research paper about online learning chapter 1

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

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.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

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

• Other Journals

Chapter 1. Online Learning in K–12 and Higher Education and the Library Professional

Lucy Santos Green

E very once in a while, my colleagues across the fields of library and information science and instructional technology debate the growth and application of online (or, as it is sometimes referred to, distance) education. For those of us who are passionate about high-quality online learning, it often comes as a surprise that anyone might still be hesitant or against the creation of a fully online course. Dr. Marshall Jones, director of Graduate Studies in the College of Education at Winthrop University, South Carolina, explained his perception of this continued hesitation:

One of the reasons online learning has a bad reputation is that early iterations of it could be pretty crummy. And there are online classes today that are still pretty crummy. I like to point out that if you take an online class, and it is crummy, you don’t have enough good online classes to balance out the bad one. So rather than just calling that one crummy online class a bad class, people tend to define the delivery mode as bad. We’ve all had bad face-to-face classes. But we’ve had enough good ones to help balance out our perceptions. We don’t think of a bad face-to-face class as defining the whole delivery system. We just think of it as a bad class. 1

Whether we laud its growth or fear its presence, online learning is here to stay. In its most recent report, the Babson Survey Research Group shared that enrollment for distance education students has steadily increased every year for the past fourteen: “The most recent year-to-year addition of 337,016 distance education students, a 5.6 percent increase, exceeds the gains seen over the past three years.” 2 In its section describing on-campus students, the report emphasized the continued shift toward online learning: “The growth in the number of students taking only distance courses, coupled with the overall decline in the overall number of students enrolled, means that there are now over a million fewer students coming to campus in 2016 than there was [ sic ] in 2012. This decline has been present across all sectors of higher education.” 3 Before you assume this growth is due to private, for-profit entities, I’d like to highlight one more data point collected by the Babson Survey Research Group: almost 69 percent of all distance education students are enrolled in public institutions. 4

Distance or online education in K–12 settings is just as ubiquitous. Due to student needs for advanced placement courses, elective college courses, credit recovery, homebound placements, and homeschooling, online learning is now a part of K–12 school systems in all fifty states. 5 In addition, the growing popularity of technologies such as Schoology, Edmodo, and Google Classroom means that even in face-to-face K–12 classrooms, students must develop a comfort level with online coursework in order to fully participate and experience academic success. 6

For the library professional, this translates into making sure we are present and involved in multiple learning settings—face-to-face, online, hybrid, blended, flipped, and any number of other setting combinations. Unfortunately, little evidence exists that coursework for library and information science prepares candidates to design and assess online learning. 7 Many library professionals either pursue training in instructional design in the form of additional degrees or certifications or amass professional development in these areas, trying to keep up with an ever-changing list of learning management system characteristics, web-based tools, adaptive and assistive technologies, and institutional regulations.

Library professionals and LIS researchers have also amassed a body of work that contextualizes librarianship in the world of online learning, with specific focus on the librarian’s role as instructional designer and instructor. The Journal of Library and Information Services in Distance Learning claims that “the issues surrounding the delivery of library services to this population are sufficiently unique,” requiring its focus on this area starting in 2005. 8 Interestingly, as one peruses the articles in the journal, beginning at 2005 and moving toward present day, it is easy to identify a shift in the librarian’s attention, from a provider of resources to a collaborator and instructor:

Librarians’ roles have expanded from putting resources in students’ hands to helping students engage, evaluate and apply information in a rich educational landscape, much of it not even library-curated. Librarians need to be out on the front lines with other faculty developing educational environments instead of merely reacting when assistance is requested. . . . Efforts to be visible and proactive will help faculty develop direct associations between information literacy/critical literacy pedagogy and collaboration with librarians. 9

A parallel focus exists among school library researchers. In 2017, Lucy Santos Green, Stephanie Jones, and Panne Burke surveyed eighty-five school library preparation programs representing thirty-eight states, asking how these programs prepared candidates to deliver school library programs in fully online settings. 10 The national study concluded that while the preparation program coursework did not directly address fully online school librarians, graduate programs were aware of their importance and attempted to address the knowledge, skills, and dispositions needed through an emphasis on technology-enabled learning, web design, the teaching of digital literacy, and the development of candidates who continually pursued professional development after graduation.

