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A SeNA-Based Study of In-Service Teachers' Interaction Patterns in an Online Learning Community

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Title: A SeNA-Based Study of In-Service Teachers' Interaction Patterns in an Online Learning Community
Language: English
Authors: Yixin Zhang (ORCID 0009-0005-5878-6813), Hui Zhang, Qi Wang (ORCID 0000-0001-9006-9914)
Source: Distance Education. 2025 46(3):514-546.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 33
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Communities of Practice, Electronic Learning, Computer Mediated Communication, Interaction, Faculty Development, Foreign Countries, Asynchronous Communication, Teacher Collaboration, Interpersonal Relationship, Graduate Students, Teachers
Geographic Terms: China (Beijing)
DOI: 10.1080/01587919.2024.2392689
ISSN: 0158-7919
1475-0198
Abstract: In-service teachers, constrained by work commitments, adopt online learning methods for their graduate studies, emphasizing the significance of exploring the construction of online learning communities. Analyzing collaborative conversation texts within the online learning process of the "Learning Science" course at a university in Beijing, this study employed social epistemic network analysis (SeNA) to investigate interaction patterns of in-service teachers and propose optimization strategies. The findings of this study show that in asynchronous discussions without intervention, the overall situation of collaborative conversations among in-service teachers tends to be low-level. The social interaction network shows polycentricity, categorizing members into core and peripheral interactors based on distinct social interaction characteristics, highlighting substantial disparities in epistemic networks. Core interactors prioritize engaging with others, yet their contributions primarily echo interactions, reflecting superficial knowledge dimensions. Conversely, specific peripheral interactors prioritize independent reflection, sharing content largely derived from personal experiences or knowledge, signifying a commitment to independent and in-depth thinking within deeper knowledge dimensions. Moreover, learners' online interactions are influenced by individual characteristics, instructional evaluation and the learning community atmosphere.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1482373
Database: ERIC
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  Value: <anid>AN0187593720;5f001aug.25;2025Sep01.03:17;v2.2.500</anid> <title id="AN0187593720-1">A SeNA-based study of in-service teachers' interaction patterns in an online learning community </title> <p>In-service teachers, constrained by work commitments, adopt online learning methods for their graduate studies, emphasizing the significance of exploring the construction of online learning communities. Analyzing collaborative conversation texts within the online learning process of the "Learning Science" course at a university in Beijing, this study employed social epistemic network analysis (SeNA) to investigate interaction patterns of in-service teachers and propose optimization strategies. The findings of this study show that in asynchronous discussions without intervention, the overall situation of collaborative conversations among in-service teachers tends to be low-level. The social interaction network shows polycentricity, categorizing members into core and peripheral interactors based on distinct social interaction characteristics, highlighting substantial disparities in epistemic networks. Core interactors prioritize engaging with others, yet their contributions primarily echo interactions, reflecting superficial knowledge dimensions. Conversely, specific peripheral interactors prioritize independent reflection, sharing content largely derived from personal experiences or knowledge, signifying a commitment to independent and in-depth thinking within deeper knowledge dimensions. Moreover, learners' online interactions are influenced by individual characteristics, instructional evaluation and the learning community atmosphere.</p> <p>Keywords: online learning communities; interaction patterns; social epistemic network analysis; in-service teachers; distance learning</p> <hd id="AN0187593720-2">Introduction</hd> <p>With the rising expectations of teacher competency, an increasing number of teachers are pursuing in-service education to improve their teaching abilities. Advancements in internet technology and the rise of distance education have rendered online learning a pivotal avenue for addressing the professional development needs of in-service learners. However, owing to the high work pressure on in-service teachers, the scheduling of online courses sometimes overlaps with their working hours. This work-study conflict sometimes forces learners to reduce their study time, often resulting in a high cognitive load. Moreover, learners often feel lonely and helpless due to the temporal and spatial separation between teachers and students in online education (McInnerney & Roberts, [<reflink idref="bib29" id="ref1">29</reflink>]). Therefore, it is essential to enhance the effectiveness of teacher-student and student-student interactions in the context of online learning communities (OLCs). This improvement can help reduce the cognitive load on distance learners and alleviate their sense of loneliness during the learning process.</p> <p>Several scholars have emphasized the critical role of establishing OLCs in enhancing student learning outcomes and mitigating feelings of isolation (Garrison, [<reflink idref="bib19" id="ref2">19</reflink>]; Overbaugh & Nickel, [<reflink idref="bib30" id="ref3">30</reflink>]; Peacock & Cowan, [<reflink idref="bib31" id="ref4">31</reflink>]). Nonetheless, in current teaching practices, the construction of OLCs primarily relies on methods such as random teacher groupings and spontaneous student formation, often lacking an organized and efficient construction mechanism. The ensuing problem is that members interact less efficiently and cannot complete learning tasks efficiently. Therefore, it is important to explore the construction patterns and mechanisms of OLCs to support online learning. An in-depth exploration of learner interaction patterns is a crucial aspect of research on their construction patterns and mechanisms. However, existing research has predominantly focused on the interaction patterns of full-time students and informal online training processes, with few studies exploring the online interaction patterns of in-service teachers pursuing master's degrees to alleviate the prominent work-study conflict experienced by them and to help improve their teaching abilities.</p> <p>An essential theoretical framework underlying "learning communities" is the Community of Practice (CoP), which is one of the most widely cited and influential social learning theories to date (Wenger, [<reflink idref="bib42" id="ref5">42</reflink>]). Emphasizing the practical and contextual nature of knowledge, Wenger posited that knowledge is concerned with competence in valuable endeavors. Therefore, knowing becomes the process of actively engaging in these pursuits and participating meaningfully in the world. Viewing learning as a form of social participation is a core tenet of the CoP theory, which emphasizes the critical role of collaboration and social interaction in the development of knowledge (Wenger, [<reflink idref="bib42" id="ref6">42</reflink>], pp. 3–5). CoP is defined as a "learning partnership among people who find it useful to learn from, and with each other about a particular domain. They use each other's experience of practice as a learning resource" (Wenger et al., [<reflink idref="bib45" id="ref7">45</reflink>], p. 9). In addition, the three important elements of a CoP are "mutual engagement," "a joint enterprise," and "shared repertoire" (Wenger, [<reflink idref="bib43" id="ref8">43</reflink>]). In summary, knowledge construction and social interaction are central to this theoretical framework. It is therefore important that research on the interaction patterns of OLCs focuses on individual social interaction relationships and cognitive structural characteristics.</p> <hd id="AN0187593720-3">Literature review</hd> <p></p> <hd id="AN0187593720-4">The concept of online learning communities and related research</hd> <p>A community is a group of individuals who are linked together for various reasons that define its boundaries (Schwier, [<reflink idref="bib34" id="ref9">34</reflink>]). Thus, people naturally form learning communities while learning together. In educational technology, OLCs have emerged as valuable tools for knowledge construction and collaborative learning. However, they are not mere extensions of physical communities into the digital sphere. As argued by Russell and Ginsburg, these communities are multidimensional and multilayered, fostering shared learning topics, collaborative discussions, and the formation of academic dialogue (Russell & Ginsburg, [<reflink idref="bib33" id="ref10">33</reflink>], p. 7). These characteristics, which are further highlighted by some researchers, can demonstrably lead to the development of new knowledge and understanding as individuals engage in active dialogue and knowledge exchange (Matusov et al., [<reflink idref="bib28" id="ref11">28</reflink>]). These two perspectives suggest that OLCs are not simply virtual versions of physical communities. They are unique entities with their own distinct characteristics and potential benefits.</p> <p>Globally, research on OLCs has yielded substantial results. In terms of research objects, in addition to focusing on the construction of formal OLCs for students, studies have also been conducted on the construction of learning communities for teachers' professional development (Clarke, [<reflink idref="bib12" id="ref12">12</reflink>]; Sümer, [<reflink idref="bib36" id="ref13">36</reflink>]; Zhou et al., [<reflink idref="bib49" id="ref14">49</reflink>]). Additionally, with the popularity of more and more social media platforms, some researchers have begun to focus on the construction and development of informal OLCs (Beins, [<reflink idref="bib5" id="ref15">5</reflink>]; Clough, [<reflink idref="bib13" id="ref16">13</reflink>]; Hudgins et al., [<reflink idref="bib20" id="ref17">20</reflink>]; Richards & Tangney, [<reflink idref="bib32" id="ref18">32</reflink>]; Ziegler et al., [<reflink idref="bib50" id="ref19">50</reflink>]). In terms of research content, many researchers have optimized the automatic feedback mechanisms in learning communities. For example, Thoms ([<reflink idref="bib38" id="ref20">38</reflink>]) studied the design, construction and implementation of a dynamic social feedback system to support higher education. Wang et al. ([<reflink idref="bib40" id="ref21">40</reflink>]) designed an automatic feedback scaffolding based on the principles of the Knowledge Construction Model.</p> <hd id="AN0187593720-5">Social epistemic network construction during online learning processes</hd> <p>Social network analysis (SNA) is a research methodology that measures the relationships between actors interacting in a social network (Wasserman & Faust, [<reflink idref="bib41" id="ref22">41</reflink>], p. 28). It can be utilized to visualize learner interactions and uncover underlying patterns within their network structures. However, SNA does not directly analyze the interaction content or the cognitive engagement of individual learners. Epistemic network analysis (ENA) is a relatively new analysis technique that was developed in part based on the SNA model. This method extends the scope of social network analysis by focusing on the relationships between concepts and ideas in the learner discourse. This method can effectively represent the complex cognitive structure of learners (Lang et al., [<reflink idref="bib24" id="ref23">24</reflink>], pp. 175–187), but it lacks the ability to thoroughly analyze relationships between individuals. Recognizing these limitations, some scholars have suggested combining SNA and ENA to form a method known as social epistemic network analysis (Swiecki & Shaffer, [<reflink idref="bib37" id="ref24">37</reflink>]), offering a new perspective for analyzing collaborative interactions in OLCs among learners.