Enhanced Interpretable Knowledge Tracing for Students Performance Prediction with Human understandable Feature Space

• URL: http://arxiv.org/abs/2509.18231v1
• Date: Mon, 22 Sep 2025 13:47:28 GMT
• Title: Enhanced Interpretable Knowledge Tracing for Students Performance Prediction with Human understandable Feature Space
• Authors: Sein Minn, Roger Nkambou,
• Abstract summary: Knowledge Tracing (KT) plays a central role in assessing students skill mastery and predicting their future performance.<n>Our contributions aim to balance predictive power with interpretability, advancing the utility of adaptive learning systems.
• Score: 2.053999499982333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge Tracing (KT) plays a central role in assessing students skill mastery and predicting their future performance. While deep learning based KT models achieve superior predictive accuracy compared to traditional methods, their complexity and opacity hinder their ability to provide psychologically meaningful explanations. This disconnect between model parameters and cognitive theory poses challenges for understanding and enhancing the learning process, limiting their trustworthiness in educational applications. To address these challenges, we enhance interpretable KT models by exploring human-understandable features derived from students interaction data. By incorporating additional features, particularly those reflecting students learning abilities, our enhanced approach improves predictive accuracy while maintaining alignment with cognitive theory. Our contributions aim to balance predictive power with interpretability, advancing the utility of adaptive learning systems.

Related papers

• When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
  We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
  arXiv  Detail & Related papers  (2025-06-05T20:48:16Z)
• Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing [125.75923987618977]
  We propose the Cognitive Representation Dynamic Programming based Knowledge Tracing (CRDP-KT) model.<n>It is a dynamic programming algorithm to optimize cognitive representations based on the difficulty of the questions and the performance intervals between them.<n>It provides more accurate and systematic input features for subsequent model training, thereby minimizing distortion in the simulation of cognitive states.
  arXiv  Detail & Related papers  (2025-06-03T14:44:48Z)
• Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training [86.70255651945602]
  We introduce a novel inference-time steering methodology called Reinforcing Cognitive Experts (RICE)<n>RICE aims to improve reasoning performance without additional training or complexs.<n> Empirical evaluations with leading MoE-based LRMs demonstrate noticeable and consistent improvements in reasoning accuracy, cognitive efficiency, and cross-domain generalization.
  arXiv  Detail & Related papers  (2025-05-20T17:59:16Z)
• Improving Question Embeddings with Cognitive Representation Optimization for Knowledge Tracing [77.14348157016518]
  Research on KT modeling focuses on predicting future student performance based on existing, unupdated records of student learning interactions.<n>We propose a knowledge-tracking cognitive representation optimization (CRO-KT) model that uses dynamic programming algorithms to optimize the structure of cognitive representation.
  arXiv  Detail & Related papers  (2025-04-05T09:32:03Z)
• DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing [51.665582274736785]
  Knowledge Tracing (KT) predicts future performance by students' historical computation, and understanding students' affective states can enhance the effectiveness of KT.<n>We propose Affect Dynamic Knowledge Tracing (DASKT) to explore the impact of various student affective states on their knowledge states.<n>Our research highlights a promising avenue for future studies, focusing on achieving high interpretability and accuracy.
  arXiv  Detail & Related papers  (2025-01-18T10:02:10Z)
• Mimicking Human Intuition: Cognitive Belief-Driven Reinforcement Learning [0.0]
  We propose an innovative framework inspired by cognitive principles: Cognitive Belief-Driven Reinforcement Learning (CBD-RL)<n>CBD-RL transforms conventional trial-and-error learning into a more structured and guided learning paradigm, simulating the human reasoning process.<n>The concrete implementations of this framework, CBDQ, CBDPPO, and CBDSAC, demonstrate superior performance in discrete and continuous action spaces.
  arXiv  Detail & Related papers  (2024-10-02T16:50:29Z)
• Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing [16.354428270912138]
  We propose a Concept-driven Personalized Forgetting knowledge tracing model (CPF) CPF integrates hierarchical relationships between knowledge concepts and incorporates students' personalized cognitive abilities. Our CPF outperforms current forgetting curve theory based methods in predicting student performance.
  arXiv  Detail & Related papers  (2024-04-18T12:28:50Z)
• Predictive, scalable and interpretable knowledge tracing on structured domains [6.860460230412773]
  PSI-KT is a hierarchical generative approach that explicitly models how both individual cognitive traits and the prerequisite structure of knowledge influence learning dynamics. PSI-KT targets the real-world need for efficient personalization even with a growing body of learners and learning histories.
  arXiv  Detail & Related papers  (2024-03-19T22:19:29Z)
• A Novel Neural