In preparing this issue of Library Technology Reports on librarianship and online learning, I wanted to honor and continue the shift in focus from librarians as providers and curators of resources for learning to librarians as active designers and teachers in online spaces. Each chapter tackles an aspect of online pedagogy from this perspective, avoiding specific discussions of technology tools that might go away with next year’s budget, and instead prioritizing an examination of the affordances of those tools and learning needs librarians are tasked with addressing, regardless of our title and user population.

In chapter 2, Jennifer Banas and Russell Wartalski use radical change theory and its three principles—connectivity, interactivity, and access—as a guiding framework for librarians discussing how best to foster community in online learning spaces. Not content with merely addressing the design of online materials, Banas and Wartalski make a strong case for why intentional development of community is a goal that is just as, if not more, important for instructor librarians. Their section of this report introduces you to an ideal learning community and the course elements necessary to support its existence and growth.

Supporting our profession’s ethical and foundational belief in access for all, Heather Moorefield-Lang addresses accessibility in online course design in chapter 3 of this report. In a detailed look at the materials one might develop for an online space, Moorefield-Lang provides clear guidelines and suggestions for making each aspect of your online course accessible to all learners. Based on the principles of Universal Design for Learning, chapter 3 explains why accessible design benefits not only the fifty million individuals with learning differences, but all students on their learning journey.

Chapter 4 was written by Jered Borup and Leanna Archambault, two internationally recognized scholars on K–12 online learning and K–12 online teacher educator preparation. In a practical and resource-filled section, these authors describe the importance of personalized learning when online settings of any type or combination are designed and delivered to younger learners. Although many of the situations and resources discussed cater to K–12 educational settings, librarians in public libraries who work with children and young adults, as well as academic librarians working with incoming freshmen or first-generation college student populations, may also want to carefully review chapter 4.

In chapter 5, Tonia A. Dousay takes a library organization step-by-step through the process of selecting online settings and resources outside of expensive and official learning management systems. Prioritizing open educational resources, her chapter describes how online learning design needs can be met by both open source all-in-one solutions and build-a-system approaches. Using accessible terminology, Dousay discusses the importance of not only assessing needs, establishing goals, and selecting systems, but also selecting content and making linking decisions. For individuals who would like to develop online instruction but are facing limited budgets and resources, chapter 5 is an excellent resource.

Social media, in all of its glory, can be a wonderful platform or a librarian’s worst nightmare. Considering the pushback and ethical conundrums companies such as Facebook are facing, it is no surprise that one might hesitate to include social media tools when designing and supporting online learning. However, in chapter 6, Lucas John Jensen makes a strong case for why these tools, when appropriately selected and integrated, can lead to powerful learning experiences and community building among learners. His storytelling approach delightfully describes the best, the worst, and in-between of social media use in online teaching.

This report concludes with Ross A. Perkins’s treatise on assessment and evaluation of online learning in chapter 7. Differentiating between assessment and evaluation, and detailing how the application of each impacts every step a librarian might take in the online learning process, Perkins guides the reader through a logical and systematic approach for considering the quality of one’s design and its impact on student learning from a program perspective. For those who value evidence-based practice and data that supports instructional, as well as funding, decisions, his primer on assessment and evaluation of online learning is a must-read.

Online learning, whether in higher education, K–12, community, or work-training settings, is a unique learning venue with its own pedagogical and technological needs. It is my hope that you find this report to be informative and well-structured, enabling you to provide your constituents with well-designed and well-supported online instruction, filled with “opportunities for personal growth and participation on a global scale; opportunities to become agile, lifelong learners.” 11