</p> <p>With the rapid development of information technology, online learning has become increasingly characterized by flexibility and dynamism. The social epistemic network analysis method combines the advantages of social network analysis and epistemic network analysis to proficiently visualize learning processes and further explore the potential correlation between the degree of learners' social interaction in the collaborative process and their cognitive structural characteristics. Existing research has utilized the social epistemic network analysis method to investigate the interaction behaviors among learners in OLCs. For instance, Wu et al. ([<reflink idref="bib46" id="ref25">46</reflink>]) used SNA and LSA (lag sequence analysis) to investigate the interaction patterns among postgraduates in different roles with varying levels of collaborative reflection. Researchers have used SNA and ENA to examine whether encouraging junior students to ask questions in asynchronous discussion forums contributes to the development of their systematic thinking (Yu et al., [<reflink idref="bib48" id="ref26">48</reflink>]). Moreover, Liu et al. ([<reflink idref="bib27" id="ref27">27</reflink>]) adopted SNA and ENA to explore the relationships among network characteristics, discussion topics, and the learning outcomes of undergraduate English majors within a course discussion forum.</p> <p>With the rise in distance education, an increasing number of in-service teachers are choosing to pursue graduate degrees online, resulting in a notable conflict between work and study. Existing research has predominantly focused on the interaction patterns of full-time students (Liu et al., [<reflink idref="bib27" id="ref28">27</reflink>]; Yu et al., [<reflink idref="bib48" id="ref29">48</reflink>]) and informal online training processes (Arnold & Paulus, [<reflink idref="bib2" id="ref30">2</reflink>]; Beins, [<reflink idref="bib5" id="ref31">5</reflink>]; Clough, [<reflink idref="bib13" id="ref32">13</reflink>]; Hudgins et al., [<reflink idref="bib20" id="ref33">20</reflink>]; Richards & Tangney, [<reflink idref="bib32" id="ref34">32</reflink>]; Ziegler et al., [<reflink idref="bib50" id="ref35">50</reflink>]). Few studies explored the online interaction patterns of in-service teachers pursuing master's degrees. Therefore, it is essential for researchers in this field to consider the prominent work-study conflict experienced by in-service teachers. By investigating their distance collaborative interaction patterns, researchers can develop targeted strategies to enhance the overall efficiency of distance collaboration for this specific learner group.</p> <p>Based on a course on "Learning Science" offered by a university in Beijing, this study analyzed the collaborative dialogue text data of in-service teachers in the learning process. Using the social epistemic network analysis method, we characterized and analyzed the interaction patterns of in-service learners in an online learning community (OLC). Four basic questions were addressed.</p> <p></p> <ulist> <item> What is the overall situation of collaborative conversations among learners during online learning?</item> <p></p> <item> What are the social interaction relationships of learners?</item> <p></p> <item> What are the cognitive structural characteristics of learners?</item> <p></p> <item> What factors influence the learner interaction patterns?</item> </ulist> <hd id="AN0187593720-6">Methodology</hd> <p></p> <hd id="AN0187593720-7">Participants and context</hd> <p>This study is based on an online course on "Learning Science" conducted by a university in Beijing in 2021, relying on the Learning Cell System (LCS, etc.edu.cn) for data collection and organization. The course focused on thematic learning and group collaborative problem-solving. The teacher designed 13 thematic activities around the core content of the subject and delivered the course through live online lectures and asynchronous discussions. We selected a learning topic from that semester displayed on the LCS and described the designed thematic activities (Appendix C) to clarify the research context.</p> <p>In 2021, a total of 69 students took this course, participated in the discussions on the LCS, and successfully completed the course (102 students participated in the platform discussions). Because the course is mainly taught online and the students taking the course are all in-service teachers pursuing master's degrees with generally high work pressure, a significant number of students were unable to complete all learning tasks and submit the final paper on time and often temporarily fail to complete the course successfully. Throughout the semester, they participated in discussion activities and submitted assignments to the LCS. Additionally, course feedback, personal evaluations, and intergroup peer assessments (Filius et al., [<reflink idref="bib17" id="ref36">17</reflink>]) were submitted. This study analyzed the collaborative dialogue text data of in-service teachers in the LCS to explore interaction patterns. Additionally, course feedback, personal evaluations, and intergroup peer assessments were used as supplementary data to help explain the factors influencing the formation of these interaction patterns.</p> <hd id="AN0187593720-8">Procedure</hd> <p>The research process is illustrated in Figure 1. This figure depicts the steps involved in the research process. The relevant research processes are as follows:</p> <p>PHOTO (COLOR): Figure 1. The research process.</p> <p></p> <ulist> <item> Data collection, cleaning and organization: Cleaning and filtering collaborative conversation texts and generating a matrix of learners' interactions based on the rules of interaction behavior analysis.</item> <p></p> <item> Social network analysis: Social network analysis of the matrix of learners' interactions. This analysis delves into internal structures, such as network centrality, uncovering interaction collaboration features and role characteristics displayed by learners during the interaction process. It visualizes the extent of learners' involvement in interactions. Furthermore, it selects learners with higher interaction levels ("core interactors") and those with lower interaction levels ("peripheral interactors") based on the social network analysis results during online learning processes.</item> <p></p> <item> Data coding: Based on the "dialogue analysis model in online collaborative learning" (Liu et al., [<reflink idref="bib26" id="ref37">26</reflink>]) and adapted appropriately, the online discussion texts are coded.</item> <p></p> <item> Data analysis and comparison: Based on the screening results of social network analysis and the coding of online collaborative discussion texts, frequency and epistemic network analyses of online discussion data were conducted to compare the frequency distribution and epistemic network characteristics between core and peripheral interactors.</item> <p></p> <item> Integrating the results of the data from multiple research perspectives, analyzing the underlying reasons, and proposing optimization strategies for the construction of OLCs, along with recommendations for online course design.</item> </ulist> <hd id="AN0187593720-9">Research tools</hd> <p>When conducting social network analysis, this study used UCINET Ver.6 for the overall network structure analysis and centrality analysis. For epistemic network analysis, the study first coded the online discussion texts using an adapted coding framework (as shown in Appendix A) and then employed the ENA website (https://<ulink href="http://www.epistemicnetwork.org/">www.epistemicnetwork.org/</ulink>) for epistemic network modeling. Excel and SPSS were used for data cleaning and statistical analyses.</p> <p>This study adapted the "dialogue analysis model in online collaborative learning" (Liu et al., [<reflink idref="bib26" id="ref38">26</reflink>]). From the perspective of cognitive psychology, evaluation and reflection can be considered cognitive processes that assess cognitive objects or metacognitive processes that evaluate and reflect on cognitive activities (Liu et al., [<reflink idref="bib26" id="ref39">26</reflink>]). In this study, the indicator "information evaluation" differs from that in the original framework, as it not only reflects the assessment of cognitive objects such as viewpoints and proposals but also highlights the evaluation and reflection on individual or social cognitive objects and cognitive activity processes. Therefore, the textual information related to "evaluation" was coded with multiple dimensions of "Individual Cognition," "Social Interaction," "Individual Metacognition," and "Social Metacognition."</p> <p>Furthermore, to underscore the influence of elements from both "individual" and "social" perspectives on knowledge construction within OLCs, we reconfigured the "Metacognitive" dialogues into "Individual Metacognitive" and "Social Metacognitive" levels. We designed a framework based on the following four dimensions: "Individual Cognition," "Social Interaction," "Individual Metacognition," and "Social Metacognition." The sub-coding indicators associated with "metacognition" continue to follow the original coding framework encompassing "metacognitive knowledge," "metacognitive experience," and "metacognitive skills." After completing the coding process, frequency statistics of the collaborative discourse text data were obtained, followed by an analysis of their characteristics and potential causes.</p> <p>However, this study had certain limitations. It focused solely on the learning stage throughout the semester, overlooking continuous scrutiny and exploration of interaction patterns and dynamic evolution mechanisms across different stages within the OLC.</p> <hd id="AN0187593720-10">Results</hd> <p></p> <hd id="AN0187593720-11">The overall situation of collaborative conversations among learners during online learning</hd> <p>A total of 102 students participated in discussion exchanges on the LCS, generating a total of 2246 posts throughout the semester. After encoding according to the coding framework, we analyzed the overall frequency distribution characteristics (Table 1). As can be seen from the Table 1, the majority of platform posts are centered on individual cognitive dialogues, followed by social interactive dialogues.</p> <p>Table 1. Descriptive statistics of collaborative conversation texts.