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<p>Understanding individual participation is critical for uncovering how individuals learn in collaborative learning as well as for providing personalized support to scaffold team success and individual gains. Modelling individual participation requires a process-oriented method rather than an outcome-focused approach. There is a need for a theoretical framework guiding the collection and analysis of process data for gauging individual participation in collaborative learning. This systematic review synthesizes theoretical aspects and analytical methods for modelling individual participation using process data in collaborative learning. It analyzes 66 studies published between 2005 and 2024, identified through the PRISMA process.&#xa0;Based on the analytical results, we propose a new theoretical framework, COMPAS, consisting of six components to model the multi-faceted and multi-level nature of individual participation in collaborative learning processes: <b>Co</b>gnitive interactions, <b>Co</b>ordinative interactions, <b>M</b>etacognitive interactions, <b>P</b>assive participation, solo <b>A</b>ctive participation, and <b>S</b>ocio-emotional interactions. The six forms of individual participation were studied using various forms of collaborative learning process data—including oral conversational data, textual input data, log data, and non-verbal physical data—with analytical methods primarily involving descriptive analysis, content analysis, network analysis, and clustering. The synthesized factors influencing individual participation reflect a bi-directional relationship between individual participation and group performance in collaborative learning. This study contributes a new theoretical framework for understanding different forms of individual participation in collaborative learning, as well as highlights the need&#xa0;for and importance of multimodal process data in collaborative learning analytics.</p>

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The CoMPAS Framework for Modeling Individual Participation in Collaborative Learning Processes: a Systematic Review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月12日

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  • Shihui Feng,
  • Ziyi Wei

摘要

Understanding individual participation is critical for uncovering how individuals learn in collaborative learning as well as for providing personalized support to scaffold team success and individual gains. Modelling individual participation requires a process-oriented method rather than an outcome-focused approach. There is a need for a theoretical framework guiding the collection and analysis of process data for gauging individual participation in collaborative learning. This systematic review synthesizes theoretical aspects and analytical methods for modelling individual participation using process data in collaborative learning. It analyzes 66 studies published between 2005 and 2024, identified through the PRISMA process. Based on the analytical results, we propose a new theoretical framework, COMPAS, consisting of six components to model the multi-faceted and multi-level nature of individual participation in collaborative learning processes: Cognitive interactions, Coordinative interactions, Metacognitive interactions, Passive participation, solo Active participation, and Socio-emotional interactions. The six forms of individual participation were studied using various forms of collaborative learning process data—including oral conversational data, textual input data, log data, and non-verbal physical data—with analytical methods primarily involving descriptive analysis, content analysis, network analysis, and clustering. The synthesized factors influencing individual participation reflect a bi-directional relationship between individual participation and group performance in collaborative learning. This study contributes a new theoretical framework for understanding different forms of individual participation in collaborative learning, as well as highlights the need for and importance of multimodal process data in collaborative learning analytics.