case study of online learning

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Open Access

Peer-reviewed

Research Article

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

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

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

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

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

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

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

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

Roles Conceptualization, Supervision, Writing – review & editing

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

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

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

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

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

Editor: Heng Luo, Central China Normal University, CHINA

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

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

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

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

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

1. Introduction

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

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

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

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

2. Literature review and research questions

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

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

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

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

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

3. Methodology

3.1 research context.

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

3.2 Data collection and preprocessing

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

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

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

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

3.3 Instrumentation

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

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

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

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

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

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

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

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

3.4 Ethics statement

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

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

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

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

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

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

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

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

4.2 Characteristics of online learning interaction amid COVID-19

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

4.3 Analyses of natural science courses and social science courses

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

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

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

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

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

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

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

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

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

5. Discussion

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

6. Conclusion and future work

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

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

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

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

Jessie s. barrot.

College of Education, Arts and Sciences, National University, Manila, Philippines

Ian I. Llenares

Leo s. del rosario, associated data.

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

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

Introduction

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

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

Literature review

Education and the covid-19 pandemic.

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

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

Related studies

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

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

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

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

Conceptual framework

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

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

Material and methods

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

Participants

This study involved 200 (66 male and 134 female) students from a private higher education institution in the Philippines. These participants were Psychology, Physical Education, and Sports Management majors whose ages ranged from 17 to 25 ( x ̅  = 19.81; SD  = 1.80). The students have been engaged in online learning for at least two terms in both synchronous and asynchronous modes. The students belonged to low- and middle-income groups but were equipped with the basic online learning equipment (e.g., computer, headset, speakers) and computer skills necessary for their participation in online classes. Table ​ Table1 1 shows the primary and secondary platforms that students used during their online classes. The primary platforms are those that are formally adopted by teachers and students in a structured academic context, whereas the secondary platforms are those that are informally and spontaneously used by students and teachers for informal learning and to supplement instructional delivery. Note that almost all students identified MS Teams as their primary platform because it is the official learning management system of the university.

Participants’ Online Learning Platforms

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

Instrument and data collection

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

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

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

Data analysis

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

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

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

The extent of students’ online learning challenges

Table ​ Table2 2 presents the mean scores and SD for the extent of challenges that students’ experienced during online learning. Overall, the students experienced the identified challenges to a moderate extent ( x ̅  = 2.62, SD  = 1.03) with scores ranging from x ̅  = 1.72 ( to some extent ) to x ̅  = 3.58 ( to a great extent ). More specifically, the greatest challenge that students experienced was related to the learning environment ( x ̅  = 3.49, SD  = 1.27), particularly on distractions at home, limitations in completing the requirements for certain subjects, and difficulties in selecting the learning areas and study schedule. It is, however, found that the least challenge was on technological literacy and competency ( x ̅  = 2.10, SD  = 1.13), particularly on knowledge and training in the use of technology, technological intimidation, and resistance to learning technologies. Other areas that students experienced the least challenge are Internet access under TSC and procrastination under SRC. Nonetheless, nearly half of the students’ responses per indicator rated the challenges they experienced as moderate (14 of the 37 indicators), particularly in TCC ( x ̅  = 2.51, SD  = 1.31), SIC ( x ̅  = 2.77, SD  = 1.34), and LRC ( x ̅  = 2.93, SD  = 1.31).

The Extent of Students’ Challenges during the Interim Online Learning

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

Impact of the pandemic on students’ online learning challenges

Another objective of this study was to identify how COVID-19 influenced the online learning challenges that students experienced. As shown in Table ​ Table3, 3 , most of the students’ responses were related to teaching and learning quality ( N  = 86) and anxiety and other mental health issues ( N  = 52). Regarding the adverse impact on teaching and learning quality, most of the comments relate to the lack of preparation for the transition to online platforms (e.g., S23, S64), limited infrastructure (e.g., S13, S65, S99, S117), and poor Internet service (e.g., S3, S9, S17, S41, S65, S99). For the anxiety and mental health issues, most students reported that the anxiety, boredom, sadness, and isolation they experienced had adversely impacted the way they learn (e.g., S11, S130), completing their tasks/activities (e.g., S56, S156), and their motivation to continue studying (e.g., S122, S192). The data also reveal that COVID-19 aggravated the financial difficulties experienced by some students ( N  = 16), consequently affecting their online learning experience. This financial impact mainly revolved around the lack of funding for their online classes as a result of their parents’ unemployment and the high cost of Internet data (e.g., S18, S113, S167). Meanwhile, few concerns were raised in relation to COVID-19’s impact on mobility ( N  = 7) and face-to-face interactions ( N  = 7). For instance, some commented that the lack of face-to-face interaction with her classmates had a detrimental effect on her learning (S46) and socialization skills (S36), while others reported that restrictions in mobility limited their learning experience (S78, S110). Very few comments were related to no effect ( N  = 4) and positive effect ( N  = 2). The above findings suggest the pandemic had additive adverse effects on students’ online learning experience.

Summary of students’ responses on the impact of COVID-19 on their online learning experience

Students’ strategies to overcome challenges in an online learning environment

The third objective of this study is to identify the strategies that students employed to overcome the different online learning challenges they experienced. Table ​ Table4 4 presents that the most commonly used strategies used by students were resource management and utilization ( N  = 181), help-seeking ( N  = 155), technical aptitude enhancement ( N  = 122), time management ( N  = 98), and learning environment control ( N  = 73). Not surprisingly, the top two strategies were also the most consistently used across different challenges. However, looking closely at each of the seven challenges, the frequency of using a particular strategy varies. For TSC and LRC, the most frequently used strategy was resource management and utilization ( N  = 52, N  = 89, respectively), whereas technical aptitude enhancement was the students’ most preferred strategy to address TLCC ( N  = 77) and TCC ( N  = 38). In the case of SRC, SIC, and LEC, the most frequently employed strategies were time management ( N  = 71), psychological support ( N  = 53), and learning environment control ( N  = 60). In terms of consistency, help-seeking appears to be the most consistent across the different challenges in an online learning environment. Table ​ Table4 4 further reveals that strategies used by students within a specific type of challenge vary.

