Capturing Multidimensional Engagement Within Small Groups: A Multimodal Perspective

This study develops and validates a human-centered approach to measuring small-group, multidimensional engagement in a game-based Computer-Supported Collaborative Learning (CSCL) environment. Grounded in sociocultural perspectives, the proposed framework integrates multimodal data sources, including discourse, interaction logs, and linguistic indicators, to assess behavioral, collaborative, and disciplinary engagement through theory-informed human interpretation. Human-annotated engagement ratings serve as ground truth for evaluating whether supervised machine learning models can approximate multimodal interpretive judgments, establishing a pathway toward scalable assessment. Three supervised learning algorithms were compared to examine model performance across engagement dimensions, and multiple regression analysis was conducted to investigate associations between quest-level average engagement and group learning outcomes. Results indicate that average collaborative and disciplinary engagement ratings positively predict group performance, whereas behavioral engagement alone does not significantly relate to learning outcomes. Qualitative analysis of group responses further illustrates these associations. Methodologically and theoretically, the study advances multimodal learning analytics by proposing a generalizable, human-centered workflow that integrates multimodal interpretation with model-assisted scalability, emphasizing the essential role of human interpretation in producing valid and contextually grounded engagement assessments in collaborative learning environments.