</p> <p> <ephtml> <table><thead><tr><td>The first dimension</td><td>The second dimension</td><td>Code</td><td>Core interactors</td><td>Moderate interactors</td><td>Peripheral interactors</td><td>Total frequency</td><td>Percentage (%)</td></tr><tr><td>Frequency</td><td>Frequency</td><td>Frequency</td></tr></thead><tbody valign="top"><tr><td>Individual cognition</td><td>Information sharing</td><td>C1</td><td char=".">68</td><td char=".">364</td><td char=".">67</td><td char=".">499</td><td char=".">24.43</td></tr><tr><td>Information analysis</td><td>C2</td><td char=".">23</td><td char=".">129</td><td char=".">20</td><td char=".">172</td><td char=".">8.42</td></tr><tr><td>Information negotiation</td><td>C3</td><td char=".">86</td><td char=".">129</td><td char=".">5</td><td char=".">220</td><td char=".">10.77</td></tr><tr><td>Information modification</td><td>C4</td><td char=".">0</td><td char=".">2</td><td char=".">0</td><td char=".">2</td><td char=".">0.09</td></tr><tr><td>Information application</td><td>C5</td><td char=".">53</td><td char=".">185</td><td char=".">31</td><td char=".">269</td><td char=".">13.17</td></tr><tr><td>Information evaluation</td><td>C6</td><td char=".">205</td><td char=".">287</td><td char=".">16</td><td char=".">508</td><td char=".">24.87</td></tr><tr><td>Social interaction</td><td>Emotional expression</td><td>S1</td><td char=".">208</td><td char=".">239</td><td char=".">8</td><td char=".">455</td><td char=".">22.28</td></tr><tr><td>Open communication</td><td>S2</td><td char=".">9</td><td char=".">11</td><td char=".">2</td><td char=".">22</td><td char=".">1.07</td></tr><tr><td>Group cohesion</td><td>S3</td><td char=".">91</td><td char=".">148</td><td char=".">6</td><td char=".">245</td><td char=".">11.99</td></tr><tr><td>Individual metacognition</td><td>Metacognitive knowledge</td><td>M1</td><td char=".">59</td><td char=".">193</td><td char=".">35</td><td char=".">287</td><td char=".">14.05</td></tr><tr><td>Metacognitive experience</td><td>M2</td><td char=".">4</td><td char=".">12</td><td char=".">1</td><td char=".">17</td><td char=".">0.83</td></tr><tr><td>Metacognitive skills</td><td>M3</td><td char=".">10</td><td char=".">36</td><td char=".">9</td><td char=".">55</td><td char=".">2.69</td></tr><tr><td>Social metacognition</td><td>Metacognitive knowledge</td><td>SM1</td><td char=".">2</td><td char=".">4</td><td char=".">0</td><td char=".">6</td><td char=".">0.29</td></tr><tr><td>Metacognitive experience</td><td>SM2</td><td char=".">2</td><td char=".">5</td><td char=".">0</td><td char=".">7</td><td char=".">0.34</td></tr><tr><td>Metacognitive skills</td><td>SM3</td><td char=".">45</td><td char=".">206</td><td char=".">34</td><td char=".">285</td><td char=".">13.95</td></tr></tbody></table> </ephtml> </p> <p>In the process of asynchronous discussion in the LCS, social dialogue is not the main discourse form of this learning community. This suggests that during asynchronous discussions, there is less social interaction between learners, who tend to focus on enhancing individual cognition. Specifically, in the cognitive dialogues of this study, the relative proportions of discourse in terms of "Information sharing" (C1), "Information evaluation" (C6), and "Emotional expression" (S1), which are collectively referred to as low-level cognitive interactions, are relatively high. However, those for "Information analysis" (C2), "Information negotiation" (C3), "Information modification" (C4), and "Information application" (C5), collectively referred to as high-level cognitive interactions, are relatively low. The findings of this study are consistent with those of previous research (Chen et al., [<reflink idref="bib9" id="ref40">9</reflink>]; Faulconer et al., [<reflink idref="bib16" id="ref41">16</reflink>]), which suggests that learners in asynchronous discussions tend to engage in low-level cognitive interaction behaviors, making it difficult to reach higher cognitive levels.</p> <hd id="AN0187593720-12">Social interaction relationships of learners based on SNA</hd> <p>After cleaning and integrating the data, we found that 102 students participated in the LCS discussion, resulting in a total of 843 effective interactive text records. First, interaction behavior analysis rules were set (Table 2).</p> <p>Table 2. Rules of interaction behavior analysis.</p> <p> <ephtml> <table><thead><tr><td>Number</td><td>Definition of Interaction Behaviors</td><td>Data Analysis</td></tr></thead><tbody valign="top"><tr><td char=".">1</td><td>Learner A posted a comment without replying to anyone, received no responses, but was liked or disliked by someone.</td><td>Belonging to valid interaction data, included in the analysis, one like or dislike counts as one interaction.</td></tr><tr><td char=".">2</td><td>Learner A posted one or more comments to Learner B in the discussion forum, responding to an earlier viewpoint expressed by Learner B.</td><td>One interaction between A and B.</td></tr><tr><td char=".">3</td><td>Learner A posts one or more comments to Learner B in the discussion forum that responds to multiple of Learner B's earlier points.</td><td>Multiple interactions between A and B (responding to several points from learner B counts as several interactions).</td></tr><tr><td char=".">4</td><td>Learner A posted a comment without replying to anyone, received no response, and did not receive any likes or dislikes.</td><td>Not valid interaction data, not included in the analysis.</td></tr></tbody></table> </ephtml> </p> <p>Based on this, we used the initials of each student's real name in Chinese Pinyin to represent the learner in an Excel spreadsheet. We assigned numerical values in Arabic numerals to represent the number of valid interactions between learners, forming a matrix of learner interactions. Subsequently, the matrix was imported into the UCINET Ver. 6 software, and the social network analysis was carried out in terms of overall network structure and centrality.</p> <hd id="AN0187593720-13">Overall network structure analysis</hd> <p>Importing the learner interaction matrix into UCINET Ver. 6 generated an overall community network (Figure 2). It shows multiple learners (e.g., ZL, HZT, ZJ, PYJ, YHL, FXW, LJN, LX, KY, HPY, WP, WST, and ZD) with numerous and closely connected direct links to others, indicating frequent interactions and active engagement. They can be considered as active and enthusiastic participants.</p> <p>PHOTO (COLOR): Figure 2. Overall community network. *Network Centralization (Outdegree) = 4.720%; Network Centralization (Indegree) = 4.345%.</p> <p>Network Centralization indicates the tendency of a graph to be concentrated around certain nodes. The network centralization (outdegree) and network centralization (indegree) were 4.720% and 4.345%, respectively, indicating a slightly centralized tendency (Liu, [<reflink idref="bib25" id="ref42">25</reflink>], p. 127–152). This further reflects the polycentricity of the OLCs' social interaction network, illustrating the absence of a single "authoritative figure" as the center for information exchange within the network. Instead, the network presents a structure is centered on multiple "core interactors."</p> <hd id="AN0187593720-14">Centrality analysis</hd> <p>This section focuses on the nodal outdegree and indegree of this OLC. Combining Wasserman and Faust's explanations of nodal indegree and outdegree (Wasserman & Faust, [<reflink idref="bib41" id="ref43">41</reflink>], pp. 125–128), in this study, a high outdegree signifies that the learners can actively respond to other learners and engage in topic interactions. A high indegree means that the learner can initiate topics with substantial discussion value, thereby eliciting active responses from other learners and promoting in-depth discussion topics within the community.</p> <p>Some researchers argue that core participants in a community only account for 10-15% of the group, while the majority of participants are concentrated in the periphery of the social interaction network and seldom participate in collaborative activities (Wenger et al., [<reflink idref="bib44" id="ref44">44</reflink>], p. 56). Therefore, we hypothesized that the interaction structure of the OLC in this study is similar to that discussed by Wenger et al. ([<reflink idref="bib44" id="ref45">44</reflink>]). In this study, a total of 102 students participated in the discussion on the learning platform. The number of core participants was estimated to range from 10 to 15. Based on the overall community network graph and degree centrality, the top 13 learners in terms of both outdegree and indegree are regarded as "core interactors" who foster direct and close interactive relationships with other learners in the community (as depicted in Table 3). This table indicates that, in addition to the prominently displayed "core interactors" in the overall network community graph (ZL, HZT, ZJ, PYJ, YHL, FXW, LJN, LX, KY, HPY, WP, WST, and ZD), WP also exhibits similarly high outdegree and indegree, categorizing him/her as a "core interactor" as well. Excluding the three students who did not meet the final course requirements, there are a total of 11 "core interactors" in this learning community.</p> <p>Table 3. Top 13 learners' outdegree and indegree (Red font indicates that both outdegree and indegree are high.).</p> <p> <ephtml> <table><thead><tr><td>Name</td><td>Outdegree</td><td>Name</td><td>Indegree</td></tr></thead><tbody valign="top"><tr><td>HZT</td><td char=".">46.00</td><td>YHL</td><td char=".">43.00</td></tr><tr><td>PYJ</td><td char=".">36.00</td><td>WP</td><td char=".">35.00</td></tr><tr><td>ZL</td><td char=".">34.00</td><td>HC</td><td char=".">23.00</td></tr><tr><td>WFX</td><td char=".">33.00</td><td>ZL</td><td char=".">22.00</td></tr><tr><td>WP</td><td char=".">32.00</td><td>ZTY</td><td char=".">22.00</td></tr><tr><td>ZD</td><td char=".">31.00</td><td>WSS</td><td char=".">21.00</td></tr><tr><td>YHL</td><td char=".">30.00</td><td>LY</td><td char=".">19.00</td></tr><tr><td>HPY</td><td char=".">28.00</td><td>WLJ</td><td char=".">19.00</td></tr><tr><td>WP</td><td char=".">26.00</td><td>ZD</td><td char=".">18.00</td></tr><tr><td>FXW</td><td char=".">23.00</td><td>KY</td><td char=".">18.00</td></tr><tr><td>LX</td><td char=".">22.00</td><td>ZXJ</td><td char=".">18.00</td></tr><tr><td>GLL</td><td char=".">22.00</td><td>ZJJ</td><td char=".">18.00</td></tr><tr><td>WST</td><td char=".">21.00</td><td>PYJ</td><td char=".">17.00</td></tr></tbody></table> </ephtml> </p> <p>Similarly, referring to the perspectives of Wenger et al. ([<reflink idref="bib44" id="ref46">44</reflink>]) and considering the outdegree and indegree of each learner obtained from this study, 36 learners with extremely low outdegrees and indegrees were identified from the bottom 48 rankings. By integrating the overall network community graph and excluding students who did not meet the final course requirements, 11 learners with very low interaction levels (FJQ, HZY, LXS, LY, TFJ, WCL, XYH, YZ, ZJY, ZXW, and ZY) were filtered and identified as "peripheral interactors."</p> <hd id="AN0187593720-15">Cognitive structural characteristics of learners based on ENA</hd> <p>Some scholars contend that by integrating social network analysis with content analysis methods, a more nuanced understanding of the nature and characteristics of interactions among community members can be gained (Cela et al., [<reflink idref="bib7" id="ref47">7</reflink>]). To comprehensively explore the dynamics of the interaction and the cognitive development features of these learners, this study utilized social epistemic network analysis. Specifically, it identified and classified learners based on their level of interaction, distinguishing between "core interactors"—those with a higher degree of interaction—and "peripheral interactors"—those with a lower degree of interaction during online learning. Subsequently, the study analyzed the learning interactions of these two categories of learners by examining both their behavioral and content characteristics during the learning process.</p> <hd id="AN0187593720-16">Epistemic network model construction</hd> <p>We gathered data from student discussion forums in the Learning Cell System. After cleaning and organizing the data, 2246 valid textual entries were identified. Subsequently, the discussion data for Lecture 7 of the Learning Science course in 2021, constituting approximately 30% of the entire dataset, underwent independent coding by two researchers using a predefined framework. Cohen's kappa coefficient for coding consistency yielded a highly satisfactory result of 0.945, exceeding the threshold of 0.8 and thus indicating robust coding reliability. Building on this positive outcome, the two coders collaborated to extend their coding efforts to cover the remaining 70% of the dataset. The coding process involved 11 discussions related to instructional topics, including course feedback, individual assessments, and the reciprocal evaluations of group members. Following the thorough coding of all collaborative conversation text data, this study developed an epistemic network model for the coding of learner discourse (as depicted in Figure 3). In Figure 3, the Pearson and Spearman coefficients for the X-axis dimension are both 0.99, and those for the Y-axis dimension are both 0.99, indicating that the epistemic network model is a good fit.</p> <p>Graph: Figure 3. Epistemic network model.</p> <p>The elements proximate to the Y-axis consist of M1 (individual metacognitive knowledge), C5 (information application), S2 (open communication), SM2 (social metacognitive experience), SM3 (social metacognitive skills), and SM1 (social metacognition knowledge). Notably, M1 and SM1 are positioned distantly from each other, signifying substantial differences between these two elements. C5 can be construed as a reflection on the practical application of knowledge. Collectively, SM1, SM2, and SM3 belong to the social metacognition dimension. Consequently, "individual metacognition" and "social metacognition" were designated at the respective ends of the Y-axis.