Saqr, M., Nouri, J., Vartiainen, H. et al. What makes an online problem-based group successful? A learning analytics study using social network analysis. BMC Med Educ 20, 80 (2020). https://doi.org/10.1186/s12909-020-01997-7
What makes an online problem-based group successful? : a learning analytics study using social network analysis
|Author:||Saqr, Mohammed1,2; Nouri, Jalal2; Vartiainen, Henriikka3;|
1University of Eastern Finland, School of Computing, Joensuu Campus, Yliopistokatu 2, fi-80100, Joensuu, Finland
2Department of Computer and System Sciences (DSV), Stockholm University, Borgarfjordsgatan 12, PO Box 7003, SE-164 07, Stockholm, Sweden
3University of Eastern Finland, School of Applied Educational Science and Teacher Education, Joensuu, Yliopistokatu 2, fi-80100, Joensuu, Finland
4Department of Educational Sciences and Teacher Education, Faculty of Education, University of Oulu, P.O. Box 2000, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020050424734
|Publish Date:|| 2020-05-04
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.
BMC medical education
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
516 Educational sciences
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