mlsanityData to 14 Oct 2026

Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing

arXiv 2506.02949 ↗First posted 3 Jun 2025cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 56 references that are in the map. First posted on arXiv 3 Jun 2025.

Builds on

20 of 56 references.

References · 20

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  5. 2018Deep Learning on Graphs: A Surveycited by 393
  6. 2015Deep Knowledge Tracingcited by 332
  7. 2020Context-Aware Attentive Knowledge Tracingcited by 174
  8. 2019A Self-Attentive model for Knowledge Tracingcited by 172
  9. 2016Dynamic Key-Value Memory Networks for Knowledge Tracingcited by 171
  10. 2020Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracingcited by 81
  11. 2020RKT : Relation-Aware Self-Attention for Knowledge Tracingcited by 62
  12. 2019Knowledge Tracing with Sequential Key-Value Memory Networkscited by 60
8 more
  1. 2020SAINT+: Integrating Temporal Features for EdNet Correctness Predictioncited by 57
  2. 2020Improving Knowledge Tracing via Pre-training Question Embeddingscited by 36
  3. 2022Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracingcited by 23
  4. 2016Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimationcited by 17
  5. 2021Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracingcited by 17
  6. 2020HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracingcited by 12
  7. 2021Last Query Transformer RNN for knowledge tracingcited by 5
  8. 2023Solving Expensive Optimization Problems in Dynamic Environments with Meta-learningcited by 4

Built on by

Not cited yet.