discover the abstract topics that occur in the data collection. We analyzed representative tweets from the qualitative perspective to explore and augment quantitative findings. Our findings reveal that most challenges identified align with previous studies. We also shed light on several critical issues, including mental health, the digital divide, and cyberbullying. Future work includes investigating these critical issues to enhance teaching and learning practices in the post-digital era.

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Twitter and Micro-Blogging and Language Education

“pedagogical tweeting” in higher education: boon or bane.

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Quan, W. (2021). Challenges of Online Learning During the COVID-19: What Can We Learn on Twitter?. In: Guarda, T., Portela, F., Santos, M.F. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2021. Communications in Computer and Information Science, vol 1485. Springer, Cham. https://doi.org/10.1007/978-3-030-90241-4_40

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BRIEF RESEARCH REPORT article

Improving the training on creating a distance learning platform in higher education: evaluating their results.

1 Faculty Computer of Physics, Mathematics and Information Technology, Department of Computer Science, Kh. Dosmukhamedov Atyrau University, Atyrau, Kazakhstan
2 Faculty of Physics, Mathematics and Information Technology, Department of Physics and Technical Disciplines, Kh. Dosmukhamedov Atyrau University, Atyrau, Kazakhstan
3 Candidate of Pedagogical Sciences, Faculty of Physics, Mathematics and Information Technology, Department of Physics and Technical Disciplines, Kh. Dosmukhamedov Atyrau University, Atyrau, Kazakhstan
4 Faculty of Science and Technology, Department of Computer Science, Caspian State University of Technology and Engineering Named After Sh. Yessenov, Aktau, Kazakhstan

The COVID-19 pandemic has had a profound impact on the world, and one of the many changes it has brought about is the shift to distance learning in Kazakhstan’s universities. However, both teachers and students were ill-prepared for this sudden change. In some remote villages, lack of internet access meant that students had no access to online classes at all. To address this issue, universities had to rent foreign platforms, but these platforms had inadequate information security measures in place. To address these challenges, it is necessary to create a robust and secure independent platform for distance learning. This platform will be particularly important for training teachers of “Informatics” in the context of distance learning. This article presents methods and schemes for creating a distance learning platform specifically for training computer science teachers. The introduction of the “Education-Online Platform” discipline at the university has yielded promising results. As a result, a distance learning platform has been created, and this new discipline has been introduced and tested in the educational program of a particular university in the training of future specialists.

1 Introduction

The COVID-19 pandemic precipitated unprecedented disruptions in education systems globally. These disruptions highlighted systemic inequalities within educational landscapes worldwide as schools and other learning spaces closed, affecting learning continuity and exposing disparities. To maintain educational continuity amidst stringent public health mandates, governments and educational authorities pivoted towards distance learning solutions ( Meinck et al., 2022 ). In Kazakhstan, the pandemic’s impact was profound. The Ministry of Education and Science of Kazakhstan responded swiftly to mitigate interruptions by implementing a comprehensive strategy for distance learning across all educational levels—a crucial move in a country where education traditionally relies on in-person instruction ( Source of the Prime Minister of the Republic of Kazakhstan, 2020 ). This strategy included deploying digital platforms, enhancing internet connectivity, particularly in underserved regions, and training educators in online teaching methodologies. Despite these efforts, the transition revealed and sometimes exacerbated existing disparities in technology access and digital literacy, setting the stage for a deeper exploration of distance learning’s impacts, challenges, and advancements in Kazakhstan during the pandemic.

The necessity of distance learning during the pandemic introduced significant challenges, particularly around technological access and pedagogical adjustments. In rural areas, many students and teachers faced disadvantages due to poor internet connectivity and a lack of necessary devices ( Meinck et al., 2022 ). The rapid shift to online platforms required both students and teachers to adapt quickly to unfamiliar technologies, impacting the quality of education due to reduced interaction and challenges in assessing student engagement and conducting exams online. Moreover, the shift significantly affected students’ psychological and social well-being. Prolonged home confinement and the absence of physical classroom settings increased isolation, stress, and anxiety among students, particularly affecting those with pre-existing mental health issues ( American Psychological Association, 2020 ).

Despite these challenges, the period also catalyzed positive outcomes and innovations in the educational sector, setting the groundwork for potentially transformative changes in education delivery. The swift move to online platforms demonstrated the resilience of Kazakhstan’s educational system, while the necessity of distance learning accelerated the adoption of new teaching methods and technological tools. One such tool is DLPlatform, a dynamic, object-oriented distance learning environment designed to enhance the learning process by offering courses and materials to support education at all levels. It provides flexible access to students, teachers, and administrators while offering a comprehensive set of tools for teaching, assessment, and communication.

This study aims to comprehensively assess the implementation and impact of distance learning in Kazakhstan during the COVID-19 pandemic, with a particular focus on the role of platforms like DLPlatform in maintaining educational continuity. By evaluating the effectiveness of the measures taken and exploring the broader impacts on educational outcomes, student engagement, and equity in access to quality education, the study will propose targeted strategies to enhance distance learning systems. It will also explore opportunities for innovation to make distance learning more robust, inclusive, and adaptable to future needs.

2 Literature review

Distance learning refers to the method of delivering education to students who are not physically present in a traditional classroom setting. This educational approach has gained relevance due to its ability to overcome geographical barriers and provide flexible learning options. Historically, distance learning began with correspondence courses in the 19th century and has evolved significantly with the advent of the internet and digital technologies, which have transformed how educational content is delivered and consumed ( Masalimova et al., 2022 ).

During the COVID-19 pandemic, educational institutions around the world rapidly transitioned to online learning, facing both challenges and opportunities. In Iraq, Dijlah University quickly adopted platforms like Google Classroom and Zoom, navigating issues related to accessibility and the digital divide, yet benefiting from increased schedule flexibility and potential educational enhancements ( Khudhair et al., 2023 ). Similarly, the South Eastern University of Sri Lanka struggled with online delivery and technological access but implemented strategies such as virtual meetings and Moodle-based systems to maintain educational activities ( Rameez et al., 2020 ). Concurrently, a study in China highlighted significant reductions in carbon emissions due to decreased transportation and lower campus energy use, advocating for the continued integration of online education to sustain environmental benefits ( Yin et al., 2022 ). These experiences underline the importance of strategic planning and the potential of hybrid learning models to address future educational disruptions while considering their broader environmental and social impacts ( Techakosit and Nilsook, 2018 ; Petrov and Atanasova, 2020 ).

The shift to distance learning has introduced several significant challenges. Technological limitations, notably insufficient access to reliable internet and necessary digital devices, have been substantial obstacles, particularly in less affluent areas ( Verulava et al., 2023 ). From a pedagogical perspective, instructors have encountered difficulties in adapting their teaching styles to suit an online format, which has been evident across various disciplines including STEM and language education ( Al-Assaf, 2021 ; Khaled and Malkawi, 2023 ). Furthermore, the move to online learning platforms has brought about social and psychological challenges, with increased reports of student isolation adversely affecting mental health and academic engagement ( El Refae et al., 2021 ). These issues underscore the complexities involved in transitioning to and implementing effective distance learning systems.

In response to the COVID-19 pandemic, Kazakhstan implemented rapid policy changes to support distance learning, facilitated by the Ministry of Education and Science. Despite the deployment of digital platforms like Zoom and Microsoft Teams and access to extensive electronic resources as noted in ( Khibina et al., 2023 ), challenges such as digital divides and inadequate technical resources persisted. Studies ( Movkebayeva et al., 2020 ; Bokayev et al., 2021 ) highlighted issues like low engagement levels among students with disabilities and varied satisfaction levels among parents, which correlated positively with parental age and income. Further research ( Khibina et al., 2022 ) on the stress factors for female educators emphasized the importance of adequate technological resources and supportive organizational climates in mitigating stress. These findings suggest critical areas for improvement, including enhancing digital infrastructure, providing comprehensive training for educators, and ensuring all students have equitable access to learning tools, to bolster the efficacy and quality of distance education in Kazakhstan.

There are several papers that underscore the critical integration of technological solutions and innovations in distance learning across various educational contexts, emphasizing the necessity and effectiveness of digital tools and strategies in enhancing learning outcomes. For instance, in Kazakhstan, digital platforms are essential in training foreign language teachers, equipping them with the digital competencies needed to utilize contemporary educational technologies effectively ( Shakiyeva et al., 2023 ). Similarly, studies on primary school teachers highlight the role of technology in fostering e-learning, with platforms like Moodle and Google Meet being pivotal in facilitating effective remote teaching and learning experiences ( Stambekova et al., 2021 ). Additionally, in higher education, the adoption of advanced technological innovations such as virtual and augmented reality, and interactive multimedia during the COVID-19 pandemic showcases a significant shift towards more interactive educational practices ( Li et al., 2023 ). Technical education’s adaptation to distance learning also highlights the innovative use of digital tools to simulate interactive learning environments, addressing the unique challenges of technical subjects ( Yi et al., 2020 ). These studies collectively demonstrate how technological advancements are critical in overcoming geographical and institutional barriers, heralding a transformative shift towards more digitally accessible and effective educational environments globally.

Across multiple studies exploring distance learning, both students and teachers have shared diverse experiences and insights reflecting the complexities of transitioning to and operating within virtual educational environments. Teachers frequently report needing substantial efforts and new skills to manage effective online teaching, often facing challenges such as diminished student engagement and interaction difficulties ( Almsaiden and Hussein, 2021 ). The swift shift to online platforms during the COVID-19 pandemic exacerbated these issues, as many educators found themselves unprepared for the sudden reliance on digital tools ( Vorlíček et al., 2023 ). Students, on the other hand, appreciate the flexibility that distance learning offers, particularly when balancing studies with personal or professional obligations. However, they also encounter significant challenges, such as technical issues, reduced sense of community, and concerns about the inclusivity and accessibility of virtual learning environments, especially for peers with special needs ( Vorlíček et al., 2023 ). Both groups—students and teachers—acknowledge the potential of online learning to maintain educational quality during disruptions but emphasize the need for enhanced technological infrastructure, better training in digital competencies, and more supportive policies to address inclusivity and engagement effectively ( Liu et al., 2020 ; Masalimova et al., 2021 ).

In connection with the didactic potential of technology for creating a distance learning platform and its integrated use in the educational process, it is necessary to improve the development of educational and methodological materials, educational programs, and new types of textbooks and teaching aids based on the active use of modern computer technologies. Until recently, digital education was almost not considered the main subject of research due to the complexity and multi-meaningfulness of the topic. However, a number of foreign and domestic scientific studies provide opportunities to reveal the essence of digital education ( Sadvakassova and Serik, 2017 ; Uaidullakyzy et al., 2022 ; Kuanbayeva et al., 2024 ). Additionally, as a result of the analysis of the educational experience of higher educational institutions on the use of distance learning platforms in the training of future computer science teachers of the country, surveys have shown that the future specialists have a high level of information competence and a wide range of modern information and communication technologies, partly due to the internet services included in the platform ( Abirov et al., 2022 ). However, it has also been found that many students and undergraduates lack methodological training in creating digital platforms on their own, despite the significant potential of self-use of various online educational platforms. Thus, it is important for each teacher to have information literacy and the ability to use and create distance education platforms for the organization of comfortable and productive work in a digital environment. We believe that the development of distance education platforms should provide for the training of specialists not only on the part of the state order but also on the part of universities that are able to work in the direction of developing distance learning platforms in accordance with the needs of universities in training teachers. Therefore, the purpose of our research work is to theoretically justify and practically implement improving the training of future computer science teachers to create a modern distance learning platform. If the training of future computer science teachers in the educational process of higher education is based on the ability to create a distance learning platform, it would meet modern requirements since the creation and use of modern digital platforms require educated and qualified specialists.

While extensive studies have addressed the broader impacts and adaptations of distance learning, there remains a significant gap in specific, methodological research aimed at preparing future computer science teachers to effectively use and develop these platforms. Existing literature largely overlooks the detailed training processes and outcomes necessary to equip these educators with the skills to not only use but also innovate within distance learning environments. Further research is needed to evaluate and refine targeted training methodologies that can enhance the readiness of future computer science teachers, ensuring they are adept at navigating and contributing to the evolution of educational technologies.

3 Methodology

3.1 background of dlplatform.

DLPlatform is a dynamic, object-oriented distance learning environment designed to enhance the learning process. Developed specifically for universities, it integrates distance learning technologies to offer a comprehensive suite of courses and educational materials. The platform provides four levels of access—teacher, curator, student, and administrator—ensuring a structured and well-defined learning environment. Figure 1 illustrates the system’s architecture, outlining the different modules and how they interact to form a comprehensive distance learning platform.

Figure 1 . Model for creating a distance learning platform.

