用潜类别分析简化多模态学习数据，发现四类协作模式。
From Complexity to Parsimony: Integrating Latent Class Analysis to Uncover Multimodal Learning Patterns in Collaborative Learning
- 通过潜类别分析将17个单模态指标整合为4类简约多模态行为模式。
- 新方法在解释学生任务表现时比原始指标更简洁且解释力更强。
- 适合教育技术研究者和协作学习干预设计者参考使用。
多模态学习分析（MMLA）利用先进传感技术和人工智能捕捉复杂学习过程，但如何整合多元数据仍具挑战。本研究提出一种新方法，将潜类别分析（LCA）引入MMLA，将单模态行为指标整合为简洁的多模态指标。基于高保真医疗模拟场景，采集位置、音频和生理数据，提取17个单模态指标。LCA识别出四类潜在类别：协作沟通、具身协作、远距互动和独自参与，每类均体现独特单模态模式。知识网络分析对比显示，该多模态方法更具简洁性，且对学生成绩与协作表现的解释力更高。结果表明，LCA有助于简化复杂多模态数据分析，同时捕捉跨模态细微行为，为教育者提供可操作洞察，并推动协作学习干预设计。本研究为提升MMLA的简洁性与可管理性提供了可行路径，契合以学习者为中心的教育理念。
原文摘要 · Abstract (English)
Multimodal Learning Analytics (MMLA) leverages advanced sensing technologies and artificial intelligence to capture complex learning processes, but integrating diverse data sources into cohesive insights remains challenging. This study introduces a novel methodology for integrating latent class analysis (LCA) within MMLA to map monomodal behavioural indicators into parsimonious multimodal ones. Using a high-fidelity healthcare simulation context, we collected positional, audio, and physiological data, deriving 17 monomodal indicators. LCA identified four distinct latent classes: Collaborative Communication, Embodied Collaboration, Distant Interaction, and Solitary Engagement, each capturing unique monomodal patterns. Epistemic network analysis compared these multimodal indicators with the original monomodal indicators and found that the multimodal approach was more parsimonious while offering higher explanatory power regarding students' task and collaboration performances. The findings highlight the potential of LCA in simplifying the analysis of complex multimodal data while capturing nuanced, cross-modality behaviours, offering actionable insights for educators and enhancing the design of collaborative learning interventions. This study proposes a pathway for advancing MMLA, making it more parsimonious and manageable, and aligning with the principles of learner-centred education.
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