Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing

Lixiang Xu

doi:10.1109/tlt.2026.3702387

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Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing

2026-06-10

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DOI：10.1109/tlt.2026.3702387

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Abstract

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Knowledge tracing (KT) is a core task in intelligent education. It aims to dynamically track students’ knowledge mastery and predict their learning performance based on their historical answering records. Most of the existing mainstream methods focus on static feature enhancement while ignoring the interference of noncognitive noises, such as careless errors and random guesses. Moreover, current models cannot adaptively adjust cognitive representations according to learners’ individual differences. Therefore, it is necessary to consider the expressiveness of cognitive representations from the perspectives of students’ personalized answering performance and cognitive laws. To address these issues, this article proposes an adaptive cognitive representation orchestration knowledge tracing (AOCR-KT) model. Specifically, we design an adaptive cognitive optimization module. This module integrates answering status and question difficulty and introduces a $p$-mechanism involving interval performance and continuity rules to compensate for the subjective bias and inaccuracy caused by revising the status only from the perspective of difficulty. The relevant parameters are fed into the adaptive dynamic programming algorithm to judge and revise the coherence and continuity of the answering status, gradually minimize the cost function, and drive the answering status toward the optimal solution. In addition, we construct a partition optimization module. Considering the staged characteristics of learners’ cognitive development, the answering sequence is divided into multiple intervals according to individual differences, and each interval is optimized independently before global optimization. Finally, relation embeddings generated via bipartite graph modeling are fused with the optimized cognitive representations to further enhance cognitive expression. Extensive experiments on three large-scale public educational datasets demonstrate that the AOCR-KT model outperforms state-of-the-art KT models, which fully validates its effectiveness and superiority.

Keywords:

Cognitive representation

dynamic programming

knowledge tracing (KT)

optimal solution

optimization algorithm