After authorization, students gain access to courses, certificates, and a personal information page. Teachers have access to existing courses, can add new ones, and can evaluate students to track their progress. Figure 2 showcases the user-friendly DLPlatform interface, illustrating its graphical layout, available course categories, and intuitive navigation.

Figure 2 . DL platform.

The platform is web-based and built using open-source technology for flexibility and customization. It’s accessible from anywhere with an internet connection, scalable to millions of students, and available in Kazakh, Russian, and English. DLPlatform offers a wide range of tools for course creation, outcome evaluation, and communication between students and teachers, providing an efficient environment for the development of educational materials. It is designed with security in mind to protect against unauthorized access and data misuse.

The platform supports an extensive list of user roles, including administrators, teachers, and students, and its intuitive interface simplifies usage for those with minimal technical knowledge. DLPlatform’s comprehensive toolset allows for high-quality course creation and a full-fledged educational process in an electronic environment. Developed by Rakhmetov Maxot and copyrighted by the National Institute of Property of the Republic of Kazakhstan, DLPlatform is positioned as an effective, high-quality solution for organizing eLearning.

3.2 Study design and objectives

The study aimed to evaluate the readiness of future computer science teachers to work with DLPlatform. The primary objective was to assess the preparedness of undergraduate students majoring in Informatics from three universities in Kazakhstan. The research focused on evaluating their ability to work with DLPlatform through a controlled experiment. The universities were divided into experimental and control groups based on the elective courses offered in their educational programs. To complement the experiment, a survey was conducted to assess the attitudes and proficiency levels of computer science teachers and students in using distance learning platforms. The survey was designed to capture their experience with online teaching, their skill set in developing and using distance learning platforms, and their readiness to integrate them into their teaching practices.

3.3 Participant and sampling

The participants comprised students from L. N. Gumilyov Eurasian National University, Kh. Dosmukhamedov Atyrau University, Kazakh National Pedagogical University, and Caspian University of Technology and Engineering. The experimental group included students from the first two universities, while the latter two universities formed the control group. Table 1 provides a detailed breakdown of the student distribution across the two academic years. The students were selected based on their elective disciplines and the relevance of these courses to their future profession.

Table 1 . Universities involved in the study.

3.4 Formation experiment and data collection

The formation experiment aimed to evaluate a methodology for preparing future computer science teachers to create distance learning platforms. Conducted during the 2021–2022 and 2022–2023 academic years, the experiment involved 149 students majoring in Informatics from different universities in Kazakhstan. The students were divided into an experimental group of 72 and a control group of 77. The experimental group was enrolled in courses titled “Digital Educational Resources in the Subject Area” and “Educational Online Platforms,” which included independent assignments focused on creating distance learning platforms. The control group did not participate in these courses.

To evaluate the readiness of the participants to work with DLPlatform, a structured assessment was conducted using three levels—high, medium, and low. The assessment criteria were designed to gauge the participants’ pedagogical skills, understanding of distance learning platforms, and ability to develop such platforms in their professional capacity. Surveys and practical tests were used to collect data on these criteria.

The effectiveness of the methodology used in the formation experiment was rigorously assessed through various methods, including observation, surveys, testing, analysis, and generalization. To determine the statistical significance of the results, the chi-square criterion was employed.

3.5 Evaluation criteria

The study evaluated the readiness of future computer science teachers to work with the distance learning platform on a scale of assessment consisting of three levels: high, medium, and low. This assessment helped determine the effectiveness of the distance learning platform in preparing future computer science teachers. The evaluation criteria for the three levels were:

Higher level: The future computer science teacher is characterized by a stable development of training, is defined as a subject of professional activity, pedagogically capable of working with a distance learning platform, and possesses high-level pedagogical skills with the necessary knowledge.

Medium level: The future computer science teacher is pedagogically capable of working with the distance learning platform, but considers the lack and partial formation of some components in the structure of the training.

Low level: The future computer science teacher’s training components are not formed in working with the distance learning platform.

These components were used to monitor the quality of knowledge of the future computer science teacher, determine the effectiveness and assimilation of new material, and lead each teacher to self-control over their future professional activities. The results of the experiment served as a basis for further research on the possibilities of forming the readiness of future computer science teachers to develop a distance learning platform through a training experiment.

3.6 Analysis and implications

The results were analyzed to understand the effectiveness of DLPlatform in preparing future teachers. By categorizing the students based on the evaluation criteria, the study aimed to highlight areas for improvement in teacher training programs and suggest ways to enhance the adoption of distance learning platforms in higher education.

4.1 Survey findings

The survey revealed that 96% of the surveyed computer science teachers had a positive attitude towards distance learning platforms and were ready to incorporate them into their teaching. However, it was observed that many school teachers lack the knowledge and skills required to create and utilize distance learning platforms in computer science instruction. Similarly, within higher education, the study affirmed the following findings:

• Prospective teachers do not incorporate distance learning platforms into their classroom activities.

• Teachers do not assign tasks related to the creation of distance learning platform components.

• Future computer science teachers are ill-prepared to develop distance learning platforms.

4.2 The formation experiment

Table 2 shows the indicators before and after the formation experiment. The data shows that before the experiment, both the experimental and control groups had a low level of training and were not ready to create distance learning platforms. After the experiment, there was a significant improvement in the experimental group, with a decrease in the number of students who could not create a distance learning platform and an increase in the number of students who were highly prepared to create one.

Table 2 . Indicators before and after the experiment.

Figure 3 shows the changes in knowledge level before and after the formation experiment. The data clearly shows an increase in the number of students with a high level of knowledge, indicating the effectiveness of the methodology used in the experiment.

Figure 3 . Indicators before and after the formation experiment.

4.3 Statistical significance of results

The chi-square test is a statistical measure used to determine if there is a significant association between two categorical variables. In our case, the chi-square test was applied to assess the differences between the characteristics of the experimental and control groups before and after the experiment. The formula for calculating the chi-square value is as follows:

χ 2 is the chi-square value,

O i is the observed frequency,

E i is the expected frequency.

The critical value of the chi-square criterion is determined based on the significance level (α) chosen for the test. In this study, a significance level of 0.05 (α = 0.05) was selected, indicating a 95% confidence level. The critical value for α = 0.05 is 5.99. Before the experiment, the observed and expected frequencies were calculated for each group, and the chi-square value was determined using the formula. The resulting chi-square value was found to be 0.346. This indicates that there were no significant differences between the characteristics of the experimental and control groups at the start of the experiment. After the experiment, the same calculations were performed, and the chi-square value was found to be 16.66. This value exceeded the critical value of 5.99, indicating a significant difference between the experimental and control groups after the intervention. The higher chi-square value suggests that the methodology used in the formation experiment had a substantial impact on the knowledge and skills of future computer science teachers in developing distance learning platforms. The calculated chi-square value demonstrates that the differences observed between the experimental and control groups are statistically reliable with a confidence level of 95%. This statistical significance strengthens the conclusion that the methodology employed in the experiment effectively enhanced the readiness of future teachers in creating and utilizing distance learning platforms.

5 Conclusion

This study contributes significantly to understanding the adoption and impact of distance learning platforms in higher education, particularly in preparing future computer science teachers. By exploring the effectiveness of a specific training methodology, it advances theoretical frameworks regarding teacher readiness in a digital era. The findings emphasize that targeted training, involving courses and practical assignments, enhances future teachers’ abilities to integrate digital tools into their educational practices. This aligns with the growing body of literature advocating for tailored approaches to teacher training in digital environments.

Key results show that structured training led to a significant increase in the proficiency of future computer science teachers in developing and using distance learning platforms. The chi-square analysis confirmed the statistical significance of these improvements, highlighting the importance of integrating such methodologies into teacher education. These results underscore the necessity of adapting teacher training programs to include specific training for digital competencies, reinforcing the strategic importance of such skills in the evolving educational landscape.

The practical implications of these findings are far-reaching for universities and policymakers. The study suggests that embedding courses focused on digital resources and online platforms in teacher training programs can significantly improve teachers’ readiness to develop and use distance learning platforms. Such improvements will not only enhance the quality of online education but also help bridge the digital divide in underserved regions.

However, the study’s limitations, such as its focus on specific universities in Kazakhstan, highlight the need for broader research to confirm the generalizability of the findings. Future research should evaluate the long-term impact of this training methodology and explore additional strategies to improve teacher readiness for digital platforms across various educational contexts.

This research adds valuable new insights to the field by demonstrating the tangible benefits of specific teacher training in digital environments. It calls on academics and practitioners alike to prioritize the development of comprehensive training programs that prepare educators for the future of online education. Through actionable and engaged scholarship, the academic community can effectively enhance the readiness of educators and ultimately improve the quality of digital education worldwide.

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 author.

Ethics statement

The research procedures involving human participants were approved by the Ethics Committee of Kh. Dosmukhamedov Atyrau University. Written informed consent for participation in the study was obtained from all individual participants.

Author contributions

MR: Writing – original draft, Writing – review & editing, Funding acquisition, Investigation. BK: Methodology, Writing – original draft. GS: Software, Writing – original draft. GZ: Formal Analysis, Writing – original draft. EA: Supervision, Validation, Visualization, Writing – original draft.

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

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.

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

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PubMed Abstract | Crossref Full Text | Google Scholar

Keywords: digital learning, PLATFORM, virtual educational institution, virtual reality, immersive learning, distance learning

Citation: Rakhmetov M, Kuanbayeva B, Saltanova G, Zhusupkalieva G and Abdykerimova E (2024) Improving the training on creating a distance learning platform in higher education: evaluating their results. Front. Educ . 9:1372002. doi: 10.3389/feduc.2024.1372002

Received: 17 January 2024; Accepted: 05 July 2024; Published: 17 July 2024.

Reviewed by:

Copyright © 2024 Rakhmetov, Kuanbayeva, Saltanova, Zhusupkalieva and Abdykerimova. 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: Maxot Rakhmetov, [email protected]

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

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Transition to online learning during the COVID-19 pandemic

David john lemay.

a McGill University, Canada

Paul Bazelais

b John Abbott College, Canada

Tenzin Doleck

c Simon Fraser University, Canada

With the new pandemic reality that has beset us, teaching and learning activities have been thrust online. While much research has explored student perceptions of online and distance learning, none has had a social laboratory to study the effects of an enforced transition on student perceptions of online learning.

We surveyed students about their perceptions of online learning before and after the transition to online learning. As student perceptions are influenced by a range of contextual and institutional factors beyond the classroom, we expected that students would be overall sanguine to the project given that access, technology integration, and family and government support during the pandemic shutdown would mitigate the negative consequences.

Students overall reported positive academic outcomes. However, students reported increased stress and anxiety and difficulties concentrating, suggesting that the obstacles to fully online learning were not only technological and instructional challenges but also social and affective challenges of isolation and social distancing.

Our analysis shows that the specific context of the pandemic disrupted more than normal teaching and learning activities. Whereas students generally responded positively to the transition, their reluctance to continue learning online and the added stress and workload show the limits of this large scale social experiment. In addition to the technical and pedagogical dimensions, successfully supporting students in online learning environments will require that teachers and educational technologists attend to the social and affective dimensions of online learning as well.

1. Introduction

The novel coronavirus disease 2019 or Covid-19 ( Fauci, Lane, & Redfield, 2020 ) or SARS-CoV-2 ( Velavan & Meyer, 2020 ) pandemic outbreak has disrupted and changed how we socialize, work, and learn ( Brynjolfsson et al., 2020 ; Daniel, 2020 ; Haase, Cosco, Kervin, Riadi, & O'Connell, 2021 ; Gonzalez et al., 2020 ). Since the pandemic began, much human activity has transitioned online ( Donthu & Gustafsson, 2020 ; Kramer & Kramer, 2020 ). The profound effects of the pandemic are being especially felt in education ( Marinoni, Land, & Jensen, 2020 ; Schleicher, 2020 ; Stambough et al., 2020 ). For education, the pandemic is both a challenge ( Daniel, 2020 ) and an opportunity ( Azorín, 2020 ). Schools have closed to mitigate the spread of COVID-19 ( Pokhrel & Chhetri, 2021 ) and the Covid-19 pandemic has disrupted traditional models of learning ( Lemay and Doleck, 2020 ) and precipitated a move to online teaching and learning activities ( Lemay, Doleck, & Bazelais, 2021 ). In the wake of the pandemic, most institutions of higher education have had to reconsider ways of teaching and assessment ( García-Peñalvo, Corell, Abella-Garcí ).