</p> <p>The elements proximate to the X-axis consist of S3 (group cohesion), M3 (individual metacognitive skills), C5 (information application), S2 (open communication), SM2 (social metacognitive experience), and C1 (information sharing). Notably, S3 and C1 are distantly positioned, indicating substantial differences between these elements. M3 and C5 can be interpreted as the application of information and demonstration of skills relevant to individual performance in group tasks. Additionally, SM2 is linked to the emotional experiences in the interaction among group members. Consequently, the designations of "Social Interaction" and "Individual Cognition" have been respectively assigned to the two ends of the X-axis.</p> <p>Combining the definitions of the X- and Y-axes, this study further categorized the first quadrant as the "Individual Cognition-Individual Metacognition" dimension, the second quadrant as the "Social Interaction-Individual Metacognition" dimension, the third quadrant as the "Social Interaction-Social Metacognition" dimension, and the fourth quadrant as the "Individual Cognition-Social Metacognition" dimension.</p> <hd id="AN0187593720-17">Centroid position characterization of online learners</hd> <p>The centroid is a microcosm of the epistemic network, representing the connectivity structure in a particular arrangement of the network model. Figure 4 depicts the centroid of the epistemic network for the two distinct learner types, with core interactors presented in blue in the ENA space and peripheral interactors shown in purple in this space. The squares denote the average centroid of the epistemic network structure for each learner type, and the dashed boxes indicate the 95% confidence intervals.</p> <p>PHOTO (COLOR): Figure 4. Epistemic network centroid (blue for core interactors and purple for peripheral interactors).</p> <p>Figure 4 illustrates the disparate positions of the epistemic network centroids for the two types of learners, indicating substantial differences in their respective epistemic network structures. Examining the distribution of epistemic network centroids reveals that the centroid of core interactors is situated within the "Social Interaction-Social Metacognition" dimension, with its confidence interval primarily encompassing the dimensions of "Social Interaction-Individual Metacognition" and "Social Interaction-Social Metacognition." In contrast, the centroid of peripheral interactors is located in the "Individual Cognition-Individual Metacognition" dimension, and its confidence intervals Predominantly cover the "Individual Cognition-Individual MetaCognition" and "Individual Cognition-Social Metacognition" dimensions.</p> <p>To address the disparities in the epistemic networks between the two learner types with statistical significance, we conducted a t-test to examine differences in the mean centroids of their respective epistemic networks. The results revealed a significant difference in the X dimension (<emph>t</emph> = −6.72, Effect Size = 2.87, <emph>p</emph> =.00 <.05) (Table 4).</p> <p>Table 4. A two sample T-test assuming unequal variance.</p> <p> <ephtml> <table><thead><tr><td /><td>X Axis</td><td>Y Axis</td></tr><tr><td>Mean</td><td>SD</td><td>Cohen's d</td><td><italic>t</italic> (19.57)</td><td><italic>p</italic></td><td>Mean</td><td>SD</td><td>Cohen's d</td><td><italic>t</italic> (15.96)</td><td><italic>p</italic></td></tr></thead><tbody valign="top"><tr><td>Core interactors</td><td char=".">−0.30</td><td char=".">0.19</td><td char=".">2.87</td><td char=".">−6.72</td><td char=".">.00</td><td char=".">−0.04</td><td char=".">0.33</td><td char=".">0.40</td><td char=".">−0.93</td><td char=".">.37</td></tr><tr><td>Peripheral interactors</td><td char=".">0.28</td><td char=".">0.22</td><td char=".">0.06</td><td char=".">0.19</td></tr></tbody></table> </ephtml> </p> <p>Consequently, the core and peripheral interactors exhibited distinct characteristics in their learning activities. Subsequent analyses revealed that the online discussion discourse of core interactors displayed a bias towards socially pertinent dimensions. The predominant content comprised emotional expression, evaluation of others' perspectives, and knowledge reflection rooted in collaborative efforts within the learning community. Conversely, the online discussion discourse of marginal interactors leaned towards individual-related dimensions, focusing primarily on information sharing related to discussion topics and reflecting on existing knowledge and experiences.</p> <hd id="AN0187593720-18">Epistemic network characterization of online learners</hd> <p>To further explore the epistemic network structures of the two learner types and elucidate the distinctions between them, this study conducted a separate analysis of the epistemic networks for each type of learner.</p> <p>Figure 5 illustrates the epistemic network of core interactors, revealing a predominant focus on two dimensions: information evaluation and the expression of emotion. The connection between elements indicates that core interactors fall within categories such as "information sharing-information evaluation," "emotional expression-group cohesion," "information sharing-emotional expression," "information application-emotional expression," "information evaluation-emotional expression," and "emotional expression-individual metacognitive knowledge," each characterized by strong connection strength.</p> <p>PHOTO (COLOR): Figure 5. Epistemic network of core interactors.</p> <p>Core interactors excel in communication during online learning, often adopting a perspective-learning approach. They express their viewpoints directly, for example, by agreeing or disagreeing. Furthermore, they evaluate and contribute to others' perspectives based on their personal experiences and knowledge. Therefore, continuous knowledge enhancement through interactive learning is notable. An analysis of the core interactors' viewpoints revealed a high frequency of interaction, possibly driven by individual motivations. Appendix B (Table B1) integrates the content published on the platform by the core interactors (only some of them are intercepted owing to space limitations). The table illustrates that given the online format of the course and the absence of a foundational background in psychology or pedagogy for many participants, some learners, driven by the motivation to self-improve, actively explore and assess the views of others in discussions. This proactive engagement serves as a compensatory mechanism to address gaps in students' learning within the course domain, ultimately enriching their knowledge base. Consequently, learners tend to engage in reciprocal learning through interactions guided by their intrinsic motivation.</p> <p>Figure 6 illustrates the epistemic network of peripheral interactors, showing a predominant focus on two dimensions: information sharing and individual metacognitive knowledge. The connection between elements reveals that peripheral interactors are situated within categories such as "information sharing-information analysis," "information sharing-information evaluation," "information sharing-emotional expression," "information sharing-individual metacognitive knowledge," and "individual metacognitive skills-social metacognitive skills," each characterized by strong connection strength.</p> <p>Although peripheral interactors occupy a marginal position in the overall community network, analysis of the structure of their epistemic network revealed that some of these peripheral interactors hold more profound cognitive characteristics in their views. The perspectives expressed in the Learning Cell System extend beyond the scope of discussion topics and incorporate reflections on their own practices related to these topics. They exhibit objective self-awareness, recognize their learning strengths and weaknesses, actively analyze key learning challenges, devise or discover learning strategies tailored to their characteristics, clarify their learning objectives, and engage in active self-reflection to foster deep personal learning. Appendix B (Table B2) consolidates the content published on the platform by the peripheral interactors (only a portion is included due to space constraints). Given the online nature of the course and the asynchronous discussion format, these learners are inclined towards independent thinking during the learning process. They connect acquired knowledge with their personal experiences and plan, reflect, and regulate their learning afterward, placing greater emphasis on the knowledge itself and their individual learning and growth, with less focus on maintaining social relationships within the community.</p> <p>As is evident from Figures 5 and 6, the cognitive components of the core and peripheral interactors exhibit substantial differences in connectivity within the OLCs. In Figure 7, the overlaid subtraction diagram illustrates the epistemic networks of the two learner types. This diagram was derived by subtracting the connectivity strengths of the epistemic networks between the core and peripheral interactors. The dots distinguished by different colors in Figure 7 (blue for core interactors and purple for peripheral interactors) represent the centroids of all e-learning participants. The squares indicate the mean values of each learner type's centroid. From Figures 5 and 6, it is apparent that both core and peripheral interactors fall into categories such as "information sharing-information negotiation," "information sharing-information evaluation," "information sharing-emotional expression," "information sharing-group cohesion," and "information application-group cohesion." As shown in Figure 7, the online discourse of core interactors emphasizes the four dimensions of information evaluation, emotional expression, group cohesion, and information negotiation more than that of peripheral interactors. Examining the connections between elements, core interactors exhibit stronger connection strengths in dimensions such as "information negotiation-information evaluation," "information application-information evaluation," "information sharing-information negotiation," "information analysis-emotional expression," "information negotiation-emotional expression," "information negotiation-group cohesion," "information application-emotional expression," "information evaluation-group cohesion," "information evaluation-individual metacognitive knowledge," and "emotional expression-individual metacognitive knowledge" compared to peripheral interactors. Online discussions of peripheral interactors focus more on the six dimensions of information sharing, information analysis, information application, individual metacognitive knowledge, individual metacognitive skills, and social metacognitive skills. In terms of the connections between elements, peripheral interactors exhibit stronger connections in dimensions such as "information sharing-information analysis," "information sharing-information application," "information analysis-information application," "information sharing-individual metacognitive knowledge," "information analysis-individual metacognitive knowledge," "information application-individual metacognitive knowledge," and "individual metacognitive skills-social metacognitive skills" compared to core interactors.</p> <p>PHOTO (COLOR): Figure 6. Epistemic network of peripheral interactors.</p> <p>PHOTO (COLOR): Figure 7. Core-peripheral interactors epistemic network subtraction (blue for core interactors and purple for peripheral interactors).