• Marshall Jones, personal communication, February 7, 2019.
• Julia E. Seaman, I. Elaine Allen, and Jeff Seaman, Grade Increase: Tracking Distance Education in the United States (Oakland, CA: Babson Survey Research Group, 2018), 3.
• Seaman, Allen, and Seaman, Grade Increase , 26.
• Seaman, Allen, and Seaman, Grade Increase , 13.
• Leanna Archambault and Kathryn Kennedy, “Teacher Preparation for K–12 Online and Blended Learning,” in Handbook of Research on K–12 Online and Blended Learning , ed. Richard E. Ferdig and Kathryn Kennedy (Pittsburgh, PA: ETC Press, 2014), 225–44.
• Lucy Santos Green, Stephanie A. Jones, and Panne Andrea Burke, “School Librarians Fully Online: Preparing the Twenty-First Century Professional,” School Library Research 20 (2017).
• Green, Jones, and Burke, “School Librarians Fully Online.”
• “Aims and Scope,” Journal of Library and Information Services in Distance Learning , Taylor & Francis Online, accessed February 22, 2019, https://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=wlis20 .
• Rebecca Bliquez and Lynn Deeken, “Hook, Line and Canvas: Launching a Professional Development Program to Help Librarians Navigate the Still and Stormy Waters of Online Teaching and Learning,” Journal of Library and Information Services in Distance Learning 10, no. 3–4 (2016): 101.
• Green, Jones, and Burke, “School Libraries Fully Online.”
• Brenda Boyer, “Designer Librarian: Embedded in K–12 Online Learning,” TechTrends 59, no. 3 (2015): 76.
• There are currently no refbacks.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Wiley - PMC COVID-19 Collection

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

Lixiang yan.

1 Centre for Learning Analytics at Monash, Faculty of Information Technology, Monash University, Clayton VIC, Australia

Alexander Whitelock‐Wainwright

2 Portfolio of the Deputy Vice‐Chancellor (Education), Monash University, Melbourne VIC, Australia

Quanlong Guan

3 Department of Computer Science, Jinan University, Guangzhou China

Gangxin Wen

4 College of Cyber Security, Jinan University, Guangzhou China

Dragan Gašević

Guanliang chen, associated data.

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

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

Practitioner notes

What is already known about this topic

• Online learning has been widely adopted during the COVID‐19 pandemic to ensure the continuation of K‐12 education.
• Student success in K‐12 online education is substantially lower than in conventional schools.
• Students experienced various difficulties related to the delivery of online learning.

What this paper adds

• Provide empirical evidence for the online learning experience of students in different school years.
• Identify the different needs of students in primary, middle, and high school.
• Identify the challenges of delivering online learning to students of different age.

Implications for practice and/or policy

• Authority and schools need to provide sufficient technical support to students in online learning.
• The delivery of online learning needs to be customised for students in different school years.

INTRODUCTION

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

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

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

LITERATURE REVIEW

Learning conditions and technologies.

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

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

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

Learning experience and interactions

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

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

Learning expectations and age differentiation

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

Research questions

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

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

Participants

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

The number of students in each school year

Survey design

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

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

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

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

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

Learning conditions—RQ1

Learning media.

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

Learning media used by students in online learning

Learning approaches

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

Learning approaches used by students in online learning

Perceived benefits and obstacles—RQ2

Perceived benefits.

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

Perceived benefits of online learning reported by students

Perceived obstacles

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

Perceived obstacles of online learning reported by students

Expectations for future practices of online learning – RQ3

Online learning activities.

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

Students’ expected online learning activities

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

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

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

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

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

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

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

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

Implications

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

Technical support

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

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

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

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

Customisation of delivery methods

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

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

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

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

Limitations

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

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

CONFLICT OF INTEREST

There is no potential conflict of interest in this study.