Students’ Strategies to Overcome Online Learning Challenges

Discussion and conclusions

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

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

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

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

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

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

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

Authors’ contributions

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

No funding was received in the conduct of this study.

Availability of data and materials

Declarations.

The study has undergone appropriate ethics protocol.

Informed consent was sought from the participants.

Authors consented the publication. Participants consented to publication as long as confidentiality is observed.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Adarkwah MA. “I’m not against online teaching, but what about us?”: ICT in Ghana post Covid-19. Education and Information Technologies. 2021; 26 (2):1665–1685. doi: 10.1007/s10639-020-10331-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Almaiah MA, Al-Khasawneh A, Althunibat A. Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies. 2020; 25 :5261–5280. doi: 10.1007/s10639-020-10219-y. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Araujo T, Wonneberger A, Neijens P, de Vreese C. How much time do you spend online? Understanding and improving the accuracy of self-reported measures of Internet use. Communication Methods and Measures. 2017; 11 (3):173–190. doi: 10.1080/19312458.2017.1317337. [ CrossRef ] [ Google Scholar ]
• Barrot, J. S. (2016). Using Facebook-based e-portfolio in ESL writing classrooms: Impact and challenges. Language, Culture and Curriculum, 29 (3), 286–301.
• Barrot, J. S. (2018). Facebook as a learning environment for language teaching and learning: A critical analysis of the literature from 2010 to 2017. Journal of Computer Assisted Learning, 34 (6), 863–875.
• Barrot, J. S. (2020). Scientific mapping of social media in education: A decade of exponential growth. Journal of Educational Computing Research . 10.1177/0735633120972010.
• Barrot, J. S. (2021). Social media as a language learning environment: A systematic review of the literature (2008–2019). Computer Assisted Language Learning . 10.1080/09588221.2021.1883673.
• Bergen N, Labonté R. “Everything is perfect, and we have no problems”: Detecting and limiting social desirability bias in qualitative research. Qualitative Health Research. 2020; 30 (5):783–792. doi: 10.1177/1049732319889354. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Birks, M., & Mills, J. (2011). Grounded theory: A practical guide . Sage.
• Boelens R, De Wever B, Voet M. Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review. 2017; 22 :1–18. doi: 10.1016/j.edurev.2017.06.001. [ CrossRef ] [ Google Scholar ]
• Buehler MA. Where is the library in course management software? Journal of Library Administration. 2004; 41 (1–2):75–84. doi: 10.1300/J111v41n01_07. [ CrossRef ] [ Google Scholar ]
• Carter RA, Jr, Rice M, Yang S, Jackson HA. Self-regulated learning in online learning environments: Strategies for remote learning. Information and Learning Sciences. 2020; 121 (5/6):321–329. doi: 10.1108/ILS-04-2020-0114. [ CrossRef ] [ Google Scholar ]
• Cavanaugh CS, Barbour MK, Clark T. Research and practice in K-12 online learning: A review of open access literature. The International Review of Research in Open and Distributed Learning. 2009; 10 (1):1–22. doi: 10.19173/irrodl.v10i1.607. [ CrossRef ] [ Google Scholar ]
• Cicchetti D, Bronen R, Spencer S, Haut S, Berg A, Oliver P, Tyrer P. Rating scales, scales of measurement, issues of reliability: Resolving some critical issues for clinicians and researchers. The Journal of Nervous and Mental Disease. 2006; 194 (8):557–564. doi: 10.1097/01.nmd.0000230392.83607.c5. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Copeland WE, McGinnis E, Bai Y, Adams Z, Nardone H, Devadanam V, Hudziak JJ. Impact of COVID-19 pandemic on college student mental health and wellness. Journal of the American Academy of Child & Adolescent Psychiatry. 2021; 60 (1):134–141. doi: 10.1016/j.jaac.2020.08.466. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Day T, Chang ICC, Chung CKL, Doolittle WE, Housel J, McDaniel PN. The immediate impact of COVID-19 on postsecondary teaching and learning. The Professional Geographer. 2021; 73 (1):1–13. doi: 10.1080/00330124.2020.1823864. [ CrossRef ] [ Google Scholar ]
• Donitsa-Schmidt S, Ramot R. Opportunities and challenges: Teacher education in Israel in the Covid-19 pandemic. Journal of Education for Teaching. 2020; 46 (4):586–595. doi: 10.1080/02607476.2020.1799708. [ CrossRef ] [ Google Scholar ]
• Drane, C., Vernon, L., & O’Shea, S. (2020). The impact of ‘learning at home’on the educational outcomes of vulnerable children in Australia during the COVID-19 pandemic. Literature Review Prepared by the National Centre for Student Equity in Higher Education. Curtin University, Australia.
• Elaish M, Shuib L, Ghani N, Yadegaridehkordi E. Mobile English language learning (MELL): A literature review. Educational Review. 2019; 71 (2):257–276. doi: 10.1080/00131911.2017.1382445. [ CrossRef ] [ Google Scholar ]
• Fawaz, M., Al Nakhal, M., & Itani, M. (2021). COVID-19 quarantine stressors and management among Lebanese students: A qualitative study.  Current Psychology , 1–8. [ PMC free article ] [ PubMed ]
• Franchi T. The impact of the Covid-19 pandemic on current anatomy education and future careers: A student’s perspective. Anatomical Sciences Education. 2020; 13 (3):312–315. doi: 10.1002/ase.1966. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Garcia R, Falkner K, Vivian R. Systematic literature review: Self-regulated learning strategies using e-learning tools for computer science. Computers & Education. 2018; 123 :150–163. doi: 10.1016/j.compedu.2018.05.006. [ CrossRef ] [ Google Scholar ]