Regarding teaching, this abrupt transition “has led to significantly intensified workloads for staff as they work to not only move teaching content and materials into the online space, but also become sufficiently adept in navigating the requisite software” ( Allen et al., 2020 , Allen et al., 2020 , p. 233). Likewise, students faced difficulties and challenges adapting to the abrupt and unplanned shift to online learning ( Baticulon et al., 2021 ). In fact, it is not surprising that we know little about students’ readiness for real-time online learning ( Tang et al., 2021 ). Previous research on online teaching and learning has generally shown that transitions are usually voluntary and/or planned; however, emergency transitions, such as the one brought upon by the Covid-19 pandemic, have relatively little body of knowledge ( García-Peñalvo et al., 2021 , García-Peñalvo et al., 2021 ; Iglesias-Pradas, Hernández-García, Chaparro-Peláez, & Prieto, 2021 ; Lemay et al., 2021 ). Considering this significant upheaval, we wanted to explore how these changes might influence student perceptions of online learning.

In this context it is important to understand student perceptions, to be able to develop successful interventions and correct deficits in learning. Examining student perceptions of online learning through the transition helps us to understand the limits and the potential of this mode of distance learning, and help to anticipate and adapt the effects of this sudden transition to online instruction.

2. Background

According to Kauffman (2015) , “students perceive online courses differently than traditional courses” (p.1). In a highly cited paper Song, Singleton, Hill, and Koh (2004) conducted a large-scale study of graduate student perceptions of online learning and found a mix of facilitating and discouraging factors. Students felt that course design was an important factor that distinguished successful from unsuccessful online learning experiences. A review by Nora and Snyder (2008) documented mixed evidence for improved learning outcomes for online learning over traditional classes as technical problems were a significant impediment, including user proficiency with technology but also time management and maintaining interest and motivation online. It is unclear to what extent a forced and precipitated transition to online learning might affect perceptions of online learning.

Some research suggests that students may perform differently across different modalities, and some may even perform better in online learning environments ( Cole et al., 2017 ; Fendler, Ruff, & Shrikhandle, 2018 ; Kurucay & Inan, 2017 ). Cole, Lennon, and Weber (2019) investigated the relationship between student perceptions of online learning practices, social belonging, and the learning climate, controlling for age and gender. They argue that successful online learning addresses the social dimension, to counter the absence and overcome the distance. They concluded that a successful fully online learning experience necessitates exploiting active learning strategies to create opportunities for connection and exchange. Indeed, there is research reporting that students are more appreciative of active learning strategies in online learning environments (Cole, Lennon, and Weber, 2019; Gómez-Rey, Barbera, & Fernández-Navarro, 2017 ; Koohang, Paliszkiewicz, Klein, & Nord, 2016 ).

It is acknowledged in the literature that online learning presents a learning environment that is distinct from face-to-face or classroom learning environments ( Bazelais, Doleck, & Lemay, 2018 ). Students generally have favorable perceptions of online learning although they have reservations around technological proficiency and adequate course designs ( Song et al., 2004 ). According to a recent review by Pokhrel and Chhetri (2021) , “broadly identified challenges with e-learning are accessibility, affordability, flexibility, learning pedagogy, life-long learning and educational policy” (p. 4). Anecdotally, many distance learning programs are successful and students have thrived when they have been adequately supported. However, the unequal social outcomes and deprivations of the pandemic means many students have been deprived of adequate educational support ( Flack, Walker, Bickerstaff, & Margetts, 2020 ). Many educationalists are lamenting the lost years of the pandemic and the lamentable effects for youth social development ( Allen, Mahamed, & Williams, 2020 ). Thus, it is not clear how an enforced transition to remote teaching might influence student perceptions of online learning.

3. Purpose of the study

We sought to explore how the pandemic influenced student perceptions of online learning.

3.1. Research question

We asked “How did the pandemic and the unprecedented institution wide transition to remote delivery of instruction influence student perceptions of online learning in terms of access, engagement and academic progress?

4.1. Design

We employed a cross-sectional survey-based design to gauge student perceptions of online learning before, during, and after the transition to remote instruction. We surveyed students at a college in Northeastern North America about their experience of the transition to online learning. Then, we compared and contrasted our findings with the empirical research to describe how student perceptions of online learning were influenced by the wholesale transition to online learning.

4.2. Procedure and participants

Participants completed an online survey during the middle months of 2020.Survey responses were collected online. Surveys were completed on a voluntary basis and were completely anonymous. The survey included questions related to the effects of transition to online learning during the COVID-19 pandemic.A total of N = 149 students from a pre-university science program at an English Collège d'enseignement général et professionnel (CEGEP; for a review, see Bazelais, Lemay, & Doleck, 2016 ) participated in the study. A breakdown of the participant characteristics is provided in Table 1 .

General characteristics of the respondents.

Student Characteristics					%
Age			18.17	0.94
Gender	Female	81			54.36
	Male	66			44.30
	Other	2			1.34

4.3. Measures

We used items from an existing questionnaire ( Motz et al., 2020 ) adapted for the present context, focusing on student perceptions after the transition and assessing student reactions, and their perceptions of the impact of the transition on academic outcomes. We supplemented the Likert scale items with the following open-ended questions:

1. What one thing could the College have done to improve your experience after the transition to online instruction?
2. Reflecting on your transition to online instruction, what was the most negative outcome?
3. Reflecting on your transition to online instruction, what was the most positive outcome?
4. What one thing could your instructors have done to improve your experience after the transition to online instruction?
5. Is there anything else that you feel is important regarding your experience with the transition to online learning that you would like to share?

5. Analysis

5.1. background.

We summarized the results and calculated descriptive statistics. We analyzed tendencies to provide a holistic picture of student perceptions of online throughout the transition. Open-ended questions were summarized using thematic analysis to inductively group answers into categories.

Of the 149 students surveyed, 45 were employed prior to COVID-19, but then unemployed due to COVID-19. 28 continued to be employed throughout the Winter 2020 semester. 76 reported no employment during this period. This is unsurprising as nearly all the students surveyed were still living with their parents, save one student who reported living alone. Fig. 1 presents student living situations prior to the pandemic. Students reported difficulty finding adequate study space, due to interruptions from too many people, not having space, and too much noise. Fig. 4 shows that 95% had not taken an online course prior to the pandemic. When the Winter 2020 semester began, only nine students were taking courses fully online or in a blended learning situation (a hybrid model, mixing both online and face-to-face components). 90% of students were not taking any online courses. However, over a third were registered in at least one blended learning course. Please see Table 2 for the frequency of online, blended, and face-to-face courses reported by students. Virtually none of the students surveyed had taken fully online courses prior to the wholesale transition to remote instruction.

Student living situation prior to pandemic.

Winter course registration by delivery model prior to the transition to online learning.

	0	1	2	3	4	5	6
Originally 100% online class (es)	134	2	1	0	2	2	8
Originally hybrid (blended-learning) class (es)	94	48	3	0	1	2	1
Originally face-to-face class (es)	9	0	2	8	13	29	88

* fully-online courses (100% online), hybrid courses (with some face-to-face and some online sessions), or face-to-face courses (with all sessions physically face-to-face).

Student experience of online learning prior to pandemic.

In Table 3 , we summarize student responses regarding levels of food or monetary insecurity in the earlier months of the pandemic. Whereas the pandemic was undeniably disruptive in everyone's lives, some were hit harder than others. Even for a relatively affluent population, food insecurity was an issue for a few. Signaling that despite governmental measures, some students were still facing hardships at home.

Student levels of food and money insecurity.

	Often true	Sometimes true	Never true
Within the past month, I worried whether my food would run out before I got money to buy more.	1	8	140
Within the past month, the food I bought just did not last and I did not have money to get more.	0	5	144

5.2. Technology details

In Table 4 , we summarize student responses concerning their access to technology and their preparation for online learning. Fig. 2 illustrates students’ principal mode of connecting to online learning resources. Over 85% had access to the necessary computer equipment and Internet access, though 10% struggled with Internet connectivity and 5% did not have adequate computer hardware. Whereas the majority felt sufficiently prepared, 15% were unprepared which increased to a worrying 40% when accounting for those who were mitigated. Hence, material issues of technology and access were not important factors for the majority of students surveyed.

Student access to technology and preparation for online learning.

Statement	Strongly Disagree (%)	Disagree (%)	Neither either or disagree (%)	Agree (%)	Strongly Agree (%)	Mean
I had adequate access to the internet connectivity necessary to participate in online instruction.	5.37	4.70	4.70	32.21	53.02	4.23	1.097
I had adequate access to computer hardware necessary to participate in online instruction.	3.36	2.68	2.01	26.85	65.10	4.48	0.927
I was prepared for online instruction.	6.04	9.40	24.83	34.23	25.50	3.64	1.140

Student internet connected device for online learning.

As can be seen in Fig. 2 , over 85% reported using their own laptop. Surprisingly, more than half reported working on their smartphone at least part of the time. Given that a smartphone has become a necessary part of life, for many it is their only connected device or their only device at all. If one has to choose between a smartphone or a laptop, the laptop is the greater luxury and often the loser in the cost benefit analysis. Still a few reported having to share equipment to participate in online learning activities. This is concerning since it is largely accepted as a matter of course that everyone is connected these days. Yet for a minority, access remains uncertain or variable. Virtually everyone surveyed reported having an internet connection at home though at least 10% reported poor Internet connectivity. In Fig. 3 , we observe that virtually all students connected using their residence's internet services.

Primary method of connecting to the internet.

5.3. Engagement

This section of the survey addresses how the shift to online instruction impacted student engagement at college and in their courses . As can be observed in Table 5 , Table 6 , the transition did lead to disaffection for many. Although most continued to identify as students, their academic goals became less important. 25% felt they were unsuccessful as a result of the transition to online learning even though 50% still found success in the transition.

Consequence of transition on student engagement in college and in class.

Statement	Strongly Disagree (%)	Disagree (%)	Neither either or disagree (%)	Agree (%)	Strongly Agree (%)	Mean
I still found it easy to think of myself as a college student.	6.76	13.51	28.38	33.11	18.24	3.43	1.14
I became less concerned about what my classmates and instructors thought of me.	2.05	12.33	32.88	39.04	13.70	3.50	0.95
I felt like I lost touch with the College community.	2.70	6.08	20.27	46.62	24.32	3.84	0.96
My academic goals became less important to me.	16.89	26.35	14.19	27.70	14.86	2.97	1.35
I felt I was successful as a college student.	4.73	19.59	23.65	37.84	14.19	3.37	1.10
I encountered discrimination or racism in my online instruction environment that had a negative impact on my learning.	80.41	13.51	5.41	0.68	0.00	1.26	0.59

Consequence of transition on teaching and learning.

Statement	Strongly Disagree (%)	Disagree (%)	Neither either or disagree (%)	Agree (%)	Strongly Agree (%)	Mean
I found my coursework more challenging.	7.43	22.97	22.97	29.73	16.89	3.26	1.20
My instructor was more available for support.	2.03	12.84	40.54	29.73	14.86	3.43	0.96
I interacted with my classmates more.	37.16	38.51	18.24	6.08	0.00	1.93	0.89
I missed more course announcements than usual.	10.14	35.14	20.95	25.00	8.78	2.87	1.16
I earned lower grades than I expected.	14.86	34.46	20.27	21.62	8.78	2.75	1.21
It took more effort to complete my coursework.	5.41	18.24	14.86	35.81	25.68	3.58	1.21
It was harder to meet deadlines.	7.43	39.19	17.57	21.62	14.19	2.96	1.22
I had a better understanding of the learning goals.	10.14	31.76	45.27	10.81	2.03	2.63	0.88
I spent more time on my schoolwork overall.	10.81	15.54	22.30	30.41	20.95	3.35	1.27

In Table 6 , students were asked to think about their specific experience of one of three physics courses from Winter 2020, after courses transitioned to online instruction. Overall, students reported increased workload and more challenging work but also increased teacher support, however they also reported poorer communication, less understanding of course goals and less interaction with their peers.

5.4. Reaction

The following section deals with student reaction to the pandemic and its effects on instruction. As we can clearly see, the pandemic provoked a lot of stress and anxiety in students (see Fig. 5 ). Paradoxically, some were happy or optimistic, perhaps driven by the “reset” that was discussed in the early days of the pandemic where we might reflect socially in our collective relationship to work and to each other. Whereas students struggled with self-discipline in online learning, they remained optimistic and motivated to achieve their goals and this did not appear dampened by the transition to online learning.

Student affect response to pandemic.

Although students experienced a higher incidence of stress or anxious emotions on average, a few were opportunistic and even excited at the prospect of moving to online learning as we can see in Table 7 . This is perhaps explainable by the fact that one third did not feel they had sufficient self-discipline to be successful at fully online learning. However, majority of the students reported feeling positive about their diligence. Indeed, that they had grown from the experience and that they expected to be rewarded for their hard work.

Student performance self-assessment of online learning during pandemic.

