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

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arxiv 2502.19112 v1 pith:BHTQ3SNS submitted 2025-02-26 cs.SI

classification cs.SI
keywords learningrolesemergingindividualstudentscollaborativeanalysischanges
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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.

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  1. AI-Driven Contribution Evaluation and Conflict Resolution: A Framework & Design for Group Workload Investigation

    cs.AI 2025-11 conditional novelty 5.0 of 10

    A three-dimension, nine-benchmark framework with LLM-based expert analysis is proposed to assist instructors in investigating group-work contribution disputes.

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