Analyzing Students' Emerging Roles Based on Quantity and Heterogeneity of Individual Contributions in Small Group Online Collaborative Learning Using Bipartite Network Analysis

2025-02-26

AI Summary

This study addresses the challenge of disentangling individual contributions from collective outcomes in computer-supported collaborative learning (CSCL). We propose an emergent role analysis framework grounded in a bipartite student–subtask network, capturing two dimensions: contribution volume and task heterogeneity. Using social network analysis—specifically centrality and diversity metrics—combined with a mixed-methods approach (quantitative network modeling and semi-structured interviews), we empirically evaluate the framework across seven online groups comprising 21 high school students. Our analysis reveals, for the first time, a statistically significant association between externally assigned leadership roles and dynamically evolving, data-driven emergent roles. Quantitative findings align closely with qualitative insights from interviews, demonstrating the framework’s validity in supporting regulatory learning at both individual and group levels, while ensuring pedagogical interpretability and educational relevance.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Social Networks and Social Media: Applications and emergent phenomena
• Economics, Online Markets and Human Computation: Social networks and social learning
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Understanding students' emerging roles in computer-supported collaborative learning (CSCL) is critical for promoting regulated learning processes and supporting learning at both individual and group levels. However, it has been challenging to disentangle individual performance from group-based deliverables. This study introduces new learning analytic methods based on student -- subtask bipartite networks to gauge two conceptual dimensions -- quantity and heterogeneity of individual contribution to subtasks -- for understanding students' emerging roles in online collaborative learning in small groups. We analyzed these two dimensions and explored the changes of individual emerging roles within seven groups of high school students ($N = 21$) in two consecutive collaborative learning projects. We found a significant association in the changes between assigned leadership roles and changes in the identified emerging roles between the two projects, echoing the importance of externally facilitated regulation scaffolding in CSCL. We also collected qualitative data through a semi-structured interview to further validate the quantitative analysis results, which revealed that student perceptions of their emerging roles were consistent with the quantitative analysis results. This study contributes new learning analytic methods for collaboration analytics as well as a two-dimensional theoretical framework for understanding students' emerging roles in small group CSCL.

Problem

• Analyze student roles in collaborative learning.
• Measure individual contributions in group tasks.
• Explore role changes in online learning groups.

Innovation

• Bipartite network analysis method
• Quantitative and qualitative data integration
• Two-dimensional theoretical framework