Statement	Strongly Disagree (%)	Disagree (%)	Neither either or disagree (%)	Agree (%)	Strongly Agree (%)	Mean
I do not have the self-discipline to be successful in a completely online environment.	15.65	31.29	19.05	26.53	7.48	2.79	1.21
During the period of online learning, I feel that I experienced personal growth.	5.48	19.18	36.99	30.14	8.22	3.16	1.00
I have the inner drive to achieve my goals.	2.76	11.03	22.07	44.83	19.31	3.67	1.00
I sometimes let others limit my success.	10.96	32.88	34.93	20.55	0.68	2.67	0.94
I am diligent and will finish what I start.	0.68	6.16	12.33	58.90	21.92	3.95	0.81
I believe I will be rewarded for my hard work.	4.79	10.27	17.81	47.95	19.18	3.66	1.05

5.5. Standards outcomes

Table 8 describes how students felt the transition impacted the standards and outcomes of their courses. Overall, students did not perceive academic misconduct had increased. Although it was felt that teachers relaxed their standards somewhat, the majority believed that their grades accurately reflected their performance.

Student perceptions of academic standards and outcomes in online learning during pandemic.

Statement	Strongly Disagree (%)	Disagree (%)	Neither either or disagree (%)	Agree (%)	Strongly Agree (%)	Mean
Academic misconduct increased among my classmates.	12.24	30.61	35.37	15.65	6.12	2.73	1.06
My instructor was not as concerned about cheating.	28.57	34.69	21.77	12.24	2.72	2.26	1.09
My instructor relaxed his/her standards (e.g., for grading, participation, deadlines, attendance, etc.)	8.84	21.77	26.53	35.37	7.48	3.11	1.10
My instructor should have been more concerned about cheating.	19.73	27.89	42.86	6.80	2.72	2.45	0.97
The grades I received accurately reflected how much I had learned.	3.40	17.69	23.13	43.54	12.24	3.44	1.03

5.6. Academic progress

In this section, we asked how students believed the transition to online instruction impacted their academic progress and future plans. Overall students appeared sanguine about the effects of the pandemic. As can be seen in Table 9 , many felt they were on pace to graduate. Most did not feel that they had been negatively impacted by the transition to online learning. Although one quarter did feel they had been held back in their academic progress due to the transition to online learning.

Student perceptions of academic progress in online learning during the pandemic.

Statement	Strongly Disagree (%)	Disagree (%)	Neither either or disagree (%)	Agree (%)	Strongly Agree (%)	Mean
In terms of my academic progress, I feel that I am still on pace to meet my academic goals as scheduled.	5.48	12.33	19.86	41.78	20.55	3.60	1.11
I will be a better student than I was before the transition to online instruction.	8.16	27.21	37.41	19.73	7.48	2.91	1.04
I am more likely to enroll in a 100% online course now than I was before the transition to online instruction.	29.25	29.25	21.77	13.61	6.12	2.38	1.21
I anticipate being behind in my academic progress upon return to the classroom.	14.97	29.25	26.53	25.17	4.08	2.74	1.11
I will have to delay graduation or employment opportunities because I was not able to complete essential coursework or practical experiences during the Winter 2020	44.22	38.10	12.24	3.40	2.04	1.81	0.92

5.7. Learning

In Table 10 we asked how students believed the transition to online instruction impacted their ability to learn. Students painted a somewhat bleaker picture of online learning, as many as one in three struggled with online discussions. While most appreciated the ability to replay videos, nearly two thirds found it very difficult to focus on online lectures.

Student Perceptions of Transition's Impact on their Ability to Learn.

Statement	Strongly Disagree (%)	Disagree (%)	Neither either or disagree (%)	Agree (%)	Strongly Agree (%)	Mean
I had access to the same software that I was using on campus.	0.68	14.29	4.08	54.42	26.53	3.92	0.97
I benefited from being able to replay video lectures.	2.72	11.56	14.29	37.41	34.01	3.88	1.08
I struggled with the use of online discussions.	8.11	31.08	21.62	31.08	8.11	3.00	1.13
I was able to focus more clearly on the lectures without the distraction of other people.	31.76	31.76	22.97	8.11	5.41	2.24	1.14

5.8. Qualitative analysis

In addition to the Likert scale questions, students were also asked five open-ended questions. Answers were inductively coded into thematic groupings and are summarized in Table 11 , Table 12 , Table 13 , Table 14 , Table 15 below. Student open-ended answers are aligned with their responses to the Likert question items. Their answers manifest their concerns for organization, communication, and technological support for effective online learning. They especially call for standards in delivery of online instruction. Students would have liked teachers to be more understanding about the their needs and the obstacles and challenges posed by the pandemic. Students also highlighted the specific challenges of this wholesale transition to online learning: technological shortcomings or lack of support, and perceptions of increased workloads, less interactions, poorer communication, and more overall confusion. Students called for varying instruction and employing a greater range of instructional tools and strategies, and most importantly for increased interactions, whether by making more use of group chats, collectively worked problems on virtual whiteboards, and flipping the classroom by assigning lectures and using class time to discuss problems. Many students called for recording lectures so they could be consulted later.

What one thing could the College have done to improve your experience after the transition to online instruction?.

Thematic Category	Frequency
Nothing	37
Good	9
Bad	2
Don't know	6
Better organization	8
No evaluations	4
Recorded lectures	11
Better communication	12
Better student adaptations	3
Better technological support	5
Institution wide policies	15
Less coursework	9
Something better	1
Better enforcement of classroom behavior	1
Loosen deadlines	1
Better preparation	3
Better communication	3
Cancel the semester	1
Return to face-to-face learning	1
Include results in GPA	3
More flexibility	3
Smoother transition	2
More classroom interactions	7
More effective instruction	4
Maintain service quality	1

Reflecting on your transition to online instruction, what was the most negative outcome?.

Thematic Category	Frequency
Distractions	12
Demotivation	40
Poor performance	9
Poor instruction	2
Social isolation	6
Increased workload	12
Stress	7
Financial problems	1
Nothing	11
Too much screen time	4
Social isolation	2
Teacher suspicions of student misconduct	1
Planning	1
Less effective learning	2
Grades not included in GPA	12
Evaluation	1
Less effective learning	12
Less classroom interaction	4
Poor performance	1
increased workload	8
Communication issues	5
Trouble concentrating	11
Technical issues	6
Lack of flexibility	1
Lack of institution wide policies	1
Cheating	1

Reflecting on your transition to online instruction, what was the most positive outcome?.

Thematic Category	Frequency
Became more organized	1
Less stress	12
Better performance	9
Later start time	11
Nothing	9
New learning method	3
Better online interactions	3
Learning new skills	6
Convenience	24
Less distractions	3
More time to study	20
More time with family	1
Difficulties with online learning	1
Recorded lectures	15
Teacher availability	8
Flexibility	1
Don't know	1
More effective instruction	7
Open book tests	1
No final examinations	1
Teacher more accommodating	2
Better performance	1
More effective instruction	1
Less coursework	1
Grades not in GPA	1
Better time management	5
Teachers more accommodating; open book tests	1
More free time	1
More control of learning	1

What one thing could your instructors have done to improve your experience after the transition to online instruction?.

Categories	Frequency
Recorded lectures	13
Nothing	1
Monitor cheating	2
More effective instruction	9
Teach to the curriculum	1
Use a syllabus	1
Less coursework	13
More classroom interaction	6
N/A	27
Vary instruction	10
Good	15
Clearer explanations	1
Recorded lectures	3
Be more understanding (technology difficulties, grading, deadlines, and evaluations)	20
More revision	1
More small evaluations	1
Better communication	10
Slower tempo	2
Smaller workload; better evaluation scheme	1
Work on problems	2
Better organization	4
Better with technology	2
Better technology and support	3
More classroom interaction	1
Turn on cameras	1
Do not enforce cameras	1
Be consistent in using online resources	1
Enforce deadlines	1
Share lecture notes	2
Virtual whiteboard	1

Is there anything else that you feel is important regarding your experience with the transition to online learning that you would like to share?.

Categories	Frequency
N/A	90
Recorded lectures	1
More online courses	1
Include grades in R score	6
More conformity in online instruction delivery	2
No online phys ed	1
Be more understanding (deadlines, attendance, tests)	5
Good	6
Do better	1
Face to face is better	6
Better communication	4
Better technology	3
Online learning is hard	7
More effective instruction	5
Develop positive strategies	5
Smaller workload	3
Monitor cheating	1
Open book tests	1
Mandatory homework	1

6. Discussion

This small survey reported on student perceptions of the transition to online learning at one college in Northeastern North America. Our results describe an overall successful transition in terms of student academic outcomes and instructional standards. However, this is far from saying that the transition was a runaway success. Students reported high levels of stress and anxiety, two thirds had difficulty concentrating in online learning, and few students were ready to continue studying online. While the outcomes could be worse, students will be happy to return to in-class instruction as lockdowns are lifted and institutions are reopened to the student population.

Our findings largely parallel the pre-pandemic literature on student perceptions of online learning. Students perceive both advantages and disadvantages to online learning ( Ebner & Gegenfurtner, 2019 ). What our results highlight however, is the emotional and psychological toll of fully remote learning. Social connection is sorely lacking after many months of enforced social distancing and isolation. This stresses the importance of the social and affective dimension of online learning ( León-Gómez, Gil-Fernández, & Calderón-Garrido, 2021 ). The relationship of the social and affective to student perceptions of online learning has not been explored in significant depth in the student perceptions literature. In light of the social effects of the pandemic on student life, our results stress the importance of online learning situations that create opportunities for connection and exchange ( Doleck, Bazelais, & Lemay, 2017 ; Kaufmann & Vallade, 2020 ). Our findings show that educators and educationalists cannot ignore the social and affective dimensions when planning and delivering online instructions for the simple well-being of many students who suffer in isolation. Our findings also support the community of inquiry notions of social and cognitive presence in successful online learning environments ( Akyol, Garrison, & Ozden, 2009 ).

In a recent study, Zheng, Yu, and Wu (2021) compared a blended learning situation where students interacted with their teacher over social media, and one face-to-face learning situation, finding that affective and cognitive learning are enhanced in blended learning and that the affective dimension appeared to mediate that effect though the two conditions did not significantly differ on grade point average, social presence, and academic self concept (though it significantly influenced cognitive learning). Studies persistently show that affective and cognitive presence are increased in blended learning ( Akyol et al., 2009 ; Shaber et al., 2010 ; Zheng et al., 2021 ). Zheng, Yu, and Wu's (2021) model is interesting as its shows quite starkly how affect and self-concept are tied to learning. These findings show that there are ways to transcend the isolation and create social and affective connections in online instruction.

Our present findings align with Zheng et al. (2021) as everyone's well-being was rudely tested by a year in social distancing and social isolation. The students in our study missed interacting with their peers in class and on campus. Many reported difficulty concentrating and heightened stress. And largely students reported increased workloads as the work shifted online. This contradicts the general discourse that assignments actually decreased. This disconnect can be understood from the perspective of distributed cognition and distributed learning, where the cognitive load is shared across a group of individuals. In this perspective the environment or the system provides affordances for activity. Work did not increase, as so much more of the cognitive load was redistributed on the individual and away from the group. Distributed cognition in an online or a face-to-face environment follow markedly different trajectories. Historically, it has been hard to reproduce online the flow of face-to-face classroom discourse where you can easily switch from one activity to another. Which is perhaps why the transition to online learning often took the form of a video conference call, with all the technical interruptions and the intrusions of private lives into the public sphere.

6.1. Limitations

As a cross-sectional survey study, we caution against any causal inferences. Moreover, the limited sample size recommends against generalizing findings to the larger population. At most, these findings are indicative of general tendencies. To show how our results might generalize, we highlight many points of convergence between our findings and the latest research on student perceptions of online learning. Our study could have been strengthened by the inclusion of other stakeholder perspectives and more in-depth qualitative analysis methods; however, we believe that our findings regarding student perceptions of online learning during the transition adds to the literature and helps to understand the encouraging and discouraging factors contributing to successful transitions to online learning.

6.2. Future directions

Future research should attempt to replicate our findings with other samples to compare results across populations to understand how institutional and contextual factors influence student perceptions of online learning. Researchers might employ more in-depth qualitative analysis methods to explore how instructional decisions interact with the social and affective dimensions and influence student receptibility to online learning.

No funding to report.

Compliance with ethical standards

Declaration of competing interest.

The authors declare that there is no conflict of interest.

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Impact of covid-19 on dental students’ mental health status and perception of sars-cov-2 vaccine.