</p> <p>Through comparative analysis, it was evident that the discussion content of core interactors during their participation in online learning tended to focus on surface-level knowledge dimensions. Their contributions are more interactive, involving activities such as evaluating others' perspectives, expressing a desire to establish or maintain collaborative relationships, and refining their views based on interactions. These learners openly expressed their feelings and thoughts in the learning community, resulting in a higher volume of posts, primarily of the emotional expression type. This contributes emotional support to the OLC. Given that online learning environments create spaces in which emotions and cognition intersect, community members' cognitive development is intricately linked to their emotional development. Thus, the emotional expressions of some members influence the overall learning atmosphere, boosting interaction frequency and facilitating the smooth progression of learning activities within the community, ultimately elevating the cognitive levels of its members. Conversely, the discussion content of peripheral interactors leans toward deeper knowledge dimensions. The majority of their posted content reflects profound contemplation of personal experiences or knowledge, further comparative analysis of existing information, and activities such as explanations, questioning, argumentative reasoning, and summarization. These learners contribute ideas and learning content associated with deeper and more expansive considerations, resulting in fewer posts but a greater depth of thinking about the subject matter. This dynamic promotes in-depth discussions within the learning community, enhancing the overall quality of the discourse.</p> <hd id="AN0187593720-19">Discussion</hd> <p></p> <hd id="AN0187593720-20">The overall situation of collaborative conversations among in-service teachers in asynchronou...</hd> <p>The study found that, during the asynchronous discussions throughout the semester, learners' collaborative conversations were often at a lower cognitive level. In this study, learners engaged in distance learning in a natural, intervention-free manner, spontaneously discussing and interacting in the discussion forum. This might be one reason for the limited occurrence of deep, high-level cognitive dialogues in the group's asynchronous online discussions.</p> <p>Adult learners are more likely to be motivated and involved in the learning process when they are given some control and choice over their learning (Knowles et al., [<reflink idref="bib23" id="ref48">23</reflink>]). For working learners, learning is not facilitated by either excessive or insufficient intervention. Therefore, it is recommended that lecturers, while designing richer and more diverse platform activities, use instant interactive tools (e.g., WeChat, QQ, etc.) to intervene within a certain degree in the construction of the OLC through a combination of synchronous and asynchronous discussions. For example, it can be used to provide timely guidance and assistance in WeChat groups and to set up intra-group and inter-group peer assessment activities, etc. Through these methods, students can be encouraged to engage in discussions and interactions in course groups and LCS. These activities, which create more opportunities for dialogue and communication, can help to stimulate cognitive conflicts, resolve them through discussion, and promote learners' individual knowledge construction. Simultaneously, they helps foster the cohesion of the OLC as a whole.</p> <hd id="AN0187593720-21">Learners' social interaction relationships are characterized by polycentricity and learners'...</hd> <p>Similar to the results of previous studies (Kleiner, [<reflink idref="bib22" id="ref49">22</reflink>]; Smith, [<reflink idref="bib35" id="ref50">35</reflink>]; Wang, [<reflink idref="bib39" id="ref51">39</reflink>]), this study exhibits a polycentric feature in the social network structure. This characteristic suggests that, in the LCS, the relationship between learners is less influenced by the instructor, and learners can participate in online discussions and interactions autonomously and freely. Each member of the learning community enjoys equal opportunities to engage in dialogue and communication with others and possesses the potential to become the core of the social interaction network. Learners in this distance education course showed polycentricity in their interactions while constructing an OLC. In addition, the interaction structure of the OLC in this study is similar to the structure discussed by Wenger et al. ([<reflink idref="bib44" id="ref52">44</reflink>]). Role distribution within the community presents an extremely typical feature: there are fewer core interactors (only 11); most learners are at the periphery of collaborative knowledge construction and have not established strong collaborative relationships with other members.</p> <p>This demonstrates that online learning provides in-service learners with more freedom and autonomy to explore knowledge and construct knowledge through cooperation. However, using the Internet as a primary tool for learning places higher demands on self-directed learning skills, self-control, etc. (Knowles et al., [<reflink idref="bib23" id="ref53">23</reflink>]). Consequently, teachers should take measures to support and guide in-service learners in developing and using self-directed learning skills in online learning environments. For example, clear and specific learning goals and tasks can be set to help learners stay goal-oriented in the process of online learning. Teachers can also design inspiring and interesting learning activities and cases to enhance learners' initiative and desire for continuous exploration and encourage them to participate in the process of knowledge co-construction. More importantly, teachers should create supportive and encouraging learning environments, help learners overcome difficulties and challenges through timely feedback and guidance, and encourage them to maintain enthusiasm in the learning process, thereby promoting the construction and development of OLCs.</p> <hd id="AN0187593720-22">Cognitive structural characteristics of core and peripheral interactors exhibit significant d...</hd> <p>During online learning, core interactors are more focused on interacting with others, such as evaluating others' viewpoints, expressing willingness to collaborate and reflect, and modifying their opinions. On the other hand, peripheral interactors are more inclined to share their personal experiences and engage in deep thinking, such as comparing and analyzing information, questioning, reasoning, summarizing, and solving problems.</p> <p>Therefore, different teaching strategies must be adopted for these two types of learners. The discussion and interaction of core interactors mainly echo discourse with less profound insight, but these types of learners can promote the learning atmosphere in the learning community and encourage other members to actively participate in the discussion through interaction to enhance group cohesion. Teachers should provide scaffoldings for this type of learners and guide them to study and think in-depth in a timely manner. This can help them avoid staying in shallow interactions and surface learning for a long period, grasp the core issues of the discussion topic, and improve the depth of the discussion. In contrast, peripheral interactors interact less with others and focus more on independent thinking and expressing in-depth opinions. For these learners, teachers can assign them roles as "organizer" or "group leader," encouraging active communication and interaction with members of the community through role assignments and task arrangements, thereby promoting increased interaction depth within the group and constructing the OLC.</p> <hd id="AN0187593720-23">Multiple factors influenced learners' online interaction patterns</hd> <p>The interaction patterns found in this study may be related to various factors, including individual characteristics, instructional evaluation, and the atmosphere within the learning community.</p> <p>First, adult distance learning is characterized by fragmentation, and the learning outcomes of community members may be influenced by their autonomous learning capability, self-regulation level and intrinsic motivation (Barak et al., [<reflink idref="bib4" id="ref54">4</reflink>]). Additionally, the level of engagement in online discussions among in-service learners may be related to the evaluation criteria. Because of the design of the scoring system of the LCS, some discussion tasks are set up to grant higher scores based on increased interaction frequency among learners. Therefore, to obtain higher scores, some learners post low-quality posts on these platforms. This also explains why core interactors engage in more interactions, although their perspectives are insufficiently deep. Moreover, through an analysis of learners' reflective documents, this study revealed numerous instances in which learners mentioned low participation among group members, minimal contribution of constructive opinions, and poor attitudes and capabilities towards learning in their end-of-term reflections. The level of interaction among in-service learners may be influenced by the environment in their learning community. Supportive community environments promote effective learner interaction. In contrast, students' willingness to interact is likely to decline in a poor environment.</p> <p>In summary, several factors influence online interaction among in-service learners. The findings of this study can provide effective instructional guidance for the design of future online learning courses and the construction of OLCs. First, it is crucial to consider individual characteristics such as learners' learning styles. Grouping learners based on learning styles for more scientific and precise guidance can be adopted, such as employing heterogeneous grouping within groups while maintaining homogeneity across groups. This enables different types of learners within groups to collaborate effectively, leveraging each other's strengths. Second, teachers can provide learners with training in self-regulation learning skills by using teaching strategies and designing teaching activities, including aiding them in formulating strategies, setting goals, and managing resources (Cohen & Magen-Nagar, [<reflink idref="bib14" id="ref55">14</reflink>]). Additionally, teachers can assign roles within groups to provide cognitive scaffolding (Burke, [<reflink idref="bib6" id="ref56">6</reflink>]; Chang & Brickman, [<reflink idref="bib8" id="ref57">8</reflink>]; Cheng et al., [<reflink idref="bib10" id="ref58">10</reflink>]; De Wever et al., [<reflink idref="bib15" id="ref59">15</reflink>]), helping learners clarify their responsibilities and actively engage in collaborative learning activities during online interactions. Finally, feedback is essential to the remote learning process (Attiogbe et al., [<reflink idref="bib3" id="ref60">3</reflink>]; Chetwynd & Dobbyn, [<reflink idref="bib11" id="ref61">11</reflink>]; Hyland, [<reflink idref="bib21" id="ref62">21</reflink>]; Ypsilandis, [<reflink idref="bib47" id="ref63">47</reflink>]). Teachers should promptly provide feedback and assess learners' learning processes, encouraging meaningful and effective contributions while avoiding excessively echoing discourse and low-cognitive interactions. Teachers can also include facilitators in the groups to assist in supervising, guiding, and providing feedback, with the aim of enhancing the quality of student interactions and promoting the construction of an OLC for in-service teachers.</p> <hd id="AN0187593720-24">Some limitations</hd> <p>Nevertheless, this study has certain limitations. It solely focused on the learning stage throughout the entire semester, overlooking continuous scrutiny and exploration of interaction patterns and dynamic evolution mechanisms across different stages within this OLC. Subsequent research will address these gaps. Additionally, future studies could leverage tools like machine learning and large language models for automatic encoding and analysis of interaction data in online learning. This innovation aims to diversify research methodologies, broaden perspectives, and explore wider applications of this research method, such as understanding interaction patterns and community construction among larger cohorts of in-service postgraduate students.