Using social network analysis to understand online Problem-Based Learning and predict performance

Saqr, Mohammed; Fors, Uno; Nouri, Jalal

doi:10.1371/journal.pone.0203590

Cited by 83 publications

(69 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus through the analysis, it became clear that students skipped an essential part of the process of PBL in which students have to develop their metacognitive strategies and get/give feedback for peers and tutors. This finding support the potentials of using visual temporal analysis of learning processes as also shown in the context of understanding assessment [19,22]. Furthermore, through a correlation analysis we could conclude that the early week engagement in collaborative interactions as represented by the selected network parameters significantly correlated with students' performance.…”

Section: Discussionsupporting

confidence: 82%

High resolution temporal network analysis to understand and improve collaborative learning

Saqr

Nouri

2020

Proceedings of the Tenth International Conference on Learning Analytics &Amp; Knowledge

Self Cite

View full text Add to dashboard Cite

There has been significant efforts in studying collaborative and social learning using aggregate networks. Such efforts have demonstrated the worth of the approach by providing insights about the interactions, student and teacher roles, and predictability of performance. However, using an aggregated network discounts the fine resolution of temporal interactions. By doing so, we might overlook the regularities/irregularities of students' interactions, the process of learning regulation, and how and when different actors influence each other. Thus, compressing a complex temporal process such as learning may be oversimplifying and reductionist. Through a temporal network analysis of 54 students interactions (in total 3134 interactions) in an online medical education course, this study contributes with a methodological approach to building, visualizing and quantitatively analyzing temporal networks, that could help educational practitioners understand important temporal aspects of collaborative learning that might need attention and action. Furthermore, the analysis conducted emphasize the importance of considering the time characteristics of the data that should be used when attempting to, for instance, implement early predictions of performance and early detection of students and groups that need support and attention.

show abstract

Section: Discussionsupporting

confidence: 82%

High resolution temporal network analysis to understand and improve collaborative learning

Saqr

Nouri

2020

Proceedings of the Tenth International Conference on Learning Analytics &Amp; Knowledge

Self Cite

View full text Add to dashboard Cite

show abstract

“…One such factor relates to group size. While previous research has demonstrated that larger group sizes might have detrimental effects on collaborative learning (for example [14,15,28]), few are the studies that in detail have examined how social dynamics develop depending on group size, especially when the group size is in the range of 7-15 students which is common in for instance medical education where students work in teams in problem-solving scenarios. Thus, in this learning analytics study, we used social network analysis to understand the effect of group size on performance and in particular on the social dynamics in the collaborative groups.…”

Section: Discussionmentioning

confidence: 99%

“…Researchers have used SNA centrality measures to identify roles such as leaders, collaborators, and influential students in online forums. In the same vein, centrality measures have also been used as a proxy for students' online activity to predict performance using learning analytics methods [10,27,28]. SNA has been used to study semantic and epistemic networks, by examining the content of interactions and finding insights in how knowledge is constructed and exchanged among collaborators [29,30].…”

Section: Social Network Analysismentioning

confidence: 99%

A Learning Analytics Study of the Effect of Group Size on Social Dynamics and Performance in Online Collaborative Learning

Saqr

Nouri

Jormanainen

2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Effective collaborative learning is rarely a spontaneous phenomenon. In fact, it requires that a set of conditions are met. Among these central conditions are group formation, size and interaction dynamics. While previous research has demonstrated that size might have detrimental effects on collaborative learning, few have examined how social dynamics develop depending on group size. This learning analytics paper reports on a study that asks: How is group size affecting social dynamics and performance of collaborating students? In contrast to previous research that was mainly qualitative and assessed a limited sample size, our study included 23,979 interactions from 20 courses, 114 groups and 974 students and the group size ranged from 7 to 15 in the context of online problem-based learning. To capture the social dynamics, we applied social network analysis for the study of how group size affects collaborative learning. In general, we conclude that larger groups are associated with decreased performance of individual students, poorer and less diverse social interactions. A high group size led to a less cohesive group, with less efficient communication and less information exchange among members. Large groups may facilitate isolation and inactivity of some students, which is contrary to what collaborative learning is about.

show abstract

“…Some examples exist, such as investigating the potentials of using SNA in PBL scenarios. For example, researchers have used SNA techniques to identify the roles of the students and tutors to get insights about gaps in the collaborative process, or use the centrality measures to predict performance using learning analytics methods [30][31][32]. While adding to our understanding of the process, these studies focused on the individual collaborator not the group as a unit of analysis.…”

Section: Sna In Healthcare Educationmentioning

confidence: 99%

What makes an online problem-based group successful? A learning analytics study using social network analysis

et al. 2020

Self Cite

View full text Add to dashboard Cite

Background: Although there is a wealth of research focusing on PBL, most studies employ self-reports, surveys, and interviews as data collection methods and have an exclusive focus on students. There is little research that has studied interactivity in online PBL settings through the lens of Social Network Analysis (SNA) to explore both student and teacher factors that could help monitor and possibly proactively support PBL groups. This study adopts SNA to investigate how groups, tutors and individual student's interactivity variables correlate with group performance and whether the interactivity variables could be used to predict group performance. Methods: We do so by analyzing 60 groups' work in 12 courses in dental education (598 students). The interaction data were extracted from a Moodle-based online learning platform to construct the aggregate networks of each group. SNA variables were calculated at the group level, students' level and tutor's level. We then performed correlation tests and multiple regression analysis using SNA measures and performance data. Results: The findings demonstrate that certain interaction variables are indicative of a well-performing group; particularly the quantity of interactions, active and reciprocal interactions among students, and group cohesion measures (transitivity and reciprocity). A more dominating role for teachers may be a negative sign of group performance. Finally, a stepwise multiple regression test demonstrated that SNA centrality measures could be used to predict group performance. A significant equation was found, F (4, 55) = 49.1, p < 0.01, with an R2 of 0.76. Tutor Eigen centrality, user count, and centralization outdegree were all statistically significant and negative. However, reciprocity in the group was a positive predictor of group improvement. Conclusions: The findings of this study emphasized the importance of interactions, equal participation and inclusion of all group members, and reciprocity and group cohesion as predictors of a functioning group. Furthermore, SNA could be used to monitor online PBL groups, identify important quantitative data that helps predict and potentially support groups to function and co-regulate, which would improve the outcome of interacting groups in PBL. The information offered by SNA requires relatively little effort to analyze and could help educators get valuable insights about their groups and individual collaborators.

show abstract

Using social network analysis to understand online Problem-Based Learning and predict performance

Cited by 83 publications

References 57 publications

High resolution temporal network analysis to understand and improve collaborative learning

High resolution temporal network analysis to understand and improve collaborative learning

A Learning Analytics Study of the Effect of Group Size on Social Dynamics and Performance in Online Collaborative Learning

What makes an online problem-based group successful? A learning analytics study using social network analysis

Contact Info

Product

Resources

About