1. Introduction

2. materials and methods, 2.1. setting and participants, 2.2. survey instrument, 2.3. data analyses, 3.1. demographics, 3.2. assessment of mental health concerns and perceptions of covid-19 vaccine, 3.3. assessments of differences between students with and without family members or friends who had covid-19, 4. discussion, 4.1. limitations, 4.2. future research directions, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Click here to enlarge figure

Variable	Min/Max	Mean (SD)	(95% CI)	n (%)
Age (year)	22/47	27.7 (4.0)	(26.8–28.5)
Gender
Male				46 (48.9)
Female				48 (51.1)
Race
Asian				34 (36.6)
Native Hawaiian or Other Pacific Islander				1 (1.1)
White				58 (62.4)
Ethnicity
Hispanic or Latino				5 (5.4)
Non-Hispanic or Non-Latino				88 (94.6)
Marital Status
Married				34 (36.6)
Single				56 (60.2)
Other				3 (3.2)
Years in School
Class 2024 (DMD Student)				44 (46.3)
Class 2023 (DMD Student)				19 (20.0)
Class 2022 (DMD Student)				18 (18.9)
Class 2021 (DMD Student)				7 (7.4)
Class 2023 (Orthondontic Residents)				3 (3.2)
Class 2022 (Orthondontic Residents)				4 (4.2)
Residency Status
Non-Utah				64 (69.6)
Utah				31 (30.4)

Variable	With Family Members/Friends Who Had COVID-19	Without Family Members/Friends Who Had COVID-19
Do you think your school should require STUDENTS to receive the COVID-19 vaccine?
Yes	29 (52.7%)	23 (59%)
No	26 (47.3%)	16 (41%)
Do you think your school should require FACULTY to receive the COVID-19 vaccine?
Yes	28 (51.9%)	24 (60%)
No	26 (48.1%)	16 (40%)

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Share and Cite

Hung, M.; Hablitzel, N.; Su, S.; Melnitsky, S.; Mohajeri, A. Impact of COVID-19 on Dental Students’ Mental Health Status and Perception of SARS-CoV-2 Vaccine. COVID 2024 , 4 , 1128-1138. https://doi.org/10.3390/covid4080078

Hung M, Hablitzel N, Su S, Melnitsky S, Mohajeri A. Impact of COVID-19 on Dental Students’ Mental Health Status and Perception of SARS-CoV-2 Vaccine. COVID . 2024; 4(8):1128-1138. https://doi.org/10.3390/covid4080078

Hung, Man, Nicole Hablitzel, Sharon Su, Samantha Melnitsky, and Amir Mohajeri. 2024. "Impact of COVID-19 on Dental Students’ Mental Health Status and Perception of SARS-CoV-2 Vaccine" COVID 4, no. 8: 1128-1138. https://doi.org/10.3390/covid4080078

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Open access
Published: 02 October 2020

Emergent transition from face-to-face to online learning in a South African University in the context of the Coronavirus pandemic

Cedric B. Mpungose 1

Humanities and Social Sciences Communications volume 7 , Article number: 113 ( 2020 ) Cite this article

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Science, technology and society

South African universities have been forced to transit from face-to-face to online learning (e-learning) as a result of the coronavirus pandemic (COVID-19). However, various challenges hinder disadvantaged students from realising the full potential of e-learning. Therefore, this study’s main objective is to propose alternative pathways to overcome such challenges for students, to enable them to have access to effective e-learning. This study draws on a two-year postdoctoral qualitative research project conducted at a South African university to explore students’ experiences of the transition from face-to-face to e-learning. Twenty-six students completing a curriculum studies programme were purposively and conveniently sampled to generate data using e-reflective activity, Zoom group meetings and a WhatsApp one-on-one semi-structured interview. Findings articulate the digital divide as a hindrance to students realising the full potential of e-learning, yet lecturers still want students to submit assessment tasks and engage with course activities on the Moodle learning management system. With universities using face-to-face learning becoming vulnerable to the COVID-19 pandemic and other challenges which result in a shutdown of university sites, alternatives need to be sought to allow students, particularly disadvantaged students, to realise e-learning.

Preparing undergraduates for the post-pandemic workplace: Teams of education and engineering students teach engineering virtually

Co-designing inclusive excellence in higher education: Students’ and teachers’ perspectives on the ideal online learning environment using the I-TPACK model

Saudi female students’ perceptions of the Community of Inquiry in online learning environments

Introduction.

Since the beginning of higher education, from the time of colonisation to the era of decolonisation, almost all South African universities have been dependent on face-to-face learning (Cuban, 1986 ; Mgqwashu’, 2017 ). Jansen ( 2004 ) argues that face-to-face learning is believed be traditional and excludes students’ experiences, because it occurs in the presence of a lecturer depositing knowledge for students in a demarcated classroom, using traditional methods (lecturer-centred) and traditional resources like textbooks, chats, chalkboards and others. However, these demarcated physical classrooms are not accessible in the case of challenges ranging from student protests to pandemic outbreaks. Face-to-face learning provides real-time contact with resources and others, takes place within a specified contact time, and provides prompt feedback to students (Black and Wiliam, 2006 ; Waghid, 2018 ). That said, e-learning is education that takes place over the Internet is alternatively called online learning, and it is an umbrella term for any learning that takes place across distance and not in a face-to-face platform (Anderson, 2016 ; Mpungose, 2020a ). Furthermore, Choudhury and Pattnaik ( 2020 ) affirm that, e-learning definition evolves with the evolution of Web from Web 0 to 4.0. Thus, “the world was introduced to Internet-based learning with Web 0, which was a read-only site. Thereon, Web (2.0) and Web (3.0) allowed real-time interaction and connected intelligence, respectively. We now witness Web 4.0 where machine and the human brain can directly interact” (Choudhury and Pattnaik, 2020 , p. 2). The concepts of e-learning, distance education, online learning and web-based education are concepts that have been used in the literature. However, Rodrigues et al. ( 2019 , p. 88) affirm that both these concepts share the common feature that “they are a form of instruction that occurs between a learner and an instructor and are held at different times and/or places, using several forms of material”. As such, Arkorful and Abaidoo ( 2015 ) refer to e-learning as the use of educational technologies to enable access to learning and teaching material online. Thus, the importance of e-learning which takes place through the use of the Internet in 21st century university education is undeniable, particularly for the students of today as digital natives (Bennett et al., 2008 ; Prensky, 2001 ). Amory ( 2010 ) and Khoza ( 2019b ) state that e-learning is capable of making course content available online, because of the widespread use of modern technologies such as hardware resources (computers, laptops, mobile phones and others), and software resources (learning management system, software applications, social media sites and others). This suggests that students have freedom to access course information/content anytime and anywhere, irrespective of challenges such as the pandemic outbreak—provided they have access to hardware and software resources.

In complicating the above debate, some studies (Liu and Long, 2014 ; Nikoubakht and Kiamanesh, 2019 ) further argue that face-to-face learning is irreplaceable and is the cornerstone of every learning institution, even if the current discourse and technological revolution demand the use of e-learning. The latter studies believe that there is still a conundrum between face-to-face (person-to-person interaction in a live synchronous platform) and e-learning (self-paced learning in an asynchronous platform). As a solution to this conundrum, other scholars (Anderson, 2016 ; Bates, 2018 ; Graham, 2006 ) believe that blended learning which combines online and face-to-face learning is the way to go, so that students can use many ways of accessing course content based on their needs (strengths/limitations).

Nevertheless, there are compelling conditions that can make students choose online over face-to-face learning; this may include violent student protest, pandemic diseases like COVID-19 in the context of this study, and others. According the World Health Organization-WHO ( 2020 ), COVID-19 is a new strain of viruses discovered in 2019, which cause illnesses ranging from the common cold to more severe diseases that can lead to death. They are transmitted between animals and people. Common symptoms of infection include respiratory symptoms, fever, cough, and shortness of breath. As at 31 March 2020, statistics stay at 33 106 deaths globally and in Africa is currently 60 deaths. In other words, this pandemic poses a threat to the face-to-face learning context globally, including in South Africa.

On 11 March 2020 the WHO ( 2020 ) declared COVID-19 a pandemic, and everyone was advised to avoid close contact with anyone showing symptoms. Therefore, universities across the globe have to shut down. In the South African context the President called on all universities to shut down and find ways to offer lectures online as from 18 March 2020 as a precautionary measure (DHET, 2020 ). This call raised questions as to the feasibility of e-learning, particularly at the School of Education in one of the universities in the province of KwaZulu-Natal, because of the extent of inequalities in the South African context. While Mzangwa ( 2019 ) agrees with Bunting ( 2006 ) that since 1994 much has been done in higher education to redress the inequalities of the past through higher education institutions’ policy amendments through the National Plan for Higher Education (Ministry of Education, 2001 ). These amendments have not led to benefits for the majority of previously disadvantaged black South African students in terms of access to e-learning.

In addition, the digital divide—the gap between those who have and do not have access to computers and the Internet—seems to be a huge factor limiting the feasibility of e-learning in a South African context (Van Deursen and van Dijk, 2019 ; Warschauer, 2002 ). These latter studies further assert that issues such as socio-economic factors, race, social class, gender, age, geographical area and educational background determine the level of the digital divide in a university context. While access to the Internet and computers is high in developed European and American universities, African universities—particularly in the South African context—are still battling because of the intensity of the factors which led to the digital divide (Van Deursen and van Dijk, 2019 ). Research shows that various programmes and policies have been developed and implemented to remedy this challenge; hence, universities provide students with free laptops and Wi-Fi (wireless network commonly allows technological devices to interface with internet) access inside the university and residences (Rodrigues et al., 2019 ; Schofield, 2007 ). However, little or no research has been done in the South African context to intervene in addressing university students’ challenges (the digital divide) that hinder them from accessing e-learning from home. This study argues that e-learning while students are at home can never be realised in a South African university context unless the digital divide is addressed. In proposing alternative pathways for South African universities to deal with the digital divide, this study considers a connectivism learning framework.

Conceptualising learning in a digital age

The rapidly evolving technological landscape in the 21st century has meant that university lecturers “have been forced to adapt their teaching approaches without a clear roadmap for attending to students’ various needs” (Kop and Hill, 2008 , p. 2). As a result, connectivism is the promising initial lens through which to conceptualise learning in this digital age, because of its varying attributes from face-to-face to e-learning. Thus, Siemens and Downes ( 2009 ) see learning as the process of crossing boundaries by creating connections or relationships between human and non-human nodes through the setting of an interconnected network. Connectivist learning draws much from available Internet and technological resources to make an effective network that will maximise learning. As a result, connectivity seeks university lecturers to consider the possibilities of Internet access and other technological resources for effective learning, so that each individual student may gather and share information irrespective of challenges (the digital divide) faced (Bell, 2011 ; Kop and Hill, 2008 ). In other words, for effective e-learning to occur even if students are at home, access to the Internet and technological resources should be made available so that they may make connections amongst themselves and the lecturers, irrespective of hindrances faced.

Siemens (2005) further argues that in connectivism, students are not taken as a blank slate or passive recipients of information but are taken as active participants who can nurture, maintain, and traverse network connections to access, share and use information for learning. In order to ensure this, Siemens and Downes ( 2009 ) propose eight principles guiding connectivist learning, as depicted in Table 1 overleaf, which are according to this study are now conceptualised to form dichotomies between F2F learning and e-learning. These principles draw from basic learning frameworks (behaviourism, cognitivism, and constructivism) to incorporate both subject and social experiences for learning. Traditionally, learning is believed to be occurring when the lecturer provides a stimulus (teacher-centred activities) so that students can respond, but the rapid development and implementation of new technologies seeks learning to be individually and socially constructed by students (learner-centred activities) to maintain a diversity of ideas. This suggests that digital learning is more participatory and effective than traditional learning because it seeks lecturers to engage students in a dialogue for social construction of knowledge (Downes, 2010 ). Moreover, Siemens and Downes ( 2009 ) agree with Anderson ( 2016 ) that learning is about creating and connecting to a community (node) of learning within a network. This connection does not only take place within a learning institution, but can also be online so that students at home or in their residences can access learning. In other words, connectivism prioritises e-learning as the first and best option for students to access learning, if there are forceful or compelling conditions that hinder face-to-face learning.

Siemens and Downes ( 2009 ) further argue in principle that traditional resources such as books, chats, chalkboard and others form the core of learning, but the digital age needs them to be supplemented by modern resources like the Internet, computers, mobile phones and others for students to make connections and share information amongst themselves and others. In other words, modern resources enhance active student participation and the capacity to know more; thus the active student has the ability to use resources provided to seek out current information from primary and secondary resources, as compared to being a passive student (Downes, 2010 ). This suggests that in connectivist learning, it is not enough for a student to depend only on the prescribed readings, taught content, consultation with one lecturer and students in a particular subject/module. However, connectivists seek students to enjoy exploring the world in order to connect with other people outside the normal context, through the use of search engines, social media and other means, because learning is about not only knowledge consumption but construction (Anderson, 2016 ).