• Gonzalez T, De La Rubia MA, Hincz KP, Comas-Lopez M, Subirats L, Fort S, Sacha GM. Influence of COVID-19 confinement on students’ performance in higher education. PLoS ONE. 2020; 15 (10):e0239490. doi: 10.1371/journal.pone.0239490. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hew KF, Jia C, Gonda DE, Bai S. Transitioning to the “new normal” of learning in unpredictable times: Pedagogical practices and learning performance in fully online flipped classrooms. International Journal of Educational Technology in Higher Education. 2020; 17 (1):1–22. doi: 10.1186/s41239-020-00234-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Huang Q. Comparing teacher’s roles of F2F learning and online learning in a blended English course. Computer Assisted Language Learning. 2019; 32 (3):190–209. doi: 10.1080/09588221.2018.1540434. [ CrossRef ] [ Google Scholar ]
• John Hopkins University. (2021). Global map . https://coronavirus.jhu.edu/
• Kapasia N, Paul P, Roy A, Saha J, Zaveri A, Mallick R, Chouhan P. Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal. India. Children and Youth Services Review. 2020; 116 :105194. doi: 10.1016/j.childyouth.2020.105194. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kebritchi M, Lipschuetz A, Santiague L. Issues and challenges for teaching successful online courses in higher education: A literature review. Journal of Educational Technology Systems. 2017; 46 (1):4–29. doi: 10.1177/0047239516661713. [ CrossRef ] [ Google Scholar ]
• Khalil R, Mansour AE, Fadda WA, Almisnid K, Aldamegh M, Al-Nafeesah A, Al-Wutayd O. The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: A qualitative study exploring medical students’ perspectives. BMC Medical Education. 2020; 20 (1):1–10. doi: 10.1186/s12909-020-02208-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Matsumoto K. Introspection, verbal reports and second language learning strategy research. Canadian Modern Language Review. 1994; 50 (2):363–386. doi: 10.3138/cmlr.50.2.363. [ CrossRef ] [ Google Scholar ]
• Ng W. Can we teach digital natives digital literacy? Computers & Education. 2012; 59 (3):1065–1078. doi: 10.1016/j.compedu.2012.04.016. [ CrossRef ] [ Google Scholar ]
• Pham T, Nguyen H. COVID-19: Challenges and opportunities for Vietnamese higher education. Higher Education in Southeast Asia and beyond. 2020; 8 :22–24. [ Google Scholar ]
• Rasheed RA, Kamsin A, Abdullah NA. Challenges in the online component of blended learning: A systematic review. Computers & Education. 2020; 144 :103701. doi: 10.1016/j.compedu.2019.103701. [ CrossRef ] [ Google Scholar ]
• Recker MM, Dorward J, Nelson LM. Discovery and use of online learning resources: Case study findings. Educational Technology & Society. 2004; 7 (2):93–104. [ Google Scholar ]
• Roblek V, Mesko M, Dimovski V, Peterlin J. Smart technologies as social innovation and complex social issues of the Z generation. Kybernetes. 2019; 48 (1):91–107. doi: 10.1108/K-09-2017-0356. [ CrossRef ] [ Google Scholar ]
• Seplaki CL, Agree EM, Weiss CO, Szanton SL, Bandeen-Roche K, Fried LP. Assistive devices in context: Cross-sectional association between challenges in the home environment and use of assistive devices for mobility. The Gerontologist. 2014; 54 (4):651–660. doi: 10.1093/geront/gnt030. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Simbulan N. COVID-19 and its impact on higher education in the Philippines. Higher Education in Southeast Asia and beyond. 2020; 8 :15–18. [ Google Scholar ]
• Singh K, Srivastav S, Bhardwaj A, Dixit A, Misra S. Medical education during the COVID-19 pandemic: a single institution experience. Indian Pediatrics. 2020; 57 (7):678–679. doi: 10.1007/s13312-020-1899-2. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Singh V, Thurman A. How many ways can we define online learning? A systematic literature review of definitions of online learning (1988–2018) American Journal of Distance Education. 2019; 33 (4):289–306. doi: 10.1080/08923647.2019.1663082. [ CrossRef ] [ Google Scholar ]
• Spector P. Using self-report questionnaires in OB research: A comment on the use of a controversial method. Journal of Organizational Behavior. 1994; 15 (5):385–392. doi: 10.1002/job.4030150503. [ CrossRef ] [ Google Scholar ]
• Suryaman M, Cahyono Y, Muliansyah D, Bustani O, Suryani P, Fahlevi M, Munthe AP. COVID-19 pandemic and home online learning system: Does it affect the quality of pharmacy school learning? Systematic Reviews in Pharmacy. 2020; 11 :524–530. [ Google Scholar ]
• Tallent-Runnels MK, Thomas JA, Lan WY, Cooper S, Ahern TC, Shaw SM, Liu X. Teaching courses online: A review of the research. Review of Educational Research. 2006; 76 (1):93–135. doi: 10.3102/00346543076001093. [ CrossRef ] [ Google Scholar ]
• Tang, T., Abuhmaid, A. M., Olaimat, M., Oudat, D. M., Aldhaeebi, M., & Bamanger, E. (2020). Efficiency of flipped classroom with online-based teaching under COVID-19.  Interactive Learning Environments , 1–12.
• Usher M, Barak M. Team diversity as a predictor of innovation in team projects of face-to-face and online learners. Computers & Education. 2020; 144 :103702. doi: 10.1016/j.compedu.2019.103702. [ CrossRef ] [ Google Scholar ]
• Varea, V., & González-Calvo, G. (2020). Touchless classes and absent bodies: Teaching physical education in times of Covid-19.  Sport, Education and Society , 1–15.
• Wallace RM. Online learning in higher education: A review of research on interactions among teachers and students. Education, Communication & Information. 2003; 3 (2):241–280. doi: 10.1080/14636310303143. [ CrossRef ] [ Google Scholar ]
• World Health Organization (2020). Coronavirus . https://www.who.int/health-topics/coronavirus#tab=tab_1
• Xue, E., Li, J., Li, T., & Shang, W. (2020). China’s education response to COVID-19: A perspective of policy analysis.  Educational Philosophy and Theory , 1–13.

A Face-to-Face Approach to the Online Learning Space

A blended video studio and faculty office space elevates the online teaching and learning experience for instructors and students at Texas State University.