ETHICS STATEMENT

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

Supporting information

Supplementary Material

ACKNOWLEDGEMENTS

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

SURVEY ITEMS

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

DATA AVAILABILITY STATEMENT

• Aesaert, K. , Van Nijlen, D. , Vanderlinde, R. , & van Braak, J. (2014). Direct measures of digital information processing and communication skills in primary education: Using item response theory for the development and validation of an ICT competence scale . Computers & Education , 76 , 168–181. 10.1016/j.compedu.2014.03.013 [ CrossRef ] [ Google Scholar ]
• Agung, A. S. N. , Surtikanti, M. W. , & Quinones, C. A. (2020). Students’ perception of online learning during COVID‐19 pandemic: A case study on the English students of STKIP Pamane Talino . SOSHUM: Jurnal Sosial Dan Humaniora , 10 ( 2 ), 225–235. 10.31940/soshum.v10i2.1316 [ CrossRef ] [ Google Scholar ]
• Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction . The International Review of Research in Open and Distributed Learning , 4 ( 2 ). 10.19173/irrodl.v4i2.149 [ CrossRef ] [ Google Scholar ]
• Appana, S. (2008). A review of benefits and limitations of online learning in the context of the student, the instructor and the tenured faculty . International Journal on E‐learning , 7 ( 1 ), 5–22. [ Google Scholar ]
• Bączek, M. , Zagańczyk‐Bączek, M. , Szpringer, M. , Jaroszyński, A. , & Wożakowska‐Kapłon, B. (2021). Students’ perception of online learning during the COVID‐19 pandemic: A survey study of Polish medical students . Medicine , 100 ( 7 ), e24821. 10.1097/MD.0000000000024821 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barbour, M. K. (2013). The landscape of k‐12 online learning: Examining what is known . Handbook of Distance Education , 3 , 574–593. [ Google Scholar ]
• Barbour, M. , Huerta, L. , & Miron, G. (2018). Virtual schools in the US: Case studies of policy, performance and research evidence. In Society for information technology & teacher education international conference (pp. 672–677). Association for the Advancement of Computing in Education (AACE). [ Google Scholar ]
• Barbour, M. K. , & LaBonte, R. (2017). State of the nation: K‐12 e‐learning in Canada, 2017 edition . http://k12sotn.ca/wp‐content/uploads/2018/02/StateNation17.pdf [ Google Scholar ]
• Barbour, M. K. , & Reeves, T. C. (2009). The reality of virtual schools: A review of the literature . Computers & Education , 52 ( 2 ), 402–416. [ Google Scholar ]
• Basuony, M. A. K. , EmadEldeen, R. , Farghaly, M. , El‐Bassiouny, N. , & Mohamed, E. K. A. (2020). The factors affecting student satisfaction with online education during the COVID‐19 pandemic: An empirical study of an emerging Muslim country . Journal of Islamic Marketing . 10.1108/JIMA-09-2020-0301 [ CrossRef ] [ Google Scholar ]
• Berge, Z. L. (2005). Virtual schools: Planning for success . Teachers College Press, Columbia University. [ Google Scholar ]
• Bickle, M. C. , & Rucker, R. (2018). Student‐to‐student interaction: Humanizing the online classroom using technology and group assignments . Quarterly Review of Distance Education , 19 ( 1 ), 1–56. [ Google Scholar ]
• Broadbent, J. , & Poon, W. L. (2015). Self‐regulated learning strategies & academic achievement in online higher education learning environments: A systematic review . The Internet and Higher Education , 27 , 1–13. [ Google Scholar ]
• Calderoni, J. (1998). Telesecundaria: Using TV to bring education to rural Mexico (Tech. Rep.). The World Bank. [ Google Scholar ]
• Cisco . (2018). Bandwidth requirements for meetings with cisco Webex and collaboration meeting rooms white paper . http://dwz.date/dpbc [ Google Scholar ]
• Cisco . (2019). Cisco digital readiness 2019 . https://www.cisco.com/c/m/en_us/about/corporate‐social‐responsibility/research‐resources/digital‐readiness‐index.html#/ (Library Catalog: www.cisco.com). [ Google Scholar ]
• Clark, C. , Strudler, N. , & Grove, K. (2015). Comparing asynchronous and synchronous video vs. text based discussions in an online teacher education course . Online Learning , 19 ( 3 ), 48–69. [ Google Scholar ]
• Claro, M. , Preiss, D. D. , San Martín, E. , Jara, I. , Hinostroza, J. E. , Valenzuela, S. , Cortes, F. , & Nussbaum, M. (2012). Assessment of 21st century ICT skills in Chile: Test design and results from high school level students . Computers & Education , 59 ( 3 ), 1042–1053. 10.1016/j.compedu.2012.04.004 [ CrossRef ] [ Google Scholar ]