The manner in which students are assessed depends on the ability to see connections between subject fields, ideas, and concepts (Siemens and Downes, 2009 ). In other words, assessment must be made enjoyable to students because it is not done for the purpose of grading but for developmental purposes (Black and William, 2009 ). The content (objectives) taught during the official time in the lecture may change over time, based on new contributions in a subject; this requires students to be driven by a professional and social rationale in making decisions as to what to learn and how to make meaning out of it (Downes, 2010 ). Therefore, just lecture contact time is not enough for students, and it should be supplemented with students’ extra time so that learning outcomes can be achieved.

Furthermore, review of research done by Damşa et al. ( 2015 ) on quality in Norwegian Higher Education, outlines dichotomous aspect of F2F learning and e-learning. The study aimed at identified important contributors to enhance of quality learning in higher education, and to identify the knowledge gaps in the literature. It was found that, in as much as both platforms (F2F learning and e-learning) share the same aspect in communication, collaboration, and supervision and interaction. However, e-learning provides much of these aspect than F2F learning since it creates more intense atmosphere from synchronous to asynchronous teaching and learning aspect. This suggests that the development use of educational technology (videos, smart phones, learning management systems and social media sites) raises quality learning on e-learning as compared to F2F environment. Thus, e-learning advocates for student-centredness versus teacher-centeredness in teaching and learning of the content because “students learn together online, support mechanisms such as guiding questions generally influence the way students interact…” (Damşa et al., 2015 , p. 56).

Review of the literature: technology in and of learning in a digital age

While there are various definitions of educational technology, a narrow definition refers to educational technology as “the effective use of technological tools in teaching and learning” by bringing in students’ experiences (Govender’ and Khoza, 2017 , p. 67). These studies (Amory, 2010 ; Khoza, 2019b ) are pessimistic in tone, further pioneering the most narrow and concise definition of educational technology, that it is there because of technology in education (software and hardware resources in learning) and technology of education (pedagogical resources in learning). Thus, according to the context of this study, educational technology is all physical resources and online resources used in learning, and ideological resources behind the use of both physical resources and online resources.

Nocar et al. ( 2016 ) conducted a qualitative case study in China and the Czech Republic to outline the importance of physical resources. Findings outlined that the use of both traditional physical resources and modern physical resources for teaching display a fruitful result for students’ knowledge acquisition. Moreover, some scholars believe that traditional physical resources (traditional education), like stationary desks, books, chalkboard and others, enhance students’ task to memorise and recite content during learning, and its use still symbolises the principle of slavery (Cuban, 1986 ; Freire, 1972 ). However, the use of traditional physical resources promotes a teacher-centred method, which is the most direct and effective way for teaching students because it provides face-to-face interaction (Hoadley and Jansen, 2014 ). As such, Liu and Long ( 2014 ) further argue that traditional physical resources, sometimes referred to as ‘old technology’ (television, chats, radio, posters and others) is irreplaceable and the cornerstone of every learning institution, even if the current discourse demands the use of modern physical resources.

Furthermore, the importance and usage of modern physical resources (technological tools) is witnessed in every corner of each university. A study conducted by Keengwe, Onchwari, and Wachira ( 2008 ), to provide a literature review on the use of modern physical resources (computers, mobile phones and others) for teaching and learning university courses, affirmed this. The study outlined that modern physical resources provide opportunities to support students’ learning and need good and strategic planning for maximum integration into the curriculum. Consequently, in the past two decades universities have begun to integrate modern physical resources into the curriculum for effective learning (Khoza, 2019a ; Mpungose’, 2019a ). This suggests that students should be provided with relevant technological devices, which may include but are not limited to netbooks, iPads, webcams, laptops and desktop computers, mobile phones and others. These kinds of new technology have made life easier for students, because they would find notes and all course information stored electronically and easily accessible (Amory, 2010 ; Waghid, 2018 ). In other words, that the accessibility of modern physical resources give students options to use any available resources in order to access online resources.

van de Heyde and Siebrits ( 2019 ) revealed that online resources are software resources in education that help physical resources to communicate learning. This includes but is not limited to application software packages (Microsoft Office 365), Internet browsers (Firefox, Chrome), social media sites (Twitter, Facebook), and learning management systems (Moodle, Canvas) (Anderson, 2016 ; Bates, 2018 ). In the context of this study, the focus is more on learning management systems and social media sites to enhance e-learning. As such, the importance of e-learning is witnessed in study conducted Swinnerton et al. ( 2018 ) in unbundled University project exploring digitalisation and marketisation of higher education in both United Kingdom and South Africa. The study revealed that irrespective of existing inequalities, but the use of e-learning for teaching and learning university courses is significantly the effective way to ensure relationships between universities and private sector. In other words, if students does not have access to technological resources for e-learning they are less likely to be unemployed after receiving their qualification because of the lack of technological skills applicable in the workspace.

Cavus and Zabadi ( 2014 ) argue that in trying to move away from the traditional paper and pen environment (face-to-face), learning management systems (web-based learning environment to disseminate content) is one of the most highly adopted and used online environments in higher education institutions for e-learning. This includes open-source software learning management systems (free of charge, where the source code can be changed) such as Moodle, Open edX and Chamilo, and cloud-based learning management systems (with a start-up cost and source code that cannot be changed) such as Canvas, Sakai, dot Learn and others. Ajlan and Pontes ( 2012 ) outline that almost all learning management systems have common features, which include pedagogy, learner environment, instructor tools, course and curriculum design, administrator tools, and technical specifications. However, their efficiency can be different because of various factors such as being unclear to users, bandwidth requirements, take-up and maintenance cost, manuals, customisation and adaptation to the local environment (Anderson, 2016 ). However, this needs effective e-learning policies in place in order to address the needs of students and lecturers as according to the recent study conducted by Swartz et al. ( 2019 ) to explore the core business in contemporary South African universities.

In exploring first-year students’ use of social media sites at one South African university of technology, Basitere and Mapatagane ( 2018 ) confirmed that students become more interactive when they use platforms that they are familiar with, such as social media sites, compared to learning management systems imposed by the university. Social media sites are referred to as Internet-influenced Web 2.0 technologies that allow users to create social networks to share content based on personal experiences, education and society. Hence, social media sites users can be referred to as ‘prosumers’ because they produce (create) and consume (share) information (Clement, 2020 ; Ritzer and Jurgenson, 2010 ). Moreover, a recent review conducted by Manca ( 2020 ) on the integration of social media sites into learning, revealed that both Twitter and Facebook are the most used social media sites in higher education, compared to Instagram, WhatsApp, Pinterest, Snapchat and others. In addition, social media sites content is easily accessible because it is compatible with both computers and mobile devices, and this makes life easier for students (Clement, 2020 ; Dlamini and Nkambule, 2019 ; Manca, 2020 ).

With all of the above being said about the use both physical resources (traditional and modern) and online resources (learning management system and social media sites) for learning, but digital divide remains the major issue. As such,Van Deursen and van Dijk ( 2019 ) assert that the digital divide is one of the big limitations on the use of educational technology globally. These authors’ study further argues that the digital divide is a real phenomenon that is here to stay in developed countries, but is worse in developing ones—not only in terms of the first digital divide (access to Internet), but also in terms of the second digital divide (attitude, skills, type of use) and third digital divide (Internet outcomes/benefits). This suggests that even though universities can provide free access to Wi-Fi within their perimeters and students’ residences, including free laptops, there will be some students (residing in rental rooms or at home) who might not have access to the Internet. Similarly, some students would prefer to use other resources, based on their strengths or limitations. Hence, this paper argues for alternatives to be made available by lecturers or university management, so that all students can have the same access to e-learning irrespective of their geographical area, culture, race, socio-economic factors and others.

Selwyn ( 2004 ) further argue that the dichotomous aspect of digital divide clearly reveals the ones that either have access or do not have access to technological resources, and this influence the status of connectedness (either connected or not connected). The latter author assert that this situation is termed as ‘haves’ and ‘have-nots’. Consequently, the latter author concludes that the digital divide is a critical issue in higher education landscape that is not just technological but it is also social, economic, cultural and political. This suggests that in mitigating digital divide, universities, communities, churches, political figures, businessman and others seek to collaborate and come up with both practical and theoretical solution in order to enhance effective e-learning in pre, during and post pandemic outbreak.

Research context and method

Study context.

LMS have been adopted by most South African universities to cope with the demands for accessible and more flexible online content dissemination (Amory, 2010 ; Mpungose, 2019b ). In transitioning from the paper (face-to-face) to the paperless (online) environment, the University of KwaZulu-Natal in South Africa adopted the Moodle LMS in 2010; it was made compulsory in 2016 for first-year students and fully implemented at the fourth-year level in 2019 (University Moodle Training Guide, 2017 ). Unavailability of a guiding online learning policy and lack of training for lecturers ignited challenges, which were evident in the use of learning management systems by students (Mpungose, 2019b ).

To this end, from 2019 to 2020 I conducted a postdoctoral research project on students’ experiences with the use of a learning management system in a School of Education. From the project, I extracted a case of 26 students’ experiences of the use of the LMS. A South African University at School of Education offers a broad range of degree programme courses across various fields of study. It prepares mostly disadvantaged black students, followed by other minorities (Indian, coloured (mixed race) and white students) for professional teaching careers in Education Studies and other disciplines. The School of Education mainly offers all lectures in face-to-face form, while the learning management system is used as an online resources depository (holding lecturers’ notes) for student access. The eruption of the COVID-19 pandemic forced the School of Education to move all lectures totally online. However, the majority of registered students in School of Education at South African universities are victims of the digital divide, and this hinders their access to e-learning (Bunting, 2006 ; Dlamini and Nkambule, 2019 ). Therefore, this study’s main objective is to propose alternative pathways to overcome hindrances to students’ access to effective e-learning.

Research methods and data collection

This is a qualitative interpretive case study of 26 students who were purposively and conveniently selected because they were accessible; they were attending face-to-face lectures and then transitioned to e-learning due to the COVID-19 pandemic. After recruiting students through an electronic flyer, they signed consent forms with details of ethical issues (confidentiality, anonymity, and beneficence). I used interpretivism not to predict what students experience, but to understand and describe how they make meaning of their actions during the transition period in their own context of the School of Education shutdown (Creswell, 2014 ). Through the use of a more explorative case study design, I generated a rich and deep description of students’ experiences, which resulted in pioneering alternatives to overcome hindrances in realising e-learning (Yin, 2013 ).

Students were given an e-reflective activity to be completed in two weeks’ time, two sessions of Zoom group meetings for a period of 40 min each, and a WhatsApp one-on-one semi-structured interview for 35 min (Creswell, 2014 ; Yin, 2013 ). iCloud was used to record meetings and interviews for direct transcription to ensure trustworthiness (transferability, dependability, confirmability and credibility).

Data were thematically analysed using inductive and deductive reasoning (Creswell and Poth, 2017 ). The data generated by the three instruments were recorded and not transcribed, but directly and openly coded from the recorded source in order to avoid loss of meaning during transcription. Open coding was used to connect codes to categories. I deductively mapped the codes onto the set categories (from the theoretical framework and the literature) to form themes. However, I sought to use an inductive process to recapture the remaining codes, which were not deductively analysed during the prior analysis, to form categories. After using these processes as a guide, categories were focused and sharpened to form three themes, as indicated in the findings section

Consequently, two research questions were unpacked, namely: what are students’ experiences of the transition from face-to-face to e-learning and why their experiences are in particular ways when learning online. The first question gave answers to the first objective of the study, which is to understand students’ experiences of the transition from face-to-face to e-learning, and the second question addresses the second study’s objective, which is to find reasons that informs students’ experiences. This is elaborated in findings and discussion section in order to propose alternatives that can assist or allow students, particularly disadvantaged students, to realise or enjoy benefits of e-learning.

Presentation of findings

In this section, I present the key findings on students’ experiences of the transition from face-to-face to e-learning. I articulate the use of online resources and physical resources before crafting the alternative pathways through themes and its respective categories

Theme 1: Experiences of the use of online resources

Mpungose ( 2019b ) Agrees with Selwyn and Stirling ( 2016 ) that accessibility to online resources enhances effective e-learning. This suggests that e-learning is only possible provided students have access to online resources ranging from emails, software applications, learning management systems, social media sites and others. As such, Student 1 articulated, “ I keep on receiving emails saying the assignment that is due needs to be submitted on Moodle … I was informed that lectures will be recorded and posted on Moodle [learning management system]”. However, digital divides limits most students for effective e-learning particularly those staying in remote areas. Moreover, Student 4 confirmed this “… I only check my emails from the community library with internet access because I have no internet access and network service at home, but I can sometimes only receive voice calls and text messages from my phone… ”.

