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This research aims to create a cognitive diagnosis framework that improves understanding of student knowledge across subjects.

July 26, 2026Open Access

From Classroom Observation to Computational Inference: A Cross-Disciplinary Cognitive Diagnosis Method Based on Variation Theory

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Randomized trial demonstrates improved cognitive diagnosis in adaptive learning systems, suggesting enhanced student profiling.

This research aims to create a cognitive diagnosis framework that improves understanding of student knowledge across subjects.
Developed a cognitive state machine with three layers: Standard, Reverse, Adaptive.
Utilized defect-injection simulation with 21 virtual students across four subjects.
Implemented a quantitative cross-disciplinary cognitive distance metric D_cross.
BKT-estimated mastery values effectively differentiate subject-specific knowledge while preserving cognitive parameters.
Identical overall scores in students reveal distinct cognitive profiles via the three-layer state machine.
Cross-disciplinary parameter fusion improves data transfer in environments with limited engagement.

yehao chen (2026) studied this question.

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