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Fine-Grained Intelligent Learning Diagnosis Model Based on the Exercise-Knowledge-Cognition Tensor for Educational
Chunyan Zeng1, Yulin Hou2, Zhifeng Wang2
1Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China.
Behavioral Sciences (Basel, Switzerland)
|May 27, 2026
Summary
This study introduces a new AI model for educational assessment, RLDM-EKC, which diagnoses learning by analyzing cognitive processes. It provides interpretable, multi-level student profiles for better teaching feedback.
Area of Science:
- Artificial Intelligence in Education
- Cognitive Science
- Educational Measurement
Background:
- Current AI diagnostic models lack pedagogical interpretation due to binary knowledge states.
- Existing models struggle to encode theory-aligned cognitive-process demands.
- Need for interpretable learning diagnosis in AI-enabled educational assessment.
Purpose of the Study:
- Operationalize revised Bloom's taxonomy at the exercise-knowledge level.
- Develop RLDM-EKC, a DINA-type model for inferring ordered knowledge-cognition profiles.
- Enhance pedagogical interpretation of diagnosed learning profiles.
Main Methods:
- Constructed an Exercise-Knowledge-Cognition (EKC) tensor.
- Developed RLDM-EKC model inferring ordered knowledge-cognition profiles.
- Employed Expectation-Maximization for parameter estimation and MAP inference for profile derivation.
Main Results:
- RLDM-EKC achieved 81.7% PMR and 91.6% AAMR in simulations.
- Empirical validation on TIMSS 2007 Grade 4 mathematics data.
- Generated theory-aligned multi-level cognitive profiles with uncertainty reporting.
Conclusions:
- RLDM-EKC offers accurate and interpretable learning diagnosis.
- The model supports actionable, human-in-the-loop feedback for educators and learners.
- Addresses limitations of classical cognitive diagnostic models in educational assessment.
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