A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
In: LAK22: 12th International Learning Ana-lytics and Knowledge Conference
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A method combining pretrained YOLO11, YOLOE-26, and Gaze-LLE models detects student gaze targets in collaborative learning videos with F1-score 0.829 without requiring labeled training data.
CTGAN and LLMs generate synthetic student data that passes statistical and predictive utility checks for learning analytics.
citing papers explorer
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What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
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Gaze to Insight: A Scalable AI Approach for Detecting Gaze Behaviours in Face-to-Face Collaborative Learning
A method combining pretrained YOLO11, YOLOE-26, and Gaze-LLE models detects student gaze targets in collaborative learning videos with F1-score 0.829 without requiring labeled training data.
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Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation
CTGAN and LLMs generate synthetic student data that passes statistical and predictive utility checks for learning analytics.