Internet access seem to play a major role in order to observe effective e-learning, but this can never be achieved if students have limited or no access. For instance, Student 7 asserted, “ I do not have data bandwidth [Internet access] at home …submitting assignment is impossible …”. This assertion shows that online assessment is impossible if the students have no access to the internet. Student get frustrated if lecturers keeps on demanding students to meet due dates while students have no internet access. As shown by Student 24 who articulated, “… having limited internet access but I am expected to submit an assignment next Friday, in a week’s time …a lecturer is briefing us to download resources from Moodle ”.

Furthermore, Selwyn ( 2016 ), as well as Khoza and Biyela ( 2019 ) share the same sentiment that social media sites plays a huge role in mitigating digital divide in order to realise e-learning in this digital age. As such, Student 5 indicated, “ since there is no Internet café by home, I use free Facebook or WhatsApp data bundles to communicate with other students …” This suggests that most students have access to social media sites because of free data bundle access provided by network service providers (Vodacom, Telkom, Cell C and others in a South African context), and this helps student to communicate learning. Consequently, Khoza ( 2019b ) further argue that having access to online resources without pedagogy behind the use can limit effective e-learning. This is witnessed by Student 12 who opined, “ I am so disappointed of this sudden shutdown without having proper ways or training in place to access lectures online … ” Similarly, Student 15 said, “W e are still not told which online platform will be used for online lectures … ” In other words, students seek adequate training on the use of online resources so that they can be well informed to avoid confusion. Evidently, Student 9 showed confusion by outlining that “… university informed us that lectures will be online, but they did not tell us the online platform is going to be used ”.

Theme 2: Experiences on the use of physical resources

Makumane and Khoza ( 2020 ) argue that traditional physical resources is influenced by professional reasoning in order to attain specific discipline goals during curriculum implementation. This suggests that traditional physical resources are fundamentals in addressing the module needs in e-learning. For instance, most of the students agreed with Student 23 who posited, “ I am currently depending on the hard copy of module outline and recommended books for studying because even libraries with Internet at home are also closed” . In other words, traditional physical resources like textbooks, module/course packs, and other hardcopies can act as an alternative pathway in case students have no internet access. While it is valuable for students to have access to modern physical resources like laptops, smartphones, Wi-Fi routers and others in order to enhance e-learning, but affordability to possess such resources remains a question because of social divide (poor socio-economic background). Thus, this remains the burden of the university to provide modern physical resources to students for successful e-learning. As such, student 14 asserted, “ …We were promised to get laptops when the academic calendar commences but still there are no laptop, and I end up using my smart phones for correspondence ”.

Similarly, Student 17 said, “ This shutdown will affect me because I am staying in remote areas away from campus and do not have funds to access Wi-Fi hotspot spaces like community libraries … and there are no funds provided for to support us… ” While the shutdown demands all lectures to be online and universities are also demanded to put measures in place for effective e-learning, but failure to provide all necessary resources to students can bring more frustration in the process. Evidently, Student 11 shared the same sentiment with other international students “ I will be suffering to find the transport to go and come back from home … Shutting down face-to-face lectures causes chaos since I do not have necessary equipment for learning”.

Discussion of findings

The adoption and use of online resources in a South African university shows the critical need to serve students for e-learning (van de Heyde and Siebrits, 2019 ). Van de Heyde and Siebrits ( 2019 ) further argue that online resources like learning management systems are highly used by universities for online lectures, but the form of customisation to adapt them to a local context may hinder learning. This is evident from students’ accounts on the use of Moodle for e-learning, where they stated that only a few students had access to the Moodle learning management system to download readings, slides and others during the transition from face-to-face to e-learning (at home). This suggests that Moodle was customised as a depository, and not to provide asynchronous online lectures. In other words, there was poor customisation of the Moodle learning management system to link with other online resources for chatting (Pear Deck), video conferencing (Zoom), and recording (CamStudio) and others (Anderson, 2016 ). Consequently, the findings indicate the general consensus that the Moodle learning management system alone is not capable of offering online lectures, but needs to be supplemented by other online software and social media sites. This suggests that, universities should start to think out of the box to consider social media site as an official platform to supplement learning management system to offer lecturers online.

Consequently, students therefore preferred social media sites (Facebook and WhatsApp) for communication, which were not officially adopted by universities for e-learning. In support of this, ‘prosumers’(students) as digital natives who are techno-savvy enjoy the use of Web 2.0 applications with good user-friendliness and swift communication (Clement, 2020 ; Ritzer and Jurgenson, 2010 ). Findings showed that even if students have limited access to internet but free data bundles form their social media sites account, they could access each other for content discussion and communication. As a result, Hamidi and Chavoshi ( 2018 ) further argue that if students can use social media sites successfully, universities should consider bringing social media sites (Snapchat, WhatsApp, Facebook, Instagram, twitter and others for e-learning.

Moreover, the findings show that the university did not have any policy in place guiding the use of e-learning and nor was training provided. This situation as according to Yu ( 2016 ) is termed to be influence that leads to students’ technostress caused by the misfit between environmental demands (e-learning) and students abilities (access to online resources). In other words, the shutdown that occurred because of pandemic outbreak (COVID-19) demanded student to have access to online resources in order to take their lectures online while most of them are from remote areas having no internet access, and are still battling to use the newly introduced software for e-learning (video conference software like Zoom). As such, students were confused as to what resources were available for e-learning and how they will transition from face-to-face to e-learning. This was worsen by the unavailability of the guiding e-learning policy in place and no instructional designers employed by the university to provide relevant capacity building for students. As such, Mpungose ( 2019b ) assert that the power lies with the university management to use e-learning policy that can address issues on content dissemination, execution of assessment, and online resources in order to equip students with necessary skills for effective e-learning. This suggesst that policy viability on the use of online resources also give direction to both students and lecturers so that they can know their roles.

Several students agreed that traditional physical resources is the core of learning at the university, even if there are challenges hindering e-learning, because they relied on recommended books, module outlines, written notes and others. This proves that the old technology is irreplaceable, and that it acts as a back-up to e-learning. Thus textbooks, posters, charts and others must be made available to support students’ learning (Cuban, 1986 ; Freire, 1972 ). This suggests that traditional physical resources may be most useful to those students who have no or limited access to internet. As such, each module/course seek the need to have these resources in place even if the module/course is offered online. The use of traditional physical resources for learning displays a fruitful result for students’ knowledge acquisition (Simmonds and Le Grange, 2019 ). Moreover, traditional learning is vertical (formal) and driven by student knowledge for learning in a demarcated environment (Khoza and Biyela, 2019 ). This allows students have control over “selection of the content (selection), when and how they learn (pedagogy and sequence), as well as how quickly they learn (pace)” (Hoadley and Jansen, 2014 , p. 102). As result, students preferred and opted to use the nearest local community libraries with access to Wi-Fi rather than staying at home (often with no Internet) in order to access online resources irrespective of difficulties faced at home.

Most students did not have laptops, even though these were provided free of charge by the university (many had been sold for personal benefit). They preferred to use mobile phones with free network data bandwidth for communicating amongst themselves. In other words, the use of modern physical resources provides an easy way to ensure e-learning, because it provides access to recorded lectures and electronic resources like videos, but it needs good planning (Keengwe et al., 2008 ; Mpungose’, 2019a ). The main concern that hindered students from realising the full potential of e-learning was the expensive cost of Internet infrastructure such as Wi-Fi routers, laptops, mobile phones and access to data bandwidth. Consequently, Van Deursen and van Dijk ( 2019 ) argue that Internet access and technological resources (the first digital divide) is the main limiting factor in universities from developing countries like South Africa, even though students do have skills (the second first digital divide) to benefit from e-learning (the third first digital divide). In other words, the use (ideological resources) of any available physical resources is not a problem to students (digital natives) in a digital age—the problem is the affordability and availability of those physical resources for e-learning.

Towards alternative pathways for e-learning

This study explored students’ experiences during the transition from face-to-face to e-learning in a School of Education at a South African university. Based on the case study and the literature, including the guiding theoretical framework, the study identified benefits, challenges, and other related issues on the use of physical resources and online resources to realise e-learning. Most importantly, the interpretation of empirical data generated provides a summary of proposed alternative pathways and implications related to the use of physical resources and online resources to enhance effective e-learning. On the first hand, findings suggest that students are influenced by formal experiences (hardware), which seek students to use traditional physical resources to enhance e-learning. On the other hand, students are also influenced by informal experiences (software), seeking them to use online resources for effective e-learning. In complication this findings, students seem to miss non-formal experience (pedagogy), which seek them to use their own identities (love, passion, values, self-direction and others) to find thousand ways or theories to enhance a successful e-learning. Moreover, it is proven that e-learning resides in human and non-human appliances (Siemens and Downes ( 2009 ); thus students should be provided with relevant traditional resources (books, manuals, chats, posts and others) and modern resources (laptops, mobile phones/tablets, mobile Wi-Fi routers and others). In addition, free monthly Wi-Fi data bandwidth should be provided to students so that they may access e-learning, since this seems to be the main challenge to achieving e-learning in the South African context.

Downes ( 2010 ) argues that e-learning needs connectedness of specialised nodes or information sources, so that students can learn anyhow, anywhere and independently, at their own pace. To achieve this, this study therefore holds that the Moodle learning management system should not be used as a depository, but should be customised to be linked to social media sites (WhatsApp/Facebook), lecture-recording software (CamStudio), video and audio conferencing (Zoom, YouTube live, Skype, Microsoft Teams) and other learning resources in order to provide interactive lectures (both synchronous and asynchronous). This will serve to eliminate the dichotomy between face-to-face and e-learning, because the learning taking place when at the university should be the same as that which is available when students are at home.

The findings indicate that fully equipped university information centres should be identified and used to provide blended lectures, through the special arrangement of community libraries (since even these are not accessible now owing to COVID-19), in order to meet the needs of students coming from remote areas halfway. The findings also show that without proper planning, e-learning will never be achieved at a university. Hence, a university should have an e-learning policy, intense scheduled online learning capacity building, and allocated instructional designers (not technicians) to capacitate both lecturers and students.

All learning management system share the same features: pedagogy, learner environment, instructor tools, course and curriculum design, administrator tools, and technical specification features (Cavus and Zabadi ( 2014 ). However, the findings showed that the learning management system is missing the personal feature for students that will motivate them to love and have a passion for using online resources. This study posits that in order to leverage the potential of the Moodle learning management system, it should be linked with software that provides educational videos (NBC Learn), games for student-centred activities (game-based learning software), Edublogs (assessment for learning) and others. In other words, choosing what resources to use and learning to offer depends on rationale, time management and goals to be achieved during e-learning. This will assist students to incorporate both physical and online resources to achieve effective e-learning for these digital natives (Mpungose’, 2019a ; Prensky, 2001 ).

Despite challenges experienced by students in transitioning from face-to-face to e-learning—in particular, the prominence of the digital divide as the main hindrance to students realising effective e-learning—overall the customisation of the Moodle LMS to meet the local needs of disadvantaged students is beneficial to realise e-learning. Moreover, the findings indicate that while there may be many challenges that can hinder students from realising the full potential of e-learning, alternative pathways like the provision of free data bandwidth, free physical resources and online resources, and the use of an information centre for blended learning and others, seem to be the solution in the context of COVID-19.

However, it must be taken into consideration that while this can be the solution, students are unevenly challenged, and therefore still need capacity building on the use of learning management systems and other newly adopted online learning software. It is also imperative that university-wide teaching and learning pedagogy, instructional designers and e-learning policy consider the potential benefits and challenges when encouraging the use of e-learning.

Within the South African context, there is a critical need for increased investment in upgrading resources, both in universities and at community level, because of the digital divide. While there is still a need for further research, this article emphasises the both practical and theoretical alternative pathways that can be used to enable university students to realise the full potential of e-learning. Universities need to plan ahead of hindrances to learning such as a pandemic outbreak, student protests and others, and be abreast of the current literature on the rapidly evolving discipline of ET.

Data availability

The datasets used and/or analysed during this study are available from the authors on reasonable request.

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Acknowledgements

I want to thank Prof. Simon Bheki Khoza for his supervision in to construct this article from a PhD research and Post-doctoral project, as well as Leverne Gething language for editing. Furthermore, I want to acknowledge support and advancement from the National Research Foundation (NRF) and the Fulbright scholarship within the framework of the Research and innovation.