What We Faced

Digitalization in higher education has placed online learning on an evolutionary fast track. Untethered from classrooms and textbooks, institutions increasingly rely on digital resources to support effective instruction. The development of online learning models, which proliferated during the COVID-19 pandemic, experienced continued growth as a greater number of learners, instructors, and resources continued to take advantage of digitalization.

Despite the progress achieved with current models, however, most colleges and universities still struggle to offer consistently interactive and high-quality online courses. Often educators, students, and technology teams grapple separately with online learning challenges rather than working together to resolve them. Texas State University's Division of Information Technology is testing a different approach—a collaborative, human-first strategy poised to yield significant instructional and educational gains.

Texas State University's IT community began reimagining the university's distance learning landscape long before the world went virtual with the start of the pandemic in March 2020. With the prevalence of remote learning on the rise as early as 2010, Ken Pierce, the university's vice president for Information Technology and CIO, recalled wondering, "Wouldn't it be amazing if you actually had a wall with all your students on it, and as the instructor you could get the same fidelity as you would if you were in a classroom? And what if the wall were in your office? That could be a game changer, shifting faculty preference to online instruction." Footnote 1 For Pierce, delivery of quality online instruction is paramount to institutional success. While A/V teams and instructional designers historically focused on next-generation hybrid classrooms and learning management tools, Pierce focused on the instructor's ability to create an optimal online environment. Furthermore, he added, the solution "has to be affordable and scalable."

Over the next decade, as computing, video, and conferencing tools evolved, what once seemed a futuristic concept began to take shape. In 2018, the project impetus and platform capabilities aligned, allowing Pierce to begin working with the IT Division's Technology Innovation Office (TIO) and Learning Spaces Design team, and the potential future of Texas State's online teaching strategy officially launched. A charter was drawn up and approved, triggering formal planning meetings and the procurement of funding. The design and development of the pilot studio were slowed by the pandemic but progressed over the next several semesters.

What We Did

The Teaching Space of Tomorrow (TSOT) is a multifunctional space, incorporating an online delivery "studio" and a typical faculty office with a desk, shelves, and a computer. In other words, faculty "office" and teach from the same space. In conceptualizing the TSOT, the IT team used as its springboard the instructor's user experience. Carlos Solís, Associate VP for Technology Innovation and Director of TIO, put it simply: "An engaged instructor leads to an engaged learner." Pierce and Solís worked with Brian Shanks and Atkins Fleming of the Learning Spaces Design team and Gwen Morel of the Office of Distance and Extended Learning to set up a prototype teaching space. Next, TIO issued a university-wide call for teaching proposals, and the search for the space's first Instructor-in-Residence began. Designers knew the prototype staging area would afford the first Instructor-in-Residence an immediate opportunity to give authentic feedback. The success of the TSOT, its designers understood, depended on listening closely to the instructors who would engage with the platform.

Instructor-Centered Approach

The call for teaching proposals led to Seth Frei. Frei, a lecturing instructor for Texas State University's McCoy College of Business Department of Management, was eager to assume the role of Instructor-in-Residence. The recipient of Texas State's Office of Distance Learning's 2018 Award for Excellence in Online Teaching, Frei had consistently experienced success engaging online learners, having conducted hybrid and online courses since 2013. As Frei explained, "I've been in the Management Department for about five years now, and prior to that I was in Communication Studies. I've always taught either remote, hybrid, or fully online courses [at Texas State]. I've always really tried to push the envelope further and try new things through the classes I teach."

Continuous Feedback Loop

Frei's involvement brought valuable new insights to the initiative. In June 2021, Frei began exploring Shanks and Fleming's staged teaching space setup to think through an actual class. Displays, computers, lights, and working surfaces were mobile. Everything could be adjusted. Feedback from Frei's earlier experiences with online learning served as the starting point. As Solís explained, "The first design centered on Dr. Frei's teaching style. The focus was on the way he teaches, how he could be more engaged, and his pain points around the current use of Zoom."

A user-driven design and iterative approach guided the development. Shanks and Fleming addressed Frei's struggles with prior remote instruction, and their flexible solutions created a solid foundation for the teaching space. Fleming explained, "We prefer to start with requirements straight from whoever the end user will be." Shanks added, "Our requirements [were to] provide a technical solution to facilitate online synchronous classes in a way that makes the faculty experience as close to face-to-face as possible. Additionally, [we were asked to] fit this solution in a space like a faculty office so that it becomes scalable."

Beta Launch

In August 2021, the beta version of Texas State's TSOT launched. Streamlining the instructor's experience, the TSOT served as Frei's teaching studio and his office. Designing the TSOT to incorporate the lecturer's office simplified access to instructional materials and encouraged exploration of new teaching methods and techniques. Personalizing the audio and video setup enhanced the TSOT's instructor-centric design.

For the initiative to be deemed a success, Frei observed, "tech can't be a distraction." Shanks and Fleming had selected electronic components based on quality, longevity, adaptability, and ease of use. Technology used in the Teaching Space of Tomorrow is consumer-grade and user-friendly, making the space available, affordable, and scalable. Footnote 2 Shanks echoed Frei when discussing the space's technology: "We want it to feel as seamless and transparent as possible."

Engaging Students Face-to-Face

The student experience factors in, too. While noise from the air conditioner registered no viewer complaints, light reflections on Frei's reading glasses had to be resolved. As the design team focused on Frei's feedback, they kept in mind his goal of providing an intimate, engaging learning experience for students. Student eye contact and real-time responses have always been instructional priorities for Frei, and the configuration of the TSOT allowed him to reach those goals.

As Frei explained, "Where we position that camera [makes] it really easy to have good eye contact with the people you're talking with as they are sharing. I think that's important, as well, in a space—being able to feel like there's a direct connection between you and the person on the other end." Frei is pleased with the detailed student chat visible on his six screens and resulting interactions (see figure 1). "I can see quite a few of the comments that have been made, so I can start to … put things together. If you're on a small screen, you've only got maybe the last three or four comments that you can really see. I'm able to string ideas together and then reply to the class. I can put those ideas together to then better respond to how students are interacting with the class."

