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

Mohammed Saqr1,2, Jalal Nouri3, Henriikka Vartiainen4

  • 1University of Eastern Finland, School of Computing, Joensuu Campus, Yliopistokatu 2, fi-80100, Joensuu, Finland. mohammed.saqr@uef.fi.

BMC Medical Education
|March 20, 2020
PubMed
Summary
This summary is machine-generated.

Social Network Analysis (SNA) can predict group performance in online problem-based learning (PBL) by examining interaction patterns. Key factors include interaction quantity, reciprocity, and cohesion, while teacher dominance may hinder performance.

Keywords:
Data analyticsLearning analyticsOnline learningProblem-based learningSmall groupsSocial network analysisSocial networking

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Area of Science:

  • Educational Technology
  • Social Network Analysis
  • Higher Education

Background:

  • Traditional research on problem-based learning (PBL) primarily uses self-reports, overlooking group dynamics.
  • Limited research has applied Social Network Analysis (SNA) to online PBL, neglecting student and teacher interaction factors.
  • This study addresses the gap by using SNA to analyze interactivity in online PBL settings.

Purpose of the Study:

  • To investigate the correlation between group, tutor, and individual student interactivity variables and group performance in online PBL.
  • To determine if interactivity variables derived from SNA can predict group performance.
  • To explore the utility of SNA for monitoring and supporting online PBL groups.

Main Methods:

  • Analyzed interaction data from 60 groups (598 students) across 12 dental education courses on a Moodle platform.
  • Constructed aggregate networks for each group using extracted interaction data.
  • Calculated SNA variables at group, student, and tutor levels, followed by correlation and multiple regression analyses.

Main Results:

  • High interaction quantity, active/reciprocal student interactions, and group cohesion (transitivity, reciprocity) indicate well-performing groups.
  • A dominant teacher role negatively correlates with group performance.
  • SNA centrality measures (Tutor Eigen centrality, user count, centralization outdegree) negatively predicted performance, while group reciprocity positively predicted improvement (R² = 0.76).

Conclusions:

  • Interaction, equal participation, inclusion, reciprocity, and cohesion are crucial for effective online PBL groups.
  • SNA effectively monitors online PBL groups, identifying key predictors of performance and enabling proactive support for group co-regulation.
  • SNA provides educators with valuable, easily obtainable insights into group dynamics and individual contributions.