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences . Routledge Academic. [ Google Scholar ]
• Corbi, A. , & Solans, D. B. (2014). Review of current student‐monitoring techniques used in elearning‐focused recommender systems and learning analytics: The experience API & LIME model case study . IJIMAI , 2 ( 7 ), 44–52. [ Google Scholar ]
• Dabbagh, N. , & Kitsantas, A. (2012). Personal learning environments, social media, and self‐regulated learning: A natural formula for connecting formal and informal learning . The Internet and Higher Education , 15 ( 1 ), 3–8. 10.1016/j.iheduc.2011.06.002 [ CrossRef ] [ Google Scholar ]
• Garrison, D. R. , Cleveland‐Innes, M. , & Fung, T. S. (2010). Exploring causal relationships among teaching, cognitive and social presence: Student perceptions of the community of inquiry framework . The Internet and Higher Education , 13 ( 1–2 ), 31–36. 10.1016/j.iheduc.2009.10.002 [ CrossRef ] [ Google Scholar ]
• Gašević, D. , Adesope, O. , Joksimović, S. , & Kovanović, V. (2015). Externally‐facilitated regulation scaffolding and role assignment to develop cognitive presence in asynchronous online discussions . The Internet and Higher Education , 24 , 53–65. 10.1016/j.iheduc.2014.09.006 [ CrossRef ] [ Google Scholar ]
• Gašević, D. , Zouaq, A. , & Janzen, R. (2013). “Choose your classmates, your GPA is at stake!” The association of cross‐class social ties and academic performance . American Behavioral Scientist , 57 ( 10 ), 1460–1479. [ Google Scholar ]
• Gikas, J. , & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media . The Internet and Higher Education , 19 , 18–26. [ Google Scholar ]
• Harvey, D. , Greer, D. , Basham, J. , & Hu, B. (2014). From the student perspective: Experiences of middle and high school students in online learning . American Journal of Distance Education , 28 ( 1 ), 14–26. 10.1080/08923647.2014.868739 [ CrossRef ] [ Google Scholar ]
• Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning . Research in Learning Technology , 23 . 10.3402/rlt.v23.26507 [ CrossRef ] [ Google Scholar ]
• Kuo, Y.‐C. , Walker, A. E. , Belland, B. R. , Schroder, K. E. , & Kuo, Y.‐T. (2014). A case study of integrating interwise: Interaction, internet self‐efficacy, and satisfaction in synchronous online learning environments . International Review of Research in Open and Distributed Learning , 15 ( 1 ), 161–181. 10.19173/irrodl.v15i1.1664 [ CrossRef ] [ Google Scholar ]
• Lee, S. J. , Srinivasan, S. , Trail, T. , Lewis, D. , & Lopez, S. (2011). Examining the relationship among student perception of support, course satisfaction, and learning outcomes in online learning . The Internet and Higher Education , 14 ( 3 ), 158–163. 10.1016/j.iheduc.2011.04.001 [ CrossRef ] [ Google Scholar ]
• Liu, F. , & Cavanaugh, C. (2012). Factors influencing student academic performance in online high school algebra . Open Learning: The Journal of Open, Distance and e‐Learning , 27 ( 2 ), 149–167. 10.1080/02680513.2012.678613 [ CrossRef ] [ Google Scholar ]
• Lou, Y. , Bernard, R. M. , & Abrami, P. C. (2006). Media and pedagogy in undergraduate distance education: A theory‐based meta‐analysis of empirical literature . Educational Technology Research and Development , 54 ( 2 ), 141–176. 10.1007/s11423-006-8252-x [ CrossRef ] [ Google Scholar ]
• Malik, M. , & Fatima, G. (2017). E‐learning: Students’ perspectives about asynchronous and synchronous resources at higher education level . Bulletin of Education and Research , 39 ( 2 ), 183–195. [ Google Scholar ]
• McInnerney, J. M. , & Roberts, T. S. (2004). Online learning: Social interaction and the creation of a sense of community . Journal of Educational Technology & Society , 7 ( 3 ), 73–81. [ Google Scholar ]
• Molnar, A. , Miron, G. , Elgeberi, N. , Barbour, M. K. , Huerta, L. , Shafer, S. R. , & Rice, J. K. (2019). Virtual schools in the US 2019 . National Education Policy Center. [ Google Scholar ]
• Montague, M. , & Rinaldi, C. (2001). Classroom dynamics and children at risk: A followup . Learning Disability Quarterly , 24 ( 2 ), 75–83. [ Google Scholar ]
• Montag, C. , Becker, B. , & Gan, C. (2018). The multipurpose application Wechat: A review on recent research . Frontiers in Psychology , 9 , 2247. 10.3389/fpsyg.2018.02247 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moore, M. G. (1989). Editorial: Three types of interaction . American Journal of Distance Education , 3 ( 2 ), 1–7. 10.1080/08923648909526659 [ CrossRef ] [ Google Scholar ]