Student feedback validated the TSOT's prototype design. Participants' commentary from the fall 2021 semester was overwhelmingly positive. Students were extremely pleased with the sound and video quality, which they felt elevated their overall online experience, and student performance data trends indicated productive learner engagement. Senior public relations student Urvi Dalal, who took one of Frei's online courses in fall 2022, found significant benefit in the TSOT. Dalal said, "I've had online classes in the past that have worked well, but I think that the Teaching Space of Tomorrow adds just an extra layer of quality. Even though it's a class full of a hundred, two hundred people, I could directly have a conversation with the professor. It was a better experience than some of the other online classes that I've taken because the overall presentation quality helped retain my attention, which I believe helped me succeed in the class." As another student expressed, "This was the best online course I've ever taken. I felt like my instructor was in the room with me."

Throughout fall 2021, experimentation and revision continued. At the close of the fall semester, the full team, including Frei, took a deeper dive into the lessons learned during those first three months of beta testing. Their review session resulted in a design worthy of deployment in January 2022, launching the first-generation version of the TSOT. The spring semester saw very few changes to the space, and the design team is ready for the second Instructor-in-Residence to move in and pave the way for a next-generation design.

What We Learned

While human communication continues its whirlwind transformation, the fundamental need for personal interaction endures. Computer displays serve as both portals and barriers. Optimizing the former requires disrupting the latter.

• The TSOT dislodges boundaries digital screens impose by immersing, expanding, and joining the once classroom-bound learning community. Students and instructors can participate from nontraditional sites and far-reaching locations. Diverse learning cooperatives can thrive.
• By connecting virtually but also personally, lecturers and learners engage, share, and extend their learning in ways yet unimagined. As online learning revolutionizes higher education and transforms collaboration models for instructors and students, the TSOT is designed to capitalize on this evolution and further its progress.
• Enhancing an instructor's ability to produce this sense of exceptional quality, the TSOT blends the teaching studio and the faculty office . Colocation maximizes efficiency and efficacy. With no competing schedules to juggle, instructors are free to try new strategies when inspiration strikes. Personalized settings translate into time bonuses during and outside class periods. The merged space allows instructors to produce a high-quality online learning experience and achieve enhanced student outcomes.
• Besides optimizing time and results, the TSOT is simultaneously futuristic and economical . The current space pushes existing conferencing technology to its limits, yet it is flexible enough to adapt to evolving technologies. Strategic integration of hardware components and software design created a solution now costing less than $15,000 to replicate, making the space affordable and scalable.

When asked what advice he would give to the next Instructor-in-Residence, Frei first emphasized utilizing the Division of Information Technology's support network. "There's a team here working on this project that really wants to see this succeed," he said. Pierce, too, is already looking ahead. Having solved the feasibility and affordability puzzles, he is ready to move on to the next phase: scalability. His goal is to get the next instructor's perspective so "we have another set of things we can lock down and adjust." Pierce wants students to have a clear sense of exceptional quality when engaging in online learning, "so it's important for the professor to be able to produce that."

Just as the TSOT began with a desire to enhance the experience of the instructor, Pierce's plans for its future also begin with this key user. He hopes the next TSOT faculty members enjoy teaching from the space just as much as Frei did. The second Instructor-in-Residence brings yet another opportunity to gain valuable, actionable feedback. Beyond the next residency, Pierce's vision is clear: "Once we've perfected it here, how do we scale the Teaching Space of Tomorrow to online instructors across the university?" Pierce hopes the scalability of the TSOT will continue to engage instructors and students, regardless of geographic location, with its high-quality instructional delivery.

• All quotations in this article are from personal communications with the author. Jump back to footnote 1 in the text. ↩
• Brian Shanks, "Teaching Space of Tomorrow, Technical Specifications," Texas State University, n.d., accessed August 1, 2022. Jump back to footnote 2 in the text. ↩

Kimberly A. Conner is a Communications Specialist in the Office of the Vice President for Information Technology at Texas State University.

© 2022 Kimberly A. Conner. The text of this work is licensed under a Creative Commons BY-NC-ND 4.0 International License.

• DOI: 10.48009/1_iis_2007_160-166
• Corpus ID: 55991312

Effectiveness of Online Learning Program: A Case Study of A Higher Education Institution

• Hongjiang Xu , Omamerhi Ebojoh
• Published 2007
• Education, Computer Science

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A study of benefits and limitations of e learning - a learners perspective, an empirical study on effectiveness of e-learning over conventional class room learning – a case study with respect to online degree programmes in higher education, literature review on benefits, opportunities, challenges, prospects of online teaching in higher education, student's perception towards effectiveness of e-learning, perceptions towards the effectiveness of e-learning in private and public universities in uganda: a comparative study., e-learning: an enabler for better performance of management institutes, a study on perception of teachers and students toward online classes in dakshina kannada and udupi district, students’ perception towards new face of education during this unprecedented phase of covid-19 outbreak: an empirical study of higher educational institutions in saudi arabia, diseño de estrategia de aprendizaje para la licenciatura de diseño industrial bajo un enfoque de la educación 4.0, social interaction and effectiveness of the online learning - a moderating role of maintaining social distance during the pandemic covid-19, 18 references, mediators of the effectiveness of online courses, evaluating e-learning: a case study, designing and evaluating e-learning in higher education: a review and recommendations, active and interactive learning online: a comparison of web-based and conventional writing classes, effectiveness of e-learning course materials for learning database management systems: an experimental investigation, education research using web-based assessment systems, risks of e-education, an assessment of the effectiveness of e‐learning on university space planning and design, the role of personality in web-based distance education courses, can e-learning replace classroom learning, related papers.

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5 Benefits of Learning Through the Case Study Method

• 28 Nov 2023

While several factors make HBS Online unique —including a global Community and real-world outcomes —active learning through the case study method rises to the top.

In a 2023 City Square Associates survey, 74 percent of HBS Online learners who also took a course from another provider said HBS Online’s case method and real-world examples were better by comparison.

Here’s a primer on the case method, five benefits you could gain, and how to experience it for yourself.

Access your free e-book today.

What Is the Harvard Business School Case Study Method?