</p> <p>In general, our results show that in asynchronous discussions without facilitation, the overall situation of collaborative conversations among in-service teachers tends to be low-level. Their social interactions manifest polycentricity, showcasing distinct role characteristics among participants. Notably, significant disparities emerge in the content of discussions between core and peripheral interactors, accentuating diverse epistemic networks. Based on the research findings, we attempted to analyze the underlying reasons behind these interaction patterns and proposed optimization strategies for constructing OLCs. This offers beneficial insights and references for the development of related fields such as in-service teacher education, in-service postgraduate education, distance learning, and adult education.</p> <hd id="AN0187593720-25">Conclusions and implications</hd> <p>In this study, the social epistemic network analysis method was used to examine interaction patterns among postgraduate learners. This validated the method's applicability in investigating the formation of learning communities among in-service teachers, offering valuable insights for further research in this field.</p> <p>In actual distance learning scenarios, the interaction patterns of learners in a learning community tend to be reflected in the behavioral, cognitive, and emotional dimensions (Appleton et al., [<reflink idref="bib1" id="ref64">1</reflink>]; Fredricks et al., [<reflink idref="bib18" id="ref65">18</reflink>]). The spectrum of these interactions is extensive, encompassing various facets such as comments, responses, and likes within asynchronous online discussions, as well as exchanges and immediate feedback in synchronous online discussions. How to collect diverse interactive data to analyze learners' interaction patterns so as to help understand the mechanisms, patterns, and strategies implicated in the construction of OLCs, is a practical problem that needs to be deeply thought about and solved. This study is a preliminary attempt to solve this problem, based on the collaborative conversation texts of in-service learners, using social epistemic network analysis to analyze the interaction patterns of learners and explore the potential factors. From the practical perspective, optimization strategies are proposed to enhance the formation of OLCs. These findings offer invaluable insights and stand as valuable references for future considerations in in-service postgraduate education and adult learning.</p> <hd id="AN0187593720-26">Disclosure statement</hd> <p>The authors declare that there is no conflict of interest regarding the publication of this paper. No financial or personal relationships that could inappropriately influence or bias the content of this work.</p> <hd id="AN0187593720-27">Appendix A</hd> <p>Table A1. Coding framework.</p> <p> <ephtml> <table><thead><tr><td>Dimension</td><td>Sub-Dimension</td><td>Description</td><td>Example</td><td>Code</td></tr></thead><tbody valign="top"><tr><td>Individual Cognition</td><td>Information Sharing</td><td>Simple Sharing of Information, Opinions, Remarks, Etc. related to the Topic or Task, Including Your Own Additions to Your Own Comments.</td><td><italic>Learning Science is a Very Practical Course That Gives us a Whole New Perspective on How Humans Learn from a Pedagogical Point of View.</italic></td><td>C1</td></tr><tr><td>Information Analysis</td><td>Arguing, Comparing, Analyzing, Interpreting, Questioning, Rejecting, Reasoning, Summarizing, and Revising Information in Depth</td><td><italic>I Have a Better Understanding of Scaffolding. Like a Building, Scaffolding Not Only Supports the Construction of the Building, but Additional Scaffolding is Added When the Construction Workers Need to Reach a Higher Position and Can Be Removed When the Building is Completed. In an Effective Learning Environment, Scaffolding is Gradually Added, Modified and Removed as Learners Need It, and Eventually the Scaffolding Disappears Altogether. Scaffolding is the Assistance Provided to a Learner, Customized to That Learner's Needs in Achieving His or Her Current Goals. Effective Scaffolding Provides Prompts to Help Learners Solve Problems on Their Own. Effective Learning Environments Help Students Actively Construct Knowledge Rather than Telling Them How to Do It or Doing It for Them(by LYD.)°</italic></td><td>C2</td></tr><tr><td>Information Negotiation</td><td>Further Expand, Ask for Others' Perspectives, Understand Others' Perspectives, Adjust Their Own Perspectives, and Harmonize Their Perspectives or Programs</td><td><italic>I Don't Know If Other Students Have Practiced the Literature Circle Reading Program? (by WST)</italic></td><td>C3</td></tr><tr><td>Information Modification</td><td>Testing, Adding and Revising Ideas</td><td><italic>Summarizes the Concepts Very Well; in the Process, I Would Suggest Adding a Section on the Teacher as a Guiding Role; the Evaluation Criteria Could Be Richer! (by LYD)</italic></td><td>C4</td></tr><tr><td>Information Application</td><td>Apply and Integrate Knowledge to Form Programs, Plans, Goals, Etc.</td><td><italic>After Studying the Course "Project-Based Learning and Problem-Based Learning" Shared by the Teacher, I Have Gained a Deeper Understanding and Knowledge of Project-Based Learning and Problem-Based Learning, Which Also Guided Me to Further Reflect on How to Design Project-Based Learning Tasks in the Classroom. Taking the Example of "World Youth Robotics Skills Competition" in the Tianjin Paper of the 2018 Gao Kao University Entrance Examination as an Example, I Would like to Illustrate How to Design Project-Based Learning Tasks in the Classroom......(by ZL)</italic></td><td>C5</td></tr><tr><td>Information Evaluation</td><td>Evaluate and Reflect on Ideas, Comments, Programs, Etc. e.g., "agreeing with the views of ..., resonating with ...", etc.</td><td><italic>It's a Good Program Practice, and It's Kind of an Interdisciplinary Practice That Exercises Students in All Aspects of Their Lives. (by CL)</italic></td><td>C6</td></tr><tr><td>Social Interaction</td><td>Emotional Expression</td><td>Expresses Emotional States in Response to Others' Ideas, Such as Positive Emotions like Praise, Gratitude, Affirmation, and Empathy, and Negative Emotions like Rejection and Opposition</td><td><italic>Gao's Comment Made Me Realize Something. (by ZSW)</italic></td><td>S1</td></tr><tr><td>Open Communication</td><td>Communication Not Directly Related to the Task, Such as Introductions, Small Talk, Trivial Expressions, Etc.</td><td><italic>I Can't Switch the Chinese Input Just Now, but I Can in the Reply? (by FXW)</italic></td><td>S2</td></tr><tr><td>Group Cohesion</td><td>Desire to Establish or Maintain a Collaborative Relationship: includes Friendly Interactive Behaviors Such as Greeting, Encouraging, and Expressing Anticipation of Communicating Together.</td><td><italic>I Look Forward to Learning More English Teaching Methods and Techniques from All the Teachers in the Continuous Exchanges and Learning! (by WFX)</italic></td><td>S3</td></tr><tr><td>Individual Metacognition</td><td>Individual Metacognitive Knowledge</td><td>Person: all of an Individual's Knowledge about Themselves or Their Peers as Cognitive Objects Tasks: Knowledge about the Requirements, Characteristics, etc. of Cognitive Activity Tasks Strategies: an Individual's Knowledge of the Cognitive Approaches, etc., needed to Accomplish a Task</td><td><italic>I Have Worked in Public and Private International High Schools as an Operations Manager and an IELTS Instructor. During That Time, I Often Communicated with English Teachers in Ordinary High Schools and Junior High Schools, and I Also Communicated with Some Junior High and High School Students about Their English Learning. I Found One Problem: The pace of English learning in middle and high schools is too slow. (by HZT)</italic></td><td>M1</td></tr><tr><td>Individual Metacognitive Experience</td><td>An Experience That Occurs When an Individual Becomes Aware of Situations Related to the Process of Cognitive Activity.</td><td><italic>Although Our Lives Are Very Busy, I Would like to Say That Everyone Who Joins This Online Learning Community Has an Upwardly Mobile Heart for Learning and Truthfulness. For Example, in Our Group, Every Time we Have a Group Collaborative Study, Even If we Are Busy, we All Take the Initiative to Participate in the Discussion of the Collaborative Meeting and the Execution of the Assigned Tasks. In One Semester, Our Group Completed All the Group Collaborative Learning Tasks on Time. It is Because of the Companionship of These Online Partners That I Have Experienced the Various Emotions of Studying This Course: anxiety, Happiness, Recognition, Mutual Support, Admiration, Achievement, Etc.(by ZH)</italic></td><td>M2</td></tr><tr><td>Individual Metacognitive Skills</td><td>Planning: Decision Making or Initial Planning of Learning Tasks; Monitoring: Awareness and Monitoring of Behavioral, Cognitive, Motivational, and Affective States during Cognitive Processes; Moderation: using Cognitive Strategies to keep the Learning Process along the set Goals and Programs to be carried out; Assessment: Evaluation and Reflection on Cognitive Processes and Outcomes.</td><td><italic>The Course "Learning Science" is the Most Informative, the Most Knowledgeable, and the Most Used Learning Platform in This Semester, with Each Lesson Being Full of Content, Distinctive Themes, and Attentively Produced Courseware. Through Reflection, we Were Able to Connect Our Actual Teaching Experience and Cases with the Course Content of Each Class, Further Transform Knowledge into Practice, and Continuously Promote the Effectiveness of Our Teaching Ability and Improve Our Teaching Methods and Tools. (by ZQL)</italic></td><td>M3</td></tr><tr><td>Social Metacognition</td><td>Social Metacognitive Knowledge</td><td>Metacognitive Knowledge Related to Other Members of the Group/Community.</td><td><italic>The Course Content and Use of Learning Elements Were Not Too Difficult and Were Easier to Accept. The Final Assignment (Thesis) is a Bit of a Challenge, but There is More Time. If You Make Good Use of Teamwork, You Get Twice the Result with Half the Effort; Otherwise, You Might as Well Do It on Your Own. (by LYD)</italic></td><td>SM1</td></tr><tr><td>Social Metacognitive Experience</td><td>Metacognitive Experiences Related to Other Members of the Group/Community.</td><td><italic>Learning and Cooperating in a Group is my Favorite Way. Each Group Meeting and Discussion of the Selected Topic Allows us to Have a Clash of Ideas and Enhancement. I Deeply Feel my Own Shortcomings at the Same Time, but Also from Each Member of the Group to Learn from Their Own Strengths Do Not Have. Our Group Has Held Many Group Meetings to Do Homework. Real Communication is More Effective than Words, and It's Really Effective to Review Homework and Exchange Practical Exercises in the Middle of a conversation. (by ZJ)</italic></td><td>SM2</td></tr><tr><td>Social Metacognitive Skills</td><td>Metacognitive Skills Related to Other Members of the Group/Community.</td><td><italic>We Use a Collaborative Approach, so That we Can Have a Clear Division of Labor Can Also Be Free to Discuss, but Also to Increase Mutual Understanding of Each Other, in the Process of Completing the Content There Are Problems we Usually Help Each Other or to Ask for Help from Students outside the Group, we Combine the Homework and Research with Academic Theory to Help us Explore and Promote the Efficiency of Learning. (by GJW)</italic></td><td>SM3</td></tr></tbody></table> </ephtml> </p> <hd id="AN0187593720-28">Appendix B</hd> <p>Table B1. Content of core interactors statements (partial).