• Muilenburg, L. Y. , & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study . Distance Education , 26 ( 1 ), 29–48. 10.1080/01587910500081269 [ CrossRef ] [ Google Scholar ]
• Muirhead, B. , & Juwah, C. (2004). Interactivity in computer‐mediated college and university education: A recent review of the literature . Journal of Educational Technology & Society , 7 ( 1 ), 12–20. [ Google Scholar ]
• Niemi, H. M. , & Kousa, P. (2020). A case study of students’ and teachers’ perceptions in a finnish high school during the COVID pandemic . International Journal of Technology in Education and Science , 4 ( 4 ), 352–369. 10.46328/ijtes.v4i4.167 [ CrossRef ] [ Google Scholar ]
• Oliver, K. , Osborne, J. , & Brady, K. (2009). What are secondary students’ expectations for teachers in virtual school environments? Distance Education , 30 ( 1 ), 23–45. 10.1080/01587910902845923 [ CrossRef ] [ Google Scholar ]
• Pardo, A. , Jovanovic, J. , Dawson, S. , Gašević, D. , & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback . British Journal of Educational Technology , 50 ( 1 ), 128–138. 10.1111/bjet.12592 [ CrossRef ] [ Google Scholar ]
• Ramij, M. , & Sultana, A. (2020). Preparedness of online classes in developing countries amid covid‐19 outbreak: A perspective from Bangladesh. Afrin, Preparedness of Online Classes in Developing Countries amid COVID‐19 Outbreak: A Perspective from Bangladesh (June 29, 2020) .
• Rice, K. L. (2006). A comprehensive look at distance education in the k–12 context . Journal of Research on Technology in Education , 38 ( 4 ), 425–448. 10.1080/15391523.2006.10782468 [ CrossRef ] [ Google Scholar ]
• Shishehchi, S. , Banihashem, S. Y. , & Zin, N. A. M. (2010). A proposed semantic recommendation system for elearning: A rule and ontology based e‐learning recommendation system. In 2010 international symposium on information technology (Vol. 1, pp. 1–5).
• Song, L. , Singleton, E. S. , Hill, J. R. , & Koh, M. H. (2004). Improving online learning: Student perceptions of useful and challenging characteristics . The Internet and Higher Education , 7 ( 1 ), 59–70. 10.1016/j.iheduc.2003.11.003 [ CrossRef ] [ Google Scholar ]
• Spitzer, D. R. (2001). Don’t forget the high‐touch with the high‐tech in distance learning . Educational Technology , 41 ( 2 ), 51–55. [ Google Scholar ]
• Thomas, R. M. (2000). Comparing theories of child development. Wadsworth/Thomson Learning. United Nations Educational, Scientific and Cultural Organization. (2020, March). Education: From disruption to recovery . https://en.unesco.org/covid19/educationresponse (Library Catalog: en.unesco.org)
• Uttal, D. H. , & Cohen, C. A. (2012). Spatial thinking and stem education: When, why, and how? In Psychology of learning and motivation (Vol. 57 , pp. 147–181). Elsevier. [ Google Scholar ]
• Van Lancker, W. , & Parolin, Z. (2020). Covid‐19, school closures, and child poverty: A social crisis in the making . The Lancet Public Health , 5 ( 5 ), e243–e244. 10.1016/S2468-2667(20)30084-0 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wang, C.‐H. , Shannon, D. M. , & Ross, M. E. (2013). Students’ characteristics, self‐regulated learning, technology self‐efficacy, and course outcomes in online learning . Distance Education , 34 ( 3 ), 302–323. 10.1080/01587919.2013.835779 [ CrossRef ] [ Google Scholar ]
• Wang, J. , & Antonenko, P. D. (2017). Instructor presence in instructional video: Effects on visual attention, recall, and perceived learning . Computers in Human Behavior , 71 , 79–89. 10.1016/j.chb.2017.01.049 [ CrossRef ] [ Google Scholar ]
• Wilkinson, A. , Roberts, J. , & While, A. E. (2010). Construction of an instrument to measure student information and communication technology skills, experience and attitudes to e‐learning . Computers in Human Behavior , 26 ( 6 ), 1369–1376. 10.1016/j.chb.2010.04.010 [ CrossRef ] [ Google Scholar ]
• World Health Organization . (2020, July). Coronavirus disease 2019 (COVID‐19): Situation Report‐164 (Situation Report No. 164). https://www.who.int/docs/default‐source/coronaviruse/situation‐reports/20200702‐covid‐19‐sitrep‐164.pdf?sfvrsn$=$ac074f58$_$2
• Yates, A. , Starkey, L. , Egerton, B. , & Flueggen, F. (2020). High school students’ experience of online learning during Covid‐19: The influence of technology and pedagogy . Technology, Pedagogy and Education , 9 , 1–15. 10.1080/1475939X.2020.1854337 [ CrossRef ] [ Google Scholar ]