The case study method , or case method , is a learning technique in which you’re presented with a real-world business challenge and asked how you’d solve it. After working through it yourself and with peers, you’re told how the scenario played out.

HBS pioneered the case method in 1922. Shortly before, in 1921, the first case was written.

“How do you go into an ambiguous situation and get to the bottom of it?” says HBS Professor Jan Rivkin, former senior associate dean and chair of HBS's master of business administration (MBA) program, in a video about the case method . “That skill—the skill of figuring out a course of inquiry to choose a course of action—that skill is as relevant today as it was in 1921.”

Originally developed for the in-person MBA classroom, HBS Online adapted the case method into an engaging, interactive online learning experience in 2014.

In HBS Online courses , you learn about each case from the business professional who experienced it. After reviewing their videos, you’re prompted to take their perspective and explain how you’d handle their situation.

You then get to read peers’ responses, “star” them, and comment to further the discussion. Afterward, you learn how the professional handled it and their key takeaways.

HBS Online’s adaptation of the case method incorporates the famed HBS “cold call,” in which you’re called on at random to make a decision without time to prepare.

“Learning came to life!” said Sheneka Balogun , chief administration officer and chief of staff at LeMoyne-Owen College, of her experience taking the Credential of Readiness (CORe) program . “The videos from the professors, the interactive cold calls where you were randomly selected to participate, and the case studies that enhanced and often captured the essence of objectives and learning goals were all embedded in each module. This made learning fun, engaging, and student-friendly.”

If you’re considering taking a course that leverages the case study method, here are five benefits you could experience.

5 Benefits of Learning Through Case Studies

1. take new perspectives.

The case method prompts you to consider a scenario from another person’s perspective. To work through the situation and come up with a solution, you must consider their circumstances, limitations, risk tolerance, stakeholders, resources, and potential consequences to assess how to respond.

Taking on new perspectives not only can help you navigate your own challenges but also others’. Putting yourself in someone else’s situation to understand their motivations and needs can go a long way when collaborating with stakeholders.

2. Hone Your Decision-Making Skills

Another skill you can build is the ability to make decisions effectively . The case study method forces you to use limited information to decide how to handle a problem—just like in the real world.

Throughout your career, you’ll need to make difficult decisions with incomplete or imperfect information—and sometimes, you won’t feel qualified to do so. Learning through the case method allows you to practice this skill in a low-stakes environment. When facing a real challenge, you’ll be better prepared to think quickly, collaborate with others, and present and defend your solution.

3. Become More Open-Minded

As you collaborate with peers on responses, it becomes clear that not everyone solves problems the same way. Exposing yourself to various approaches and perspectives can help you become a more open-minded professional.

When you’re part of a diverse group of learners from around the world, your experiences, cultures, and backgrounds contribute to a range of opinions on each case.

On the HBS Online course platform, you’re prompted to view and comment on others’ responses, and discussion is encouraged. This practice of considering others’ perspectives can make you more receptive in your career.

“You’d be surprised at how much you can learn from your peers,” said Ratnaditya Jonnalagadda , a software engineer who took CORe.

In addition to interacting with peers in the course platform, Jonnalagadda was part of the HBS Online Community , where he networked with other professionals and continued discussions sparked by course content.

“You get to understand your peers better, and students share examples of businesses implementing a concept from a module you just learned,” Jonnalagadda said. “It’s a very good way to cement the concepts in one's mind.”

4. Enhance Your Curiosity

One byproduct of taking on different perspectives is that it enables you to picture yourself in various roles, industries, and business functions.

“Each case offers an opportunity for students to see what resonates with them, what excites them, what bores them, which role they could imagine inhabiting in their careers,” says former HBS Dean Nitin Nohria in the Harvard Business Review . “Cases stimulate curiosity about the range of opportunities in the world and the many ways that students can make a difference as leaders.”

Through the case method, you can “try on” roles you may not have considered and feel more prepared to change or advance your career .

5. Build Your Self-Confidence

Finally, learning through the case study method can build your confidence. Each time you assume a business leader’s perspective, aim to solve a new challenge, and express and defend your opinions and decisions to peers, you prepare to do the same in your career.

According to a 2022 City Square Associates survey , 84 percent of HBS Online learners report feeling more confident making business decisions after taking a course.

“Self-confidence is difficult to teach or coach, but the case study method seems to instill it in people,” Nohria says in the Harvard Business Review . “There may well be other ways of learning these meta-skills, such as the repeated experience gained through practice or guidance from a gifted coach. However, under the direction of a masterful teacher, the case method can engage students and help them develop powerful meta-skills like no other form of teaching.”

How to Experience the Case Study Method

If the case method seems like a good fit for your learning style, experience it for yourself by taking an HBS Online course. Offerings span eight subject areas, including:

• Business essentials
• Leadership and management
• Entrepreneurship and innovation
• Digital transformation
• Finance and accounting
• Business in society

No matter which course or credential program you choose, you’ll examine case studies from real business professionals, work through their challenges alongside peers, and gain valuable insights to apply to your career.

Are you interested in discovering how HBS Online can help advance your career? Explore our course catalog and download our free guide —complete with interactive workbook sections—to determine if online learning is right for you and which course to take.

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An evaluative case study of online learning for healthcare professionals

Affiliation.

• 1 University of Tasmania, Faculty of Education, Launceston, Tasmania, Australia. [email protected]
• PMID: 17004396
• DOI: 10.3928/00220124-20060901-04

Background: This evaluation study assessed the pedagogical and instructional design (e-pedagogy) effectiveness of online continuing professional education (CPE) courses offered by a large Australian CPE provider.

Methods: A naturalistic theory approach and a multilevel evaluation were used to examine the impact of web-based learning on more than 300 healthcare professionals. Participant satisfaction, learning achievement, self-reported practice performance change, and e-pedagogical courseware characteristics were assessed by various qualitative and quantitative data collection methods.

Results: Findings revealed that learning online was an effective means for increasing CPE knowledge (p < .05) and improving self-reported practice performance change (p < .05). Courses containing a clinical tool resulted in an increased self-reported practice performance change over courses that did not (Zobs = 3.757).