</p> <p> <ephtml> <table><thead><tr><td>Core Interactors</td><td>Statements</td></tr></thead><tbody valign="top"><tr><td>PXJ</td><td>First of all, this course is my favorite among all. On the one hand, it is because the content of Learning Science is closely related to my practical teaching, <bold>filling the gaps in my teaching theory</bold>. For example, in the past, I would only imitate others when designing lessons, especially in scaffolding construction. However, now that I understand the principles and usage of scaffolding, I can scientifically design my own courses. On the other hand, the course is in Chinese, making it easy to understand. Additionally, <bold>by learning from everyone's experiences in studying, I can gain a deeper understanding of the course</bold>. Secondly, I really enjoy the method of group cooperation. Compared to studying alone, the process of group collaboration and discussion allows me to think more comprehensively. Each person has their own expertise, and everyone can complement each other's strengths and weaknesses during collaboration, resulting in the design of better courses.</td></tr><tr><td>HPY</td><td>First of all, Mr. Wang brings us new learning concepts and learning perspectives about this course in every class. This is a brand new course that I <bold>have never been exposed to in my previous undergraduate studies</bold>. Therefore, every time Mr. Wang taught the class, I would hold an empty cup mentality and learn with an open mind. Secondly, after I learned about this subject, I looked back and scrutinized my previous teaching experience, and I felt that there were many directions that needed to be adjusted and improved. Finally, <bold>through the cooperation among our group members, we have both improved academic communication and become good companions on the road to learning</bold>, so I thank Mr. Wang for his careful arrangement and teaching.</td></tr><tr><td>WP</td><td><bold>I didn't know there was such a subject as "Learning Science" before I started studying it,</bold> but after listening to Mr. Wang's class, I really benefited a lot. After listening to Mr. Wang's class, I really benefited a lot. It seems that I can now find corresponding explanations for the theories or reasons that I usually can't figure out. I am currently a teacher in a training organization. I prepare lessons during the week and then attend classes on weekends. Whenever I have time, I basically listen to the lessons online and then watch the replay again. After using Learning Meta-Platform for a semester, I have adapted to it from being too much of a hassle in the beginning, and after each week's lesson, I always react to the Learning Meta-Platform first to see if there is any corresponding homework on it. In my case, if the first 15 lectures are completed in full with quality and quantity, it is still difficult to do so. I usually have a lot of school work and life affairs, but I basically complete 3/4 of them seriously. Thank you very much for Mr. Wang selfless contribution to the courseware, every time you complete the homework, you will force yourself to seriously study the teacher's courseware, so that the enrichment once again. At present, I also like and get used to Learning Meta-Platform, and I think it's quite good to use it, and there are a lot of good learning resources on it.</td></tr><tr><td>YHL</td><td><bold>This semester for the first time in contact with this discipline</bold>, from the beginning of the doubt to the later awe, Mr. Wang took us to learn the aspects of this discipline and also allowed us to participate in a number of international exchange conferences, which are very cutting-edge academic exchanges; and then to the various types of literature provided by Mr. Wang, through the review of the world's more advanced research process and development, combined with the knowledge of the Learning Sciences taught by Ms. Wang, to understand that: Internationally, Learning Science is mainly divided into three categories, focusing on the basic theories and cutting-edge perspectives of Learning Science "how people learn", focusing on learning technology and learning environment learning design and assessment, and focusing on the Learning Science research methodology of quantitative, qualitative and mixed research methods used in learning science research. The teaching objectives and content of these courses are characterized by the coexistence of basic theories and cutting-edge perspectives, the integration of technological innovation design and cognitive process evaluation, and the integration of multiple research methods. Teaching activities are closely related to the teaching objectives, emphasize the form of course participation experience and interaction, and focus on multiple formative evaluation in teaching evaluation. These contents are precisely the part that we need to carry out in depth in regular teaching, but at present in China's overall teaching process is relatively little, it is very worthy of in-depth thinking, my own also experimented with a number of small methods in their own actual teaching process, and it is indeed very effective.</td></tr><tr><td>ZD</td><td>Mr. Wang's content was very informative, and he used the platform to post assignments and provide classroom materials to share in each class, so that we gained a lot every week. I learned a lot from the midterm homework debriefing and the final homework debriefing, <bold>which allowed us to listen to other groups' sharing as well as Mr. Wang's comments, in addition to the fact that our group's design received Mr. Wang's pertinent comments</bold>.</td></tr><tr><td>ZL</td><td>Before the course began, I was full of infinite expectations for the course, <bold>looking forward to the course to learn how to quickly and efficiently learn the way to want to learn content</bold>. In the course of study, Mr. Wang's attentive preparation before class, patient explanation in class, and careful study arrangements after class all provided sufficient conditions for us to master the relevant theoretical knowledge and practical aspects of Learning Science. All of this made me have a deep experience in learning content, learning methods and learning outcomes. In terms of learning methods, the learning method of "lectures + classroom exercises + group work assignments" helped us to learn in a combination of online and offline ways. Among them, Mr. Wang shared a lot of offline reading materials for us through the learning platform, and at the same time, he also arranged synchronous assignments on the platform to help us further self-study and study the course content, and also prompted and urged us to use the platform not only to publish our own experiences on different course topics, but also to pay attention to other students' opinions on the same issue. <bold>On this kind of platform communication, we encourage each other and complement each other's strengths and weaknesses.</bold></td></tr></tbody></table> </ephtml> </p> <p>Table B2. Content of peripheral interactors statements (partial).</p> <p> <ephtml> <table><thead><tr><td>Peripheral Interactors</td><td>Statements</td></tr></thead><tbody valign="top"><tr><td>FJQ</td><td><bold>In my opinion, improving students' ability to learn independently is the point at which current education should be improved.</bold> Under the background of exam-oriented education, teachers speak and students learn is the main mode of knowledge transfer. Based on subjective and objective factors, it is difficult to integrate the new teaching mode into the real teaching practice. The booming online classes during the epidemic were seen as a failure by the vast majority of teachers and parents because students did not learn thoroughly or well? Why is this so? One of the important reasons is that the student lacks the ability to learn on his own. For example, if he encounters a problem that he does not know, he will not think about how to solve it, he will not arrange the corresponding exercises for himself, and he does not know what he learns that he really understands. <bold>Based on the above problems, I would like to mainly conduct research on the effect of improving learning motivation; improving learning strategies; on improving students' independent learning ability.</bold> Then I need to go into specific aspects of improving learning motivation and improving learning strategies, then divide improving students' independent learning ability into specific measurable and observable aspects, and finally conduct correlation studies. Finally find out which factor has the greatest influence, then practice in teaching, constantly debugging, and finally form a set of relatively reasonable model.</td></tr><tr><td>LXS</td><td>Embodied cognition emphasizes one's own experience, and we can use our own perceptions to work with environmental supports during problem solving or knowledge acquisition. <bold>In teaching, we should focus on students' perception of the environment and of the target object of inquiry, and use an inquiry-based learning model.</bold> Taking the theme of food as an example, firstly, we need to introduce the situation to lead to the problem, and secondly, we need to explore together so that students can observe through hands-on experience, take out the menu teaching aids and waiter's aprons made in advance, and students will play the corresponding roles to observe and learn in the experience. Finally, the teacher explains and summarizes the problems that occurred during the process, summarizes and corrects them, announces the results and shares them with the students.</td></tr><tr><td>LXS</td><td><bold>I believe that knowledge construction should include knowledge assimilation and knowledge accommodation.</bold> Knowledge construction must be realized in a knowledge construction community. Learners for the common field of inquiry to find and define the need to master the understanding of the problem, to carry out inquiry activities, the formation of the group's initial insights, and these insights are open to each other, each other's comments, questioning, improvement and enrichment, sublimation of the new problem, to promote the understanding of the knowledge of the learners and inquiry. Constructive English classroom design, take the elementary school English classroom as an example, elementary school students are in the stage of rapid physiological and psychological development, authentic contextual teaching can enhance the infectious force of teaching and help to improve the enthusiasm of students to participate in the classroom.1. Based on students' life experience and background, introduce the theme context; 2. the classroom makes good use of games to carry out and create thematic contexts, learning in games and having fun;3. Make good use of teaching aids such as pictures and videos to help students understand and feel.</td></tr><tr><td>XYH</td><td>The students I am currently teaching are junior high school students. At this stage, secondary school students still take the examination as an important criterion for learning English and testing their English performance, and in the classroom the teacher still fills in the teaching, the teacher teaches words, phrases, translates the text, and explains grammar, and the students accept it passively and lack initiative, and there is a lack of interaction in the classroom, so the oral communication skills cannot be practiced. <bold>There are two main problems in the interactive activities of oral English teaching,</bold> the first is that interaction may take up a lot of classroom time. Classroom teaching cannot be completed. The second problem is that students' linguistic errors in interaction cannot be corrected in time. <bold>After learning the design-based research, the classroom teaching process needs to be designed when solving the classroom oral teaching.</bold> To address the first problem, the teacher can take 10 min of classroom time and use 23 min of it to set up the group activities, and the remaining 78 min for the students to carry out the oral communication in the group. Assuming that the class is divided into 8 groups of 5 students each, there will be 40 students who can carry out the language output. Group activities are more suitable for large classes and can create more opportunities for interaction. For the second problem, during the group interaction, the teacher can walk around and listen to the students' discussion, and in the process, he/she can write down the students' general problems and summarize them in the class to effectively solve the problem that the students' language errors cannot be corrected in time during the interaction.