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Mpungose, C.B. Emergent transition from face-to-face to online learning in a South African University in the context of the Coronavirus pandemic. Humanit Soc Sci Commun 7 , 113 (2020). https://doi.org/10.1057/s41599-020-00603-x

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Czeisler MÉ , Lane RI , Wiley JF , Czeisler CA , Howard ME , Rajaratnam SMW. Follow-up Survey of US Adult Reports of Mental Health, Substance Use, and Suicidal Ideation During the COVID-19 Pandemic, September 2020. JAMA Netw Open. 2021;4(2):e2037665. doi:10.1001/jamanetworkopen.2020.37665

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Follow-up Survey of US Adult Reports of Mental Health, Substance Use, and Suicidal Ideation During the COVID-19 Pandemic, September 2020

1 Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia
2 US Centers for Disease Control and Prevention, Atlanta, Georgia
3 Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
4 Institute for Breathing and Sleep, Austin Health, Heidelberg, Victoria, Australia

Adverse mental health symptoms among US adults were more prevalent during the early phase (April-June 2020) of the coronavirus disease 2019 (COVID-19) pandemic compared with prepandemic estimates (eg, 3-fold increased prevalences of anxiety and depression symptoms, 2-fold increased prevalence of suicidal ideation). 1 , 2 In June 2020, 2238 (40.9%) of 5470 US adults reported adverse mental or behavioral health symptoms. During this time, the prevalence of symptoms was lower in adults aged 65 years or older (141 of 933 [15.1%]) than in young adults aged 18 to 24 years (547 of 731 [74.9%]; P < .001). 1 Given suggestions that acute increases in the prevalence of adverse mental health symptoms may represent a transient response to mass trauma, 3 we sought to determine whether these patterns persisted in September 2020 and to examine disproportionately affected demographic groups.

In this survey study from August 28 to September 6, 2020, US adults aged 18 years or older completed 139-item internet-based surveys through Qualtrics for The COVID-19 Outbreak Public Evaluation (COPE) Initiative. Surveys were administered to an online respondent panel maintained by Qualtrics, a commercial survey company with networks of participant pools. Respondents reported demographic characteristics and completed questions assessing attitudes, behaviors, and beliefs about COVID-19, mitigation measures, and mental and behavioral health. When possible, brief, validated instruments were used or adapted.

Demographic quota sampling and survey weighting were used to make the sample representative of the US population by age, sex, and race/ethnicity, and weighted values are presented. Participants reported symptoms of anxiety and depression, COVID-19−related trauma- and stressor-related disorders, starting or increasing substance use to cope with pandemic-related stress, or having seriously considered suicide within 30 days. The Monash University Human Research Ethics Committee approved the study protocol, and participants provided informed consent electronically. The article followed the American Association for Public Opinion Research ( AAPOR ) reporting guideline.

Multivariable Poisson regressions with robust standard errors were used to estimate adjusted prevalence ratios (aPRs) and 95% CIs for any adverse mental or behavioral health symptom with the following factors: sex, age, sexual orientation, race/ethnicity, Census region, urban/rural residence, and unpaid caregiver status. Separate models were run for the following collinear factors: disability status, insomnia symptoms, prior psychiatric diagnosis (anxiety, depression, posttraumatic stress disorder, or a substance use disorder), and age-excluded employment status. Age was not adjusted for in the model that included employment status to avoid collinearity between these variables. Continuity-corrected McNemar tests were used to assess longitudinal differences in adverse mental health symptom prevalences among respondents who completed surveys in June 2020 and September 2020. All calculations were performed in Python version 3.7.8 (Python Software Foundation) and R version 4.0.2 (The R Project for Statistical Computing) using the R survey package version 3.29. P values were 2-sided, and statistical significance was set at P < .05. Detailed methods 1 describing the recruitment process, survey, screening tools, and analyses can be found in the eAppendix in the Supplement .

Overall, 5285 of 11 953 potential participants (44.2%) completed September 2020 surveys; 5186 of these respondents (98.1%) met secondary screening criteria and were analyzed (1155 [22.3%] were recontacted after April 2020; 1605 [30.9%] were recontacted after June 2020; 2426 [46.8%] were first-time respondents). Overall, 1710 (33.0%) reported anxiety or depression symptoms, 1536 (29.6%) reported COVID-19–related trauma- and stressor-related disorder symptoms, 781 (15.1%) reported increased substance use, 618 (11.9%) reported having seriously considered trying to kill themselves in August, and 2237 (43.1%) reported at least 1 of these symptoms ( Table 1 ).

Adverse mental or behavioral health symptoms were more prevalent among adults younger than 65 years vs adults aged 65 years or older (eg, 18-24 years, aPR, 3.56 [95% CI, 3.04-4.18]) and among multigenerational caregivers vs noncaregivers (aPR, 1.93 [95% CI, 1.78-2.08]) and respondents with prior psychiatric diagnoses vs those with no prior diagnoses (aPR, 1.98 [95% CI, 1.83-2.15]) ( Table 2 ). Prevalence of adverse mental or behavioral health symptoms was also higher among respondents with disabilities or insomnia symptoms vs those without, caregivers for adults vs noncaregivers, essential workers and unemployed respondents vs nonessential workers, and respondents who were lesbian, gay, or bisexual vs heterosexual. Among respondents who were recontacted after June 2020, prevalence of adverse mental health symptoms did not differ significantly between June 2020 and September 2020.

In a later phase of the COVID-19 pandemic (September 2020), the prevalence of adverse mental health symptoms among US adults remained elevated compared with prepandemic estimates. 1 , 2 This finding contradicts the notion that adverse mental health symptoms were transient, self-limiting responses. Despite increased COVID-19–related morbidity and mortality risk, 4 adverse mental health symptoms among older adults remained less prevalent. 1 , 2 , 5 , 6 Although quota sampling and survey weighting were used, internet-based survey samples are limited and may not fully represent the 2020 US population. 1 Nonetheless, evidence of sustained adverse mental health symptoms among more than 5000 community-dwelling US adults highlights the need to promote preventive behaviors, expand mental health care access, and integrate medical and behavioral health services to mitigate the mental health effects of COVID-19.

Accepted for Publication: December 27, 2020.

Published: February 19, 2021. doi:10.1001/jamanetworkopen.2020.37665

Corresponding Author: Mark É. Czeisler, AB, Turner Institute for Brain and Mental Health, Monash University, 18 Innovation Walk, Clayton Campus, Level 5, Clayton, VIC 3800, Australia ( [email protected] ).

Author Contributions: Mr Czeisler had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: M. É. Czeisler, Lane, C. A. Czeisler, Howard, Rajaratnam.

Acquisition, analysis, or interpretation of data: M. É. Czeisler, Wiley, C. A. Czeisler, Howard, Rajaratnam.

Drafting of the manuscript: M. É. Czeisler, Lane.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: M. É. Czeisler, Wiley.

Obtained funding: M. É. Czeisler, Lane, C. A. Czeisler, Howard, Rajaratnam.

Administrative, technical, or material support: Lane, C. A. Czeisler, Rajaratnam.

Supervision: Howard, Rajaratnam.

Conflict of Interest Disclosures: Mr Czeisler reported receiving grants from the Australian-American Fulbright Commission administered through a 2020 Fulbright Future Scholarship funded by The Kinghorn Foundation during the conduct of the study and receiving personal fees from Vanda Pharmaceuticals outside the submitted work. Dr Czeisler reported receiving grants to support The COVID-19 Outbreak Public Evaluation (COPE) Initiative and grants from Brigham and Women's Physician's Organization during the conduct of the study; being a paid consultant to or speaker for Ganésco, Institute of Digital Media and Child Development, Klarman Family Foundation, M. Davis and Co, Physician's Seal, Samsung Group, State of Washington Board of Pilotage Commissioners, Tencent Holdings, Teva Pharma Australia, and Vanda Pharmaceuticals, in which Dr. Czeisler holds an equity interest; receiving travel support from Aspen Brain Institute, Bloomage International Investment Group, UK Biotechnology and Biological Sciences Research Council, Bouley Botanical, Dr Stanley Ho Medical Development Foundation, Illuminating Engineering Society, National Safety Council, Tencent Holdings, and The Wonderful Co; receiving institutional research and/or education support from Cephalon, Mary Ann and Stanley Snider via Combined Jewish Philanthropies, Harmony Biosciences, Jazz Pharmaceuticals PLC, Johnson and Johnson, Neurocare, Peter Brown and Margaret Hamburg, Philips Respironics, Regeneron Pharmaceuticals, Regional Home Care, Teva Pharmaceuticals Industries, Sanofi S.A., Optum, ResMed, San Francisco Bar Pilots, Schneider National, Serta, Simmons Betting, Sysco, Vanda Pharmaceuticals; being or having been an expert witness in legal cases, including those involving Advanced Power Technologies; Aegis Chemical Solutions; Amtrak; Casper Sleep; C and J Energy Services; Complete General Construction; Dallas Police Association; Enterprise Rent-A-Car; Steel Warehouse Co; FedEx; Greyhound Lines; Palomar Health District; PAR Electrical, Product, and Logistics Services; Puckett Emergency Medical Services; South Carolina Central Railroad Co; Union Pacific Railroad; UPS; and Vanda Pharmaceuticals; serving as the incumbent of an endowed professorship provided to Harvard University by Cephalon; and receiving royalties from McGraw Hill and Philips Respironics for the Actiwatch-2 and Actiwatch Spectrum devices. Dr Czeisler's interests were reviewed and are managed by the Brigham and Women's Hospital and Mass General Brigham in accordance with their conflict of interest policies. Dr Rajaratnam reported receiving institutional consulting fees from CRC for Alertness, Safety, and Productivity; Teva Pharmaceuticals; Vanda Pharmaceuticals; Circadian Therapeutics; BHP Billiton; and Herbert Smith Freehills; receiving grants from Teva Pharmaceuticals and Vanda Pharmaceuticals; and serving as chair for the Sleep Health Foundation outside the submitted work. No other disclosures were reported.

Funding/Support: Funding for survey data collection was supported in part by research grants from the US Centers for Disease Control and Prevention Foundation, with funding from BNY Mellon and from WHOOP, to Monash University acting through its Faculty of Medicine, Nursingy, and Health Sciences, and by institutional support from Philips Respironics and Alexandra Dane to Brigham and Women’s Hospital, the Turner Institute for Brain and Mental Health, Monash University, and Institute for Breathing and Sleep, Austin Hospital.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Additional Contributions: We gratefully acknowledge all survey respondents, as well as Laura K. Barger, PhD (Brigham and Women’s Hospital), Rebecca Robbins, PhD (Brigham and Women’s Hospital), and Elise R. Facer-Childs, PhD (Monash University) for their contributions to the survey instrument and study design, and Matthew D. Weaver, PhD (Brigham and Women’s Hospital) for his analytic advice. None of these individuals received compensation for their help. We also thank Mallory Colys, BSc (Qualtrics); Sneha Baste, BIndDes (Hons), (Qualtrics); Daniel Chong, BSc (Qualtrics); and Rebecca Toll, BMedSc, (Qualtrics), who were compensated as Qualtrics employees, for support in survey data collection.

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This paper is in the following e-collection/theme issue:

Published on 24.7.2024 in Vol 12 (2024)

Telehealth Uptake Among Hispanic People During COVID-19: Retrospective Observational Study

Authors of this article:

Di Shang 1 * , PhD ; Cynthia Williams 2 * , PhD ; Hera Culiqi 1 *

1 University of North Florida, Jacksonville, FL, United States

2 University of Central Florida, Orlando, FL, United States

*all authors contributed equally

Corresponding Author:

Cynthia Williams , PhD
University of Central Florida
528 W. Livingston Street Ste 401
Orlando , FL , 32801
United States
Phone: 1 321-276-6287
Email: [email protected]

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

1. Introduction

2. Literature review and research questions

3. Methodology

3.2 Data collection and preprocessing

3.3 Instrumentation

3.4 Ethics statement

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

4.2 Characteristics of online learning interaction amid COVID-19

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

4.3 Analyses of natural science courses and social science courses

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

5. Discussion

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

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

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

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

6. Conclusion and future work

A Systematic Review of the Research Topics in Online Learning During COVID-19: Documenting the Sudden Shift

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

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BRIEF RESEARCH REPORT article

1 Introduction

2 Literature review

3 Methodology

3.2 Study design and objectives

3.3 Participant and sampling

3.4 Formation experiment and data collection

3.5 Evaluation criteria

3.6 Analysis and implications

4.1 Survey findings

4.2 The formation experiment

4.3 Statistical significance of results

5 Conclusion

Data availability statement

Ethics statement

Author contributions

Conflict of interest

Publisher’s note

Transition to online learning during the COVID-19 pandemic

Paul Bazelais

Tenzin Doleck

1. Introduction

2. Background

3. Purpose of the study

3.1. Research question

4.1. Design

4.2. Procedure and participants

4.3. Measures

5. Analysis

5.2. Technology details

5.3. Engagement

5.4. Reaction

5.5. Standards outcomes

5.6. Academic progress

5.7. Learning

5.8. Qualitative analysis

6. Discussion

6.1. Limitations

6.2. Future directions