用网络模型分析学习过程中的行为模式，揭示关键事件与群体动态。
Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning Processes
- 结合随机过程挖掘与概率图模型，建模学习行为转移路径。
- 在191名学生的小组协作中识别出显著的调节性行为模式。
- 适合教育研究者分析学习过程的时序演化与群体互动。
本文提出一种新型学习分析方法——转移网络分析（TNA），融合随机过程挖掘与概率图表示，用于建模、可视化和识别学习过程数据中的转移模式。该方法将关系与时间维度整合为单一视角，超越单一框架能力，包括利用中心性捕捉关键学习事件、通过社区检测识别行为模式、借助聚类揭示时间规律。此外，TNA引入多种显著性检验，增强分析严谨性。本文阐述了TNA的理论与数学基础，并以191名学生参与小组协作的案例研究，基于协同调节与社会共享调节学习理论，分析群体动态模式。结果表明，TNA可有效映射调节过程，识别重要事件、模式与聚类。自助法验证确认了显著转移，剔除了虚假转移。因此，TNA能捕捉学习动态，提供稳健框架以探究学习过程的时序演变。未来方向包括扩展估计方法、可靠性评估及构建纵向TNA。
原文摘要 · Abstract (English)
This paper presents a novel learning analytics method: Transition Network Analysis (TNA), a method that integrates Stochastic Process Mining and probabilistic graph representation to model, visualize, and identify transition patterns in the learning process data. Combining the relational and temporal aspects into a single lens offers capabilities beyond either framework, including centralities to capture important learning events, community detection to identify behavior patterns, and clustering to reveal temporal patterns. Furthermore, TNA introduces several significance tests that go beyond either method and add rigor to the analysis. Here, we introduce the theoretical and mathematical foundations of TNA and we demonstrate the functionalities of TNA with a case study where students (n=191) engaged in small-group collaboration to map patterns of group dynamics using the theories of co-regulation and socially-shared regulated learning. The analysis revealed that TNA can map the regulatory processes as well as identify important events, patterns, and clusters. Bootstrap validation established the significant transitions and eliminated spurious transitions. As such, TNA can capture learning dynamics and provide a robust framework for investigating the temporal evolution of learning processes. Future directions include -- inter alia -- expanding estimation methods, reliability assessment, and building longitudinal TNA.
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