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Improving Question Embeddings With Cognitive Representation Optimization for Knowledge Tracing
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AI
DOI：10.1109/TCYB.2025.3612399.png)
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Abstract
En 中文
The knowledge tracing (KT) aims to track changes in students’ knowledge status and predict their future answers based on their historical answer records. Current research on KT modeling focuses on predicting student’ future performance based on existing, unupdated records of student learning interactions. However, these approaches ignore the distractors (such as slipping and guessing) in the answering process and overlook that static cognitive representations are temporary and limited. Most of them assume that there are no distractors in the answering process and that the record representations fully represent the students’ level of understanding and proficiency in knowledge. In this case, it may lead to many lack of synergy and incoordination issue in the original records. Therefore we propose a cognitive representation optimization for KT (CRO-KT) model, which utilizes a dynamic programming algorithm to optimize structure of cognitive representations. This ensures that the structure matches the students’ cognitive patterns in terms of the difficulty of the exercises. Furthermore, we use the co-optimization algorithm to optimize the cognitive representations of the subtarget exercises in terms of the overall situation of exercises responses by considering all the exercises with co-relationships as a single goal. Meanwhile, the CRO-KT model fuses the learned relational embeddings from the bipartite graph with the optimized record representations in a weighted manner, enhancing the expression of students’ cognition. Finally, experiments are conducted on three publicly available datasets respectively to validate the effectiveness of the proposed cognitive representation optimization model. The source code of CRDP-KT is available at https://github.com/bigdata-graph/CRO-KT.
Keywords:
Cognitive representation
knowledge tracing (KT)
optimal solution
optimization algorithm
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10.5
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1.1W
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5.0W
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