Abstract
BackgroundAlthough 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.MethodsWe 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.ResultsThe 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.ConclusionsThe 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.
Highlights
There is a wealth of research focusing on Problem-based learning (PBL), most studies employ self-reports, surveys, and interviews as data collection methods and have an exclusive focus on students
This study investigates the group as a unit through the lens of Social Network Analysis (SNA) and learning analytics
Our research focuses on the group dynamics, and how the elements of the PBL process influence the outcome of the group as a whole and the participating students
Summary
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. PBL was first developed in medical education in the 1950s to respond to the criticism that traditional teaching methods fail to prepare medical students for solving problems in clinical settings [1]. The ability to make insightful and productive use of the learning resources is playing an important part in PBL method [1, 6]. This is to say, PBL often challenges students, since it requires learners to engage in selfregulated learning [7], that includes, for example, ability to purposefully regulate own cognitive, motivational, and emotional behavior as well as that of others for optimal learning [8]
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