Research on the Contributing Factors of Postgraduate Students’ Online Learning Experience

• Conference paper
• First Online: 02 February 2023
• Cite this conference paper

• Mengjie Zhang 6 &
• Feng Liu 6  

Part of the book series: Educational Communications and Technology Yearbook ((ECTY))

Included in the following conference series:

• Annual conference of Hong Kong Association for Educational, Communications and Technology

270 Accesses

In recent years, online learning has become a hot research issue in higher education. In order to gain insight into the actual situation of online learning for postgraduates, and explore the important factors that affect their online learning perceptions, this study adopts qualitative research methods and selects 13 graduate students of Chinese University as interview subjects, using the grounded theory to process and analyze the interview data. Research results show that the online learning experience of graduate students is jointly affected by the internal factors of the subject and external factors. Specifically, it mainly includes 11 key factors, which are teachers’ online teaching ability, teachers’ familiarity with technology, online course design, social interaction, network conditions, platform functions, assessment methods, course workload, intrinsic learning motivation, self-monitoring ability, and self-learning ability. Based on the research findings, we put forward four suggestions to improve the online learning experience of postgraduates.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Chen, W., Jia, W.: Study on contributing factors of college students’ online learning experience. J. East China Normal Univ. 38 (07) , 42–53 (2020). https://doi.org/10.13811/j.cnki.eer.2019.06.007

Glaser, B.G., Strauss, A.L.: Discovery of grounded theory: strategies for qualitative research. Routledge (2017). https://doi.org/10.4324/9780203793206

Article   Google Scholar  

He, C., Wang, Z., Lu, X.: Investigation and research on the learning experience of Chinese college students’ MOOCs. Distance Educ. China (11) , 42–49 (2016). CNKI:SUN:DDJY.0.2014-11-009

Google Scholar  

Hu, Y., Zhao, F.: Theoretical analysis model and measurement of online learning effectiveness. E-Educ. Res. 36 (10), 37–45 (2015). CNKI:SUN:DHJY.0.2015-10-008

Jiang, Y., Bai, X, Wu, W., Luo, X.: Analysis on the structural relationship of contributing factors of online learning experience. Modern Distance Educ. (01) , 27–36 (2019). NKI:SUN:YUAN.0.2019-01-004

Khalil, H., Ebner, M.: MOOCs completion rates and possible methods to improve retention - a literature review. In: Viteli, J., Leikomaa, M. (eds.) Proceedings of EdMedia 2014--World Conference on Educational Media and Technology, pp. 1305–1313. Association for the Advancement of Computing in Education (AACE), Tampere (2014). https://www.learntechlib.org/primary/p/147656/