Conclusion: Online CPE offers a convenient format for healthcare professionals from educationally and geographically diverse populations to update their knowledge and view best practice.

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Learner Perspectives on Online Education

• According to new research, learners of all types are increasingly interested in flexibility and quality digital experiences. Working professionals who are looking to advance their careers and expand industry knowledge are particularly in favor of online learning.
• Although a significant proportion of AACSB schools offer online, hybrid, and multimodal formats across their degree and non-degree programs, face-to-face learning remains the predominant delivery method.
• Across all learner types, program cost, field of study, and flexibility are critical considerations for selecting educational programs.

This infographic incorporates insights from various thought leaders to create an engaging visual representation of learner views on online learning:

• AACSB data on student enrollment and program offerings collected through AACSB’s 2021–22 Business School Questionnaire Programs Module are available to members in DataDirect .
• Data on prospective business school students come from GMAC’s Prospective Students Survey—2022 Summary Report .
• Information on undergraduate and graduate students comes from Voice of the Online Learner 2022 , a Wiley research report.
• Insights on lifelong learners from the vantage point of employees seeking additional education come from The Future of Lifelong and Executive Education , a joint study by CarringtonCrisp and LinkedIn.
• The Salesforce Connected Student Report shares what students value as they prepare for the future of work.
• The Chronicle of Higher Education 's " Goals of Digital Transformation " infographic provides data on senior administrators’ views about the digital future.
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Report: Teaching Online Students to Self-Regulate Learning

New research suggests that short skill-building activities can motivate self-regulated learning among learners taking courses remotely.

By  Ashley Mowreader

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Online learners may need help learning self-regulated learning skills. Here are two online interventions that can support them.

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The availability of online courses has jumped since the COVID-19 pandemic forced remote instruction. In fall 2022, 30 percent of undergraduate students were enrolled in distance education classes exclusively and 60 percent were in at least one distance education course, according to data from the National Center for Education Statistics .

A recently published study from the Postsecondary Teaching and Technology Collaborative evaluates the effectiveness of two strategies to improve online community college students’ skills. Researchers found that exposure to reflection exercises and informational videos had a positive relationship with students’ engagement on the learning management system for planning and assessment.

The background: In online education, students’ ability to self-regulate their learning is critical because they’re often expected to work independently and exert control over their own learning journey, compared to face-to-face instruction, according to the report. Prior research has also pointed to a positive relationship between self-regulated learning and students’ outcomes in an online environment.

Self-regulated learning (SRL) skills include time management, metacognition and effort regulation. Interventions that promote these skills include online training modules, prompts, self-assessment and peer support opportunities.

The study, authored by researchers from Columbia University and SRI International, seeks to understand how learners from historically marginalized populations at broad-access postsecondary institutions respond to interventions, as they had yet to be studied.

The study: Researchers analyzed two strategies (videos and prompts) implemented across two semesters (fall 2022 and spring 2023) at a community college in northeastern U.S. categorized as a minority-serving institution. Three instructors participated in the experiment, each teaching multiple sections of one or more fully online STEM classes.

The first intervention, a three-part video series, consisted of short clips designed by educational researchers and instructors to introduce students to SRL in general and one specific skill to attempt. Each video addresses one SRL mindset or practice, some strategies to develop the skill, and an activity for the student to consider their own practices.

The prompts, or short-answer questions, asked learners to plan for the upcoming week, monitor their progress or reflect on their understanding of course concepts.

Instructors received materials and a suggested timeline for administrating the strategies, as well as encouragement to make adaptations according to their course content.

To evaluate the impact of the intervention, researchers examined students’ online behavior on the learning management system. The full sample included 231,462 actions (clickstream data from the LMS) performed by 141 students across 10 sections within four courses.

The results: Student data revealed a positive relationship between the videos/prompts and students’ SRL behavior, meaning viewing and engaging with the interventions pointed to improved learning skills. Students who engaged with both strategies had a strong correlation to engaging with course information. Those who worked with the prompts were more likely to participate in strategic planning and evaluation, and those who engaged with the video were also likely to improve strategic planning, as well as effort regulation.

Among student sub-groups, first-generation students had stronger improvements after being exposed to the prompts, compared to their continuing-generation peers, as did women compared to men (though less prominently).

Supporting Online Learner Success

For practitioners looking for best practices in distance education, some other areas to consider include:

• Accessibility for students with disabilities in course content and university websites.
• Peer connections for belonging and retention.
• Wraparound mental health care services that support online learner demographics.
• Financial aid tied to student learning behaviors.
• Universal design principles that center online education first.
• Motivational support for asynchronous learning.

So what? The preliminary study finds students exposed to learning skills and strategies were more likely to demonstrate skill-building behavior, aligning with existing literature and pointing to future evaluation of how students need assistance with preparing for the future as they navigate course tasks.

Additionally, students from historically marginalized backgrounds were more likely to respond well to the skill-building, suggesting that this could be one way to support equitable learning.

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In the future, researchers will expand their analysis to include additional data sources, institutions and iterations of the study.

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How Writing Wonders Builds Students’ Confidence With Acuity Scheduling

At Writing Wonders , the mission is clear: build confidence in young writers. This online writing service caters to a wide range of students ages 5 to 18, from those who are struggling to read and write to those who love crafting great stories. Their approach is all about making writing fun and engaging, offering instant correction, positive feedback, and helping kids develop lasting learning skills. But for some time, the behind the scenes work was getting in the way of their meaningful curriculum.

Seeking a solution for more teaching, less admin

Writing Wonders’ virtual class offerings are tailored to various needs and languages, including basic words and sentences and grammar and essays. In addition to small group work, students can also schedule private, one-on-one sessions with expert teachers or participate in a writer’s workshop to complete original works of literature.

Running such a diverse and dynamic program, Angie Marino, teacher and founder of Writing Wonders, found herself wearing many hats. “My main challenge was not having enough time in a day to communicate with parents and teach classes,” Marino says. “I continually had to send emails to parents reminding them when their children’s classes were, when new classes would begin, when payments were due, and so forth.”

