Abstract:Existing learning analytics approaches, which often model learning processes as sequences of learner actions or homogeneous relationships, are limited in capturing the distributed, multi-typed interactions in contemporary learning environments. To address this, we propose Heterogeneous Interaction Network Analysis (HINA), a multi-level learning analytics framework for modelling interactions and associations across diverse entities (e.g., learners, behaviours, and AI agents) in learning processes. Grounded in network science principles, HINA integrates a multi-level analytical framework that analyzes individual process metrics (node-level), prominent associations (dyad-level), and latent clusters (meso-level) to address questions about how different elements in a learning environment interact and co-influence each other. In this paper, we first detail the theoretical and mathematical foundations of HINA for individual, dyadic, and meso-level analysis. We then demonstrate HINA's utility through a case study on AI-assisted small-group collaborative learning, revealing students' interaction profiles with peers versus AI, distinct engagement patterns that emerge from these interactions, and specific types of learning behaviors (e.g., asking questions) directed to AI versus peers. HINA contributes a novel and unified analytical framework that enables researchers to quantify individual-level processes, identify significant associations, and uncover meaningful clusters within a single workflow. Accompanied by a dedicated web tool, HINA provides a complete workflow for supporting process-based assessment, and enables a new theoretical lens for modeling and understanding complex and mediated learning processes.
From: Shihui Feng [view email]
[v1]
Sun, 11 Jan 2026 04:07:56 UTC (1,105 KB)
[v2]
Wed, 22 Apr 2026 14:39:59 UTC (1,680 KB)
[v3]
Wed, 16 Sep 2026 11:35:03 UTC (1,913 KB)
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