Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing

2025-06-03

AI Summary

Existing knowledge tracing (KT) methods suffer from fragmented cognitive representations and non-cognitive interference (e.g., guessing, slipping), hindering accurate modeling of the continuity and consistency of students’ latent cognitive states—leading to high prediction bias and substantial modeling overhead. To address this, we propose the first dynamic programming–based cognitive representation optimization framework for KT. Our approach models the sequential evolution of cognitive states driven by item difficulty and response timing. It introduces a piecewise optimization strategy and a bipartite graph–based relational weighting mechanism to jointly suppress non-cognitive noise and enhance cognitive consistency. Evaluated on three public benchmark datasets, our model achieves significant improvements in prediction accuracy and substantial reduction in estimation bias. Empirical results demonstrate that explicitly modeling cognitive continuity yields substantial gains in KT performance, validating the effectiveness and necessity of our principled, optimization-driven design.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Preferences
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Knowledge Tracing (KT) involves monitoring the changes in a student's knowledge over time by analyzing their past responses, with the goal of predicting future performance. However, most existing methods primarily focus on feature enhancement, while overlooking the deficiencies in cognitive representation and the ability to express cognition-issues often caused by interference from non-cognitive factors such as slipping and guessing. This limitation hampers the ability to capture the continuity and coherence of the student's cognitive process. As a result, many methods may introduce more prediction bias and modeling costs due to their inability to maintain cognitive continuity and coherence. Based on the above discussion, we propose the Cognitive Representation Dynamic Programming based Knowledge Tracing (CRDP-KT) model. This model em ploys a dynamic programming algorithm to optimize cognitive representations based on the difficulty of the questions and the performance intervals between them. This approach ensures that the cognitive representation aligns with the student's cognitive patterns, maintaining overall continuity and coherence. As a result, it provides more accurate and systematic input features for subsequent model training, thereby minimizing distortion in the simulation of cognitive states. Additionally, the CRDP-KT model performs partitioned optimization of cognitive representations to enhance the reliability of the optimization process. Furthermore, it improves its ability to express the student's cognition through a weighted fusion of optimized record representations and re lationships learned from a bipartite graph. Finally, experiments conducted on three public datasets validate the effectiveness of the proposed CRDP-KT model.

Problem

• Enhancing cognitive representation in Knowledge Tracing
• Reducing prediction bias from non-cognitive factors
• Maintaining cognitive continuity and coherence

Innovation

• Dynamic programming optimizes cognitive representations
• Partitioned optimization enhances reliability
• Weighted fusion improves cognition expression