She hoped that with the right tools, she could reduce the amount of effort spent on those back-and-forths and devote more energy to her classes. “I needed to minimize my administrative work so that I could concentrate more on what I love: teaching!” she says. “I wanted payments, scheduling, and reminders to be automated so that I wouldn’t have to spend time on those things.”

Discovering A+ automations in Acuity Scheduling

Acuity Scheduling brought a breath of fresh air to Writing Wonders and their operations. The scheduling software provides a centralized platform where they can showcase their online offerings and empower parents to book independently, freeing up Marino’s time.

“Acuity gave me a place to present all of the classes that we offer, include explanations and information, and embed the opportunity to sign up and schedule classes,” Marino says. “More and more parents are using these features and signing up for classes themselves. Enabling the parents to complete those tasks gives me more time and energy to focus on teaching.”

Before implementing the scheduling software, communicating with parents about their children’s classes took up a huge chunk of Marino’s day. But now, parents are notified automatically about schedules, new sessions, and payments due, relieving that administrative burden. “The automated emails in Acuity have really helped hold parents accountable,” Marino says. “The parents rely a great deal on those email reminders and the information embedded in them.”

Solving scheduling puzzles with subscriptions

One of the standout features for Writing Wonders is the ability to offer subscriptions and packages . With Acuity, they can book several month’s worth of classes quickly and efficiently, ensuring a smooth and continuous learning experience for the students. “I really enjoy scheduling recurring classes,” Marino says. “It takes seconds to do and takes care of administrative tasks for months.” Acuity also facilitates automated payments for these subscriptions, so Marino doesn’t have to worry about reaching out to charge for every class.

“ As soon as a parent registers for our monthly subscription, I schedule 8 to 36 classes out. It only takes me about 60 seconds to do, and that student and parent are set for 2 to 9 months straight. ”

This setup is mutually beneficial to Writing Wonders and their students, with subscribers receiving a 10% discount, guaranteed weekly spots, video recordings of missed classes, cohesive lesson plans, and more.

Keeping track of these classes across multiple teachers is also a breeze. With a convenient Monday morning email, Marino gets a quick look at all of the upcoming appointments for the week to ensure everyone is prepared and can make adjustments promptly. “Receiving that email on Monday starts my week off right,” Marino says. “It’s a chance to make sure everyone’s classes are set, and shows me if I’ve made any mistakes or if any changes need to be made.”

Lifelong benefits for lifelong learning

For Writing Wonders, Acuity Scheduling has become a vital tool in managing their thriving online writing program. By automating tasks and providing a user-friendly platform for parents and students to engage with, Writing Wonders has been able to deliver high-quality education. The scheduling software has helped streamline operations, improve communication, and enhance the overall experience for teachers, students, and parents alike.

Marino is confident that Acuity Scheduling was a smart choice for the business, one that will continue to support their mission. The time saved and the organizational benefits gained allow them to concentrate on what truly matters—setting students on a path to confident writing.

For teachers and tutors considering scheduling software for their own businesses, she emphasizes, “Acuity has absolutely saved me so much time.”

Tutorial: How to Set up New Staff Members

Plush tattoo chills out with acuity’s custom scheduling software.

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https://educationhub.blog.gov.uk/2024/08/06/when-is-results-day-2024-gcses-a-levels-t-levels-and-vtqs/

When is results day 2024? GCSEs, A levels, T Levels and VTQs

In August, pupils in England will find out their results for GCSEs, A levels, T Levels and VTQ (vocational technical qualifications) exams.  

Ahead of results day, schools, colleges and assessment centres should contact pupils directly to tell them how and when to collect them. They’ll also be able to answer any questions you have ahead of the day.  

Here’s what you need to know about exam results this year.

When is GCSE and Level 1/2 VTQ results day 2024?  

GCSE  results day is on Thursday 22 August.  

Results for Level 1, Level 1/2 and Level 2  VTQs  will also be available on or before this date.  

Normally, pupils will be able to go to their school or college and collect their results in person where they can get advice from their teachers.  

Alternatively, schools will send results to pupils in the post or by email.  

When is A level, T Level and Level 3 VTQs results days 2024?  

AS level, A level and  T Level  results day is on Thursday 15 August.  

Results for VTQs at Level 3 taken alongside or instead of A levels, such as BTECs, will be released to pupils on or before Thursday 15 August.  

Results can be emailed or sent in the post, but it’s a good idea to go into school or college to receive your results so you can get support from teachers and career advisers to discuss your options, especially if your results might affect your plans for September.  

If you’re applying to university via UCAS, you can track your  application online .  

How have exams been graded since the pandemic?  

Between 2019 and 2022, we saw a significant increase in the number of entries receiving top grades, due to disruption caused by the pandemic.  

Last year saw a return to pre-pandemic grading arrangements, and overall national results were similar to those of 2019. Ofqual have confirmed that they are continuing with normal grading this year.  

This is key to making sure exam qualifications are trusted – it means that universities and employers understand the performance of candidates, have confidence in their qualifications, and can use them to help them progress into the right opportunities.   

What should I do if I’m disappointed with my results?  

Your school or college and your teachers will support you if don’t get the results you hoped for or if your plans change based on the results you get.  

Remember, there are many different exciting options to take after school and college.  

If don’t get the GCSE results you were expecting, you can find out more about your options here .  

And if you’re worried about not getting the results you need for your university course, you can find out more about your options here .  

If you need help or advice around your exam results or next steps, you can call  the National Careers Service  helpline to chat to a careers adviser on 0800 100 900.  

If you’re feeling stressed or anxious about exams and you’re aged 18 or younger, you can also call Childline for free on 0800 1111 or  chat online  to get support.  

Ofqual has also created this practical guide for students on coping with exam pressure which offers advice and support on coping with exam anxiety and stress.

You may also be interested in:

• GCSE results day: What to do if you didn’t get the grades you were expecting
• A Level and T Level results day: What to do if you don’t get the grades you need for your university course
• Exam results: 5 tips for parents and carers on supporting your child with results day

Tags: A level results , A Level results day , A levels , GCSE results , GCSE results day , gcses , results day , T Level results day , VTQs , when is results day

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