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![Overview of the interaction network. (a) Sociogram of the whole network. Node size denotes prestige measured by indegree centrality, and node colour denotes the group membership (including Group A, Group B, and the teacher). (b) A word cloud of top words that appeared in post content on Yellowdig [Colour figure can be viewed at wileyonlinelibrary.com]](profile/Bodong-Chen/publication/332201466/figure/fig3/AS:11431281176285236@1690093159831/Overview-of-the-interaction-network-a-Sociogram-of-the-whole-network-Node-size.png)
Overview of the interaction network. (a) Sociogram of the whole network. Node size denotes prestige measured by indegree centrality, and node colour denotes the group membership (including Group A, Group B, and the teacher). (b) A word cloud of top words that appeared in post content on Yellowdig [Colour figure can be viewed at wileyonlinelibrary.com]
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What is already known about this topic: Social interaction is important for learning, and asynchronous online discussions are broadly used to support social learning.
Teaching practices and strategies have been developed to support student participation in online discussions.
In some contexts, participation intensity predicts learni...
Citations
... This finding contradicts earlier studies that recognised different timing of learner participation and its connection with discussion performance (B. Chen & Huang, 2019;Riel et al., 2018). We tested whether the instructor attracted or extended more replies and found them more likely to send replies but actually less likely to be replied. ...
Online discussions are widely adopted in higher education to promote student interaction. However, prior research on online discussions falls short to estimate the effect of multiple factors collectively shape student interaction in online discussion activities. In this study, we applied a dynamic network analysis approach named relational event modelling to a data set from an online course where students participated in weekly discussion activities. In the relational event models, we incorporated multiple factors including participant characteristics, network formation mechanisms and immediate participation shifts. Results indicated that the instructor was more likely to initiate interactions but less likely to receive responses. Popularity, activity and familiarity established in prior relational events positively affected future events. Immediate participation shifts such as local popularity, immediate reciprocation and activity bursts also played a positive role. The study highlights the importance of considering multiple factors when examining online discussions, demonstrates the utility of relational event modelling for analysing online interaction and contributes empirical insights into student interaction in online discussions. Implications for practice or policy: Supporting online discussions in college classrooms requires instructors to consider multiple actors including pedagogical designs, technological affordances, learner characteristics and social dynamics. Educators could go beyond simply counting student posts to paying attention to how students interact at a micro level. Educators and instructional designers could pay attention to socio-temporal dynamics in online discussions and evaluate whether emerging dynamics in a particular course are desirable and conducive to student learning.
... The attention level of a participant in a social network is referred to as their social network prestige (Chen & Huang, 2019). It is a measure of the interactive influence of an individual learner at the micro level and an important factor in facilitating effective learning. ...
... In other words, the level of a learner's prestige is positively related to the number of evaluations their assignments have received, and the specific manifestation in the network is the number of directed links received. The analysis of social network prestige helps researchers to analyse and discuss in depth the interaction characteristics and learning impact of social networks from the perspective of individual nodes (Andrews, 2020;Chen & Huang, 2019). Exploring prestige and its related indicators in online peer assessment helps to go beyond counting basic learning behaviours, to uncover the mechanism by which the participation gap influences learning interactions and to suggest ways to reduce the participation gap. ...
... We found that the earlier the learners posted their assignments, the more likely they were to gain high prestige in peer assessment activities. This validated the previous findings, using regression analysis (Chen & Huang, 2019;Zingaro & Oztok, 2012), that assignment uploading time had a high predictive power on social network prestige. The interviews also confirmed that uploading and presenting the assignments earlier in peer assessment tended to obtain more views. ...
The rise of teacher training in online interactive learning environments has contributed to teachers’ professional development and brought new vitality to the informatisation of education. Many researchers have reported that there is a participation gap in online interactive learning environments. Research on the factors influencing this is very important. Social network prestige, which measures the degree to which learners gain peer attention in directed social networks, is one of the important metrics to characterise the participation gap. In this study, we offered an online teacher training course, and 1438 in-service teachers from primary and secondary schools attended. Among them, we selected 457 in-service teachers who participated in the three peer assessment activities as the final participants. To analyse the factors influencing learners’ social network prestige in online peer assessment, we first conducted a partial least squares structural equation modelling analysis to construct a model of factors influencing social network prestige. Then, we adopted several semi-structured interviews to investigate learners' perspectives to provide an in-depth analysis of the factors influencing social network prestige. The purpose of this study was to gain insight into the participation gap in online interactions and make effective suggestions on how to improve learning performance in online peer assessment. Implications for practice or policy: Course designers could improve the design of the introduction to peer assessment to motivate learners and enhance their acceptance of the activities. Course designers could reduce participation gap by assigning work from low-prestige learners to high-prestige learners in a non-mandatory way later in the course.
