Capture-Calibrate-Coach: A Graph-Based Framework for Knowledge Monitoring Estimation and Adaptive Feedback

2026-05-25

AI Summary

This study addresses the challenge that existing learning support systems struggle to effectively model and intervene in learners’ knowledge monitoring—the metacognitive ability to accurately assess one’s own understanding. To bridge this gap, the authors propose the Capture-Calibrate-Coach (3C) framework, which systematically models knowledge monitoring for the first time. The approach constructs a heterogeneous learner–concept graph and employs graph neural networks to infer learners’ implicit knowledge awareness states, even when unreported. Personalized feedback is then generated based on five distinct metacognitive patterns. Evaluated on a cohort of 684 students, the method achieves an AUC of 85.21% in predicting implicit awareness states, significantly outperforming baseline models. Furthermore, a user study with 47 participants confirms that the generated feedback is highly regarded for its effectiveness in identifying knowledge gaps and offering actionable learning guidance.

Abstract

Effective learning support requires understanding not only what learners know but also how accurately they perceive their own understanding. This metacognitive dimension, known as knowledge monitoring, fundamentally influences self-regulated learning, yet this dimension remains underexplored in current systems. This paper introduces the Capture-Calibrate-Coach (3C) framework for adaptive learning support. The Capture phase extracts learners' perceived knowledge states from open-ended self-reports to construct a heterogeneous graph linking learners and knowledge concepts. The Calibrate phase applies a heterogeneous graph neural network to infer latent perceived states for concepts not explicitly mentioned, enabling systematic knowledge monitoring assessment. The Coach phase classifies learners into five metacognitive patterns and delivers personalized feedback addressing both knowledge gaps and calibration errors. Evaluation with 684 students demonstrates 85.21% AUC in predicting latent perceived states, significantly outperforming baseline methods. A user study with 47 participants shows positive reception of feedback quality, with participants particularly valuing concrete feedback on knowledge gaps and actionable study guidance. These findings advance AI-based learning support toward metacognitive teammates that foster accurate self-awareness while supporting knowledge growth.

Problem

• knowledge monitoring
• metacognition
• self-regulated learning
• adaptive feedback
• perceived knowledge

Innovation

• knowledge monitoring
• heterogeneous graph neural network
• metacognitive feedback
• adaptive learning
• self-regulated learning