Li, Y.: Experience: a discourse of pedagogy. Theory Pract. Educ. (12) , 1–5 (2001). CNKI:SUN:JYLL.0.2001-12-000

Liu, B., Zhang, W., Jiang, Y.: Online course learning experience: connotation, development and influencing factors. China Educ. Technol. (10), 90–96 (2016). https://doi.org/10.3969/j.issn.1006-9860.2016.10.016

Liu, S.: Research on online learning platform experience from the perspective of users. E-Educ. Res. (10) , 47–52 (2019). https://doi.org/10.13811/j.cnki.eer.2019.10.007

Murders, M.R.: A phenomenological study of the online education experiences of college students with learning disabilities. Doctoral dissertation, University of Arkansas. University of Arkansas ProQuest (2017). https://www.proquest.com/docview/1964264271

Paechter, M., Maier, B., Macher, D.: Students’ expectations of, and experiences in e-learning: their relation to learning achievements and course satisfaction. Comput. Educ. 54 (1), 222–229 (2010). https://doi.org/10.1016/j.compedu.2009.08.005

Shan, F., Liu, J.: Analysis of influencing factors of online learning experience of art courses. Technol. Educ. (1), 34–39 (2018). NKI:SUN:XJJS.0.2018-S1008

Udo, G.J., Bagchi, K.K., Kirs, P.J.: Using SERVQUAL to assess the quality of e-learning experience. Comput. Hum. Behav. 27 (3), 1272–1283 (2011). https://doi.org/10.1016/j.chb.2011.01.009

Yang, X., Zhou, H., Zhou, X., Hao, Z.: A review of domestic online course quality certification research. E-Educ. Res. 40 (06) , 50–57 (2019). https://doi.org/10.13811/j.cnki.eer.2019.06.007

Young, B.J.: Gender differences in student attitudes toward computers. J. Res. Comput. Educ. 33 (2), 204–216 (2000). https://doi.org/10.1080/08886504.2000.10782310

Zhang Pengcheng, L., Jiamei.: The definition and discrimination of the concept of experience. Psychol. Explor. 6 , 489–493 (2012). https://doi.org/10.3969/j.issn.1003-5184.2012.06.002

Download references

Acknowledgements

This work is very grateful for the cooperation of graduate students from Nanjing University of Posts and Telecommunications. Because of them, this research can collect effective data and be able to carry out research. At the same time, I also want to thank my mentor and classmates for their help, who assisted me in the design and implementation of the interview during my research.

Author information

Authors and affiliations.

Nanjing University of Posts and Telecommunications, Nanjing, China

Mengjie Zhang & Feng Liu

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Mengjie Zhang .

Editor information

Editors and affiliations.

HKAECT, Hong Kong, China

Anna Wing Bo TSO

Ming-Ai (London) Institute, London, UK

Steven Kwan Keung NG

Faculty of Arts, Centre for Applied English Studies, The University of Hong Kong, Pok Fu Lam, Hong Kong

The Education University of Hong Kong, Hong Kong, Hong Kong

Tiffany Shurui BAI

Interview questions

1. What do you think is the difference between the online learning environment and the offline course environment?

2. Can the online learning environment meet your learning needs?

3. Can you talk about your feelings about participating in online course learning activities?

4. What kind of experience did you get through online courses?

5. What aspects do you think will affect your online course learning experience?

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper.

Zhang, M., Liu, F. (2023). Research on the Contributing Factors of Postgraduate Students’ Online Learning Experience. In: TSO, A.W.B., NG, S.K.K., LAW, L., BAI, T.S. (eds) The Post-pandemic Landscape of Education and Beyond: Innovation and Transformation. HKAECT 2022. Educational Communications and Technology Yearbook. Springer, Singapore. https://doi.org/10.1007/978-981-19-9217-9_6

Download citation

DOI : https://doi.org/10.1007/978-981-19-9217-9_6

Published : 02 February 2023

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-9216-2

Online ISBN : 978-981-19-9217-9

eBook Packages : Education Education (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research