... Another potential reason for non-participation could be that students expected the tutor to start the discussion. Indeed, providing sentence openers and suggesting all students to check forum a couple of times per week have been found to be effective strategies for facilitating online discussions (Ak, 2016;Chen and Huang, 2019). ...
With Open Data becoming more popular and more public bodies publishing their datasets, the need for educating prospective graduates on how they can use them has become prominent. This study examines the use of the Problem Based Learning (PBL) method and educational technologies to support the development of Open Data skills in university students. The study follows a Design Based Research approach and consists of three phases: a) examination of stakeholders' needs, b) design of an Open Data module, and c) re-design of the module based on the outcomes of its first run. The data collected throughout the three phases come from various sources, namely interviews with practitioners, focus groups with students, and tutors' reflection. The findings suggest that while the PBL method is suitable for Open Data education, special care should be taken to ensure that the potential of educational technologies is fully realised. The study concludes with design principles that aim to guide instructors on how they can incorporate the PBL method and digital tools into Open Data education effectively.
Supplementary information:
The online version contains supplementary material available at 10.1007/s10639-022-10995-9.
... As early as 2013, Vaquero and Cebrian [5] described the participation gap as a "rich club" phenomenon, meaning that high-achieving students are more likely to have social interactions with each other, while low-achieving students struggle to engage in such interactions. Participation gap is a dynamic and complex social phenomenon reflected in the unequal and uneven nature of learners' interactions in online learning [6]. Several studies have reported the significant damage of this gap on learning performance, but further in-depth exploration of its impact is needed [7]. ...
... Participation gaps change constantly and can be described in sociological terms [6]. Social network prestige ('prestige') is a sociological term that captures the directionality and reciprocity of learners' interactions. ...
... In addition, many technology-supported learning scaffolds use social network analysis tools to build visual interaction networks of learners to help instructors identify marginal learners who interact less and might benefit from specific intervention strategies [6]. But these technology-supported learning scaffolds, such as commonly-used force-directed network visualization tools could not fully capture the prestige differences found in this study. ...
Online peer assessment has been widely applied in online teacher training. However, not all learners participate equally, detailed characterization and impact analysis of peer assessment is required. This study draws on a sociological term, social network prestige, to evaluate the participation gap of learners in online peer assessment. A teacher training course was offered to in-service primary and secondary school teachers, and 457 participants were ranked according to their prestige. Then, the top 30% of learners were considered the high-prestige group (142 participants) and the bottom 30% as the low-prestige group (128 participants). Social network analysis and behavioral sequence analysis were used to explore the differences in the learning outcomes of these two groups. The results showed significant differences in learning performance, social network structure and learning behaviors among learners with different prestige. High-prestige learners have better learning performance and are affected by their prior knowledge. Learners with different levels of prestige differ in social network structure and learning behavior. Based on these findings, this study suggests improvements to reduce the participation gap.
... Online, members in a large group may form a subgroup that is much more active than outside that subgroup. [35][36][37] Being a broker can boost an individual's influence in online communities 38,39 and cause them to receive more responses. 29,40 Moreover, a recent study shows that being broker is positively associated with language style matching between posts and replies, which is a proxy for social support. ...
... 40 Members with high closeness centrality tend to be highly connected with many members of the community, enjoy high levels of trust from group members, 10 exert more influence on group participants, 10,11,16 and receive more responses from group members. 37,40 Therefore, we propose the following hypothesis (H): ...
... Our findings show that being an online broker who bridges different subnetworks underlies the overall provision of emotional and informational social support. Though all group members have access to all messages in online groups, as they interact more actively with members in subgroups, 35,37 brokers may work as opinion leaders, 39 or "thought-leaders" 38 across different subgroups. This social influence brings them benefits when they need social support, manifested as receiving informational and emotional support. ...
This study applies social network analysis and quantitative content analysis to messages exchanged within an online support forum of caregivers of children with chronic asthma to examine how peer-to-peer network positions and personal communication styles (seeking and providing support) impact the reception of social support. Content analysis is used to determine rates of giving and receiving informational and emotional support. Network analysis assesses levels of individual betweenness and closeness centrality in the online network. Relationships between network positions, solicitation strategies, and the provision and reception of informational and emotional support are examined. Betweenness and closeness centrality are associated with improved informational and emotional support. The provision of informational support is also improved by providing descriptions of personal experience. Practical implications for the design and use of online support platforms are discussed.
... While there is a rich history of work documenting and investigating team processes for research purposes (e.g., Chen and Huang, 2019;Edmondson et al., 2007;Senge, 1990;Wise and Cui, 2018), efforts to make information about these processes available to the teams themselves have been limited until recently. Work on group awareness tools (Bodemer et al., 2018) focused primarily on developing specialized applications that visualize social processes (who is interacting with who) and knowledge states (who knows what) as part of the communication interface itself. ...
... Second, discussion participation needs to be timely and regular so that team members can engage with each other's posts in a reciprocal manner (Chen and Huang, 2019). Students in this course were required to make their first post no later than Wednesday and second post before the end of Saturday. ...