</td></tr><tr><td>YZ</td><td>scaffolding has similarities with mind mapping, which I have come across before, in which concise, visual structure diagrams are used instead of words in teaching. Of course, scaffolding theory is much more than that, according to Mr. Wang's tips, I read the Cambridge Learning Science Handbook part1 Section 3, and got a deeper understanding of scaffolding theory, which can help students to build up their own knowledge system, which will help them to gain a deeper understanding of knowledge, which is more beneficial to generalize knowledge, and which will be more positive for learning. <bold>After reading through the third sub-part of the first part, I have more inspiration.</bold> In teaching practice, anything that allows students to acquire knowledge independently is a good approach, so scaffolding is widely used in teaching. At the same time, it can build knowledge through intuitive methods, so that the knowledge at a glance, which in the learning of students will be of great help, it really reflects the knowledge is not in the more, lies in the essence of the essence. At the same time, according to the theory of Cambridge scaffolding, scaffolding can be expanded and dismantled at any time, with a strong contraction. From this aspect, scaffolding theory will be an indispensable aid in our teaching.</td></tr><tr><td>ZXW</td><td>I am a teacher in a secondary school, and <bold>the biggest problem I encounter in teaching is that my students have a weak foundation in English and are not highly motivated to learn English.</bold> They will take the Vocational College Entrance Examination (VCE) after three years of study, and enter the university through examinations in cultural and professional subjects. <bold>This semester I have integrated English current affairs news into the course,</bold> such as Henan rainstorm, Tokyo Olympics, and the movie Lake Changjin. The practice has helped to improve students' motivation to learn English. The class is a combination of English and Chinese, and the content in the PPT is not all in English, so that it does not put too much pressure on the students to continue learning in the first place. Changjin Lake lesson I created a dialog situation for students to exchange aftermath of the movie Changjin Lake, for one of the questions: "How do you like it? / What do you think of it?", in order to stimulate students to a deeper level of thinking, in the PPT to add a volunteer soldier Song Ah Mao's suicide note; Changjin Lake Battle of the pro-life Zhou Quandi warrior material, and finally to the answer to this question to add The movie is a combination of Chinese and English, the content in the PPT is not entirely in English, so as not to bring too much pressure on students to continue learning. "The movie is touching/ moving/ patriotic/ thought provoking". Through these practices, I feel that we teachers can actually make some changes, and students' motivation can be slowly mobilized. In this process, it is also important to find the emotional resonance. I hope we can all reap the joys of education.</td></tr></tbody></table> </ephtml> </p> <hd id="AN0187593720-29">Appendix C</hd> <p></p> <hd id="AN0187593720-30">Designed thematic activities of a learning topic selected from LCS</hd> <p>During this online course, the instructor regularly updated the weekly course topics, content, and learning activities (Figure 8). For instance, in Week 12, the instructor assigned a learning activity titled "Briefly talk about the enlightenment of social learning and debate learning to you" (Figure 9). Students interacted in the discussion forum to engage with this activity (Figure 10).</p> <p>Graph: Figure 8. Home page of this online course.</p> <p>Graph: Figure 9. A learning activity in Week 12.</p> <p>Graph: Figure 10. 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Her research interests including the construction of online learning communities, professional development of in-service teachers, technology-enhanced learning and teaching.</p> <p>Hui Zhang , Master's Student from Artificial Intelligence and Human Languages Lab, Beijing Foreign Studies University, Beijing, China. Her research interests involve learning analytics, online collaborative learning, teacher education.</p> <p>Qi Wang , PhD, Associate Professor from Artificial Intelligence and Human Languages Lab, Beijing Foreign Studies University, Beijing, China. His research interests involve adaptive learning, learning resource generation, and technology-enhanced learning and teaching. Based on his research interests, he has offered several online courses and explored learners' learning mechanisms in online learning.</p> </aug> <nolink nlid="nl1" bibid="bib29" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib19" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib30" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib31" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib42" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib45" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib43" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib34" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib33" firstref="ref10"></nolink> <nolink nlid="nl10" bibid="bib28" firstref="ref11"></nolink> <nolink nlid="nl11" bibid="bib12" firstref="ref12"></nolink> <nolink nlid="nl12" bibid="bib36" firstref="ref13"></nolink> <nolink nlid="nl13" bibid="bib49" firstref="ref14"></nolink> <nolink nlid="nl14" bibid="bib13" firstref="ref16"></nolink> <nolink nlid="nl15" bibid="bib20" firstref="ref17"></nolink> <nolink nlid="nl16" bibid="bib32" firstref="ref18"></nolink> <nolink nlid="nl17" bibid="bib50" firstref="ref19"></nolink> <nolink nlid="nl18" bibid="bib38" firstref="ref20"></nolink> <nolink nlid="nl19" bibid="bib40" firstref="ref21"></nolink> <nolink nlid="nl20" bibid="bib41" firstref="ref22"></nolink> <nolink nlid="nl21" bibid="bib24" firstref="ref23"></nolink> <nolink nlid="nl22" bibid="bib37" firstref="ref24"></nolink> <nolink nlid="nl23" bibid="bib46" firstref="ref25"></nolink> <nolink nlid="nl24" bibid="bib48" firstref="ref26"></nolink> <nolink nlid="nl25" bibid="bib27" firstref="ref27"></nolink> <nolink nlid="nl26" bibid="bib17" firstref="ref36"></nolink> <nolink nlid="nl27" bibid="bib26" firstref="ref37"></nolink> <nolink nlid="nl28" bibid="bib16" firstref="ref41"></nolink> <nolink nlid="nl29" bibid="bib25" firstref="ref42"></nolink> <nolink nlid="nl30" bibid="bib44" firstref="ref44"></nolink> <nolink nlid="nl31" bibid="bib23" firstref="ref48"></nolink> <nolink nlid="nl32" bibid="bib22" firstref="ref49"></nolink> <nolink nlid="nl33" bibid="bib35" firstref="ref50"></nolink> <nolink nlid="nl34" bibid="bib39" firstref="ref51"></nolink> <nolink nlid="nl35" bibid="bib14" firstref="ref55"></nolink> <nolink nlid="nl36" bibid="bib10" firstref="ref58"></nolink> <nolink nlid="nl37" bibid="bib15" firstref="ref59"></nolink> <nolink nlid="nl38" bibid="bib11" firstref="ref61"></nolink> <nolink nlid="nl39" bibid="bib21" firstref="ref62"></nolink> <nolink nlid="nl40" bibid="bib47" firstref="ref63"></nolink> <nolink nlid="nl41" bibid="bib18" firstref="ref65"></nolink>
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A SeNA-Based Study of In-Service Teachers' Interaction Patterns in an Online Learning Community
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yixin+Zhang%22">Yixin Zhang</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0005-5878-6813">0009-0005-5878-6813</externalLink>)<br /><searchLink fieldCode="AR" term="%22Hui+Zhang%22">Hui Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Qi+Wang%22">Qi Wang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9006-9914">0000-0001-9006-9914</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Distance+Education%22"><i>Distance Education</i></searchLink>. 2025 46(3):514-546.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 33
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Communities+of+Practice%22">Communities of Practice</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Mediated+Communication%22">Computer Mediated Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Interaction%22">Interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Faculty+Development%22">Faculty Development</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Asynchronous+Communication%22">Asynchronous Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Collaboration%22">Teacher Collaboration</searchLink><br /><searchLink fieldCode="DE" term="%22Interpersonal+Relationship%22">Interpersonal Relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Graduate+Students%22">Graduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Teachers%22">Teachers</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China+%28Beijing%29%22">China (Beijing)</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/01587919.2024.2392689
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0158-7919<br />1475-0198
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In-service teachers, constrained by work commitments, adopt online learning methods for their graduate studies, emphasizing the significance of exploring the construction of online learning communities. Analyzing collaborative conversation texts within the online learning process of the "Learning Science" course at a university in Beijing, this study employed social epistemic network analysis (SeNA) to investigate interaction patterns of in-service teachers and propose optimization strategies. The findings of this study show that in asynchronous discussions without intervention, the overall situation of collaborative conversations among in-service teachers tends to be low-level. The social interaction network shows polycentricity, categorizing members into core and peripheral interactors based on distinct social interaction characteristics, highlighting substantial disparities in epistemic networks. Core interactors prioritize engaging with others, yet their contributions primarily echo interactions, reflecting superficial knowledge dimensions. Conversely, specific peripheral interactors prioritize independent reflection, sharing content largely derived from personal experiences or knowledge, signifying a commitment to independent and in-depth thinking within deeper knowledge dimensions. Moreover, learners' online interactions are influenced by individual characteristics, instructional evaluation and the learning community atmosphere.
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  Data: As Provided
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  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1482373
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1482373
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      – Type: doi
        Value: 10.1080/01587919.2024.2392689
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 33
        StartPage: 514
    Subjects:
      – SubjectFull: Communities of Practice
        Type: general
      – SubjectFull: Electronic Learning
        Type: general
      – SubjectFull: Computer Mediated Communication
        Type: general
      – SubjectFull: Interaction
        Type: general
      – SubjectFull: Faculty Development
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      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Asynchronous Communication
        Type: general
      – SubjectFull: Teacher Collaboration
        Type: general
      – SubjectFull: Interpersonal Relationship
        Type: general
      – SubjectFull: Graduate Students
        Type: general
      – SubjectFull: Teachers
        Type: general
      – SubjectFull: China (Beijing)
        Type: general
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      – TitleFull: A SeNA-Based Study of In-Service Teachers' Interaction Patterns in an Online Learning Community
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            NameFull: Yixin Zhang
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            NameFull: Qi Wang
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              Value: 3
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            – TitleFull: Distance Education
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