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<p>Understanding how multiple dimensions of learning engagement co-develop during collaborative programming remains a critical challenge. Drawing on the four-dimensional engagement framework encompassing behavioral, cognitive, emotional, and social components, this study employs multimodal learning analytics (MMLA) to investigate the dynamic interplay among engagement dimensions, prior knowledge, and leadership type in a university-level collaborative programming course. Group-level ICAP (Interactive, Constructive, Active, Passive) modes were coded from multimodal interaction data, while learning engagement was assessed via integrated behavioral, cognitive, emotional, and social indicators. Findings reveal that prior knowledge supports individual task execution but contributes to collaborative engagement only under strong leadership. The three core dimensions of learning engagement—behavioral, cognitive, and social—were found to be significantly interrelated, reflecting a tightly coupled system of action, thinking, and peer interaction. In contrast, emotional engagement showed weak or inconsistent correlations with the other dimensions. Furthermore, interactive discourse fostered richer engagement and higher achievement, with group formation strategies moderating these effects through the interplay of prior knowledge and leadership type. While ICAP modes and overall learning engagement were strongly correlated—partly reflecting overlapping cognitive components—ICAP captured discourse patterns, whereas engagement scores aggregated multidimensional indicators across the task, suggesting complementary rather than redundant constructs. These findings advance engagement research by integrating discourse classification and multidimensional profiling, offering practical guidance on group formation, scaffolding, and real-time engagement monitoring to enhance collaborative programming pedagogy.</p>

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Understanding collaborative programming dynamics: The role of prior knowledge, engagement and ICAP learning modes

  • 期刊论文

  • 发表日期：

    2025年11月6日

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  • Jihong Ding,
  • Zhihao Fan,
  • Huazhong Liu,
  • James Slotta,
  • Feng Ji,
  • Kefeng Hu

摘要

Understanding how multiple dimensions of learning engagement co-develop during collaborative programming remains a critical challenge. Drawing on the four-dimensional engagement framework encompassing behavioral, cognitive, emotional, and social components, this study employs multimodal learning analytics (MMLA) to investigate the dynamic interplay among engagement dimensions, prior knowledge, and leadership type in a university-level collaborative programming course. Group-level ICAP (Interactive, Constructive, Active, Passive) modes were coded from multimodal interaction data, while learning engagement was assessed via integrated behavioral, cognitive, emotional, and social indicators. Findings reveal that prior knowledge supports individual task execution but contributes to collaborative engagement only under strong leadership. The three core dimensions of learning engagement—behavioral, cognitive, and social—were found to be significantly interrelated, reflecting a tightly coupled system of action, thinking, and peer interaction. In contrast, emotional engagement showed weak or inconsistent correlations with the other dimensions. Furthermore, interactive discourse fostered richer engagement and higher achievement, with group formation strategies moderating these effects through the interplay of prior knowledge and leadership type. While ICAP modes and overall learning engagement were strongly correlated—partly reflecting overlapping cognitive components—ICAP captured discourse patterns, whereas engagement scores aggregated multidimensional indicators across the task, suggesting complementary rather than redundant constructs. These findings advance engagement research by integrating discourse classification and multidimensional profiling, offering practical guidance on group formation, scaffolding, and real-time engagement monitoring to enhance collaborative programming pedagogy.