Team learning has become an essential activity in both professional and educational contexts. Learning analytics offers promising opportunities to support team learning by making information about group processes available to teams in real time to help them learn how to interact more productively. The design of team analytics requires three sets of critical decisions: the choices of metrics, the ways in which these metrics are presented, and how the analytics are implemented. This chapter describes four guiding principles for navigating this decision space in ways that produce relevant, understandable, and actionable analytics to support team learning.
... In the synchronous learning environment, students attend live lectures using learning management systems (LMS) such as Blackboard, D2L, and Moodle. This learning environment enables educators to have real-time teaching interactions, and collaborations with their learners through scheduled class hours (Chen & Huang, 2019;Ng et al., 2012). Also, students are able to access all teaching and resources and submit assessments through LMS in real-time (Çakıro glu & Kılıç, 2020). ...
The current educational disruption caused by the COVID‐19 pandemic has fuelled a plethora of investments and the use of educational technologies for Emergency Remote Learning (ERL). Despite the significance of online learning for ERL across most educational institutions, there are wide mixed perceptions about online learning during this pandemic. This study, therefore, aims at examining public perception about online learning for ERL during COVID‐19. The study sample included 31,009 English language Tweets extracted and cleaned using Twitter API, Python libraries and NVivo, from 10 March 2020 to 25 July 2020, using keywords: COVID‐19, Corona, e‐learning, online learning, distance learning. Collected tweets were analysed using word frequencies of unigrams and bigrams, sentiment analysis, topic modelling, and sentiment labeling, cluster, and trend analysis. The results identified more positive and negative sentiments within the dataset and identified topics. Further, the identified topics which are learning support, COVID‐19, online learning, schools, distance learning, e‐learning, students, and education were clustered among each other. The number of daily COVID‐19 related cases had a weak linear relationship with the number of online learning tweets due to the low number of tweets during the vacation period from April to June 2020. The number of tweets increased during the early weeks of July 2020 as a result of the increasing number of mixed reactions to the reopening of schools. The study findings and recommendations underscore the need for educational systems, government agencies, and other stakeholders to practically implement online learning measures and strategies for ERL in the quest of reopening of schools.
... Even though the annotation's spatial location was not predictive of network formation, we found a reply to an annotation would suffocate potential replies to nearby annotations. This mechanism might reflect the temporally condensed activities from individuals and temporally distanced activities among them (Chen & Huang, 2019). In other words, it is plausible in the web annotation context that some students would log on in a particular time when other students are unlikely to be active, scroll through a stack of peer annotations, and selectively reply to a few in different locations. ...
... In addition to learner activity and performance as potentially relevant factors in how students interact in online environment, timing of posts could also play a role. Chen and Huang [5] showed that in-course preferential attachment in online interactions relates to the timing of student behaviour. ...
Peer effects, an influence that peers can have on one’s learning and development, have been shown to affect student achievement and attitudes. A large-scale analysis of social influences in digital online interactions showed that students interact in online university forums with peers of similar performance. Mechanisms driving this observed similarity remain unclear. To shed light as to why similar peers interact online, the current study examined the role of organizing factors in the formation of similarity patterns in online university forums, using four-years of forum interaction data of a university cohort. In the study, experiments randomized the timing of student activity, relationship between student activity levels within specific courses, and relationship between student activity and performance. Analysis suggests that similarity between students interacting online is shaped by implications of the course design on individual student behaviour, less so by social processes of selection. Social selection may drive observed similarity in later years of student experience, but its role is relatively small compared to other factors. The results highlight the need to consider what social influences are enacted by the course design and technological scaffolding of learner behaviour in online interactions, towards diversifying student social influences.
... In education, research on networks spans three decades. Networks have been used to visualize the patterns of interactions in collaborative groups, to study the roles students play in the collaboration, to rank students' activities, or to predict performance to mention a few examples (Chen & Huang, 2019;Chen & Poquet, 2020;Halatchliyski et al., 2013;Saqr et al., 2019). While such methods have contributed enormously to our understanding of the learning process with their repertoire of powerful visualizations methods, there is a need for harnessing the power of other methods to extend our understanding different phenomena. ...
Recent findings in the field of learning analytics have brought to our attention that conclusions drawn from cross-sectional group-level data may not capture the dynamic processes that unfold within each individual learner. In this light, idiographic methods have started to gain grounds in many fields as a possible solution to examine students' behavior at the individual level by using several data points from each learner to create person-specific insights. In this study, we introduce such novel methods to the learning analytics field by exploring the possible potentials that one can gain from zooming in on the fine-grained dynamics of a single student. Specifically, we make use of Gaussian Graphical Models-an emerging trend in network science-to analyze a single student's dispositions and devise insights specific to him/her. The results of our study revealed that the student under examination may be in need to learn better self-regulation techniques regarding reflection and planning.
