HINA introduces heterogeneous interaction networks to model and analyze multi-entity learning processes at individual, dyadic, and group levels, demonstrated via a case study on AI-mediated collaborative learning.
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2 Pith papers cite this work. Polarity classification is still indexing.
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AI-assisted code review in student projects increased iterative activity and supported code quality discussions while preserving engagement levels across two cohorts.
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Heterogeneous Interaction Network Analysis (HINA): A New Learning Analytics Approach for Modelling, Analyzing, and Visualizing Complex Interactions in Learning Processes
HINA introduces heterogeneous interaction networks to model and analyze multi-entity learning processes at individual, dyadic, and group levels, demonstrated via a case study on AI-mediated collaborative learning.
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AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report
AI-assisted code review in student projects increased iterative activity and supported code quality discussions while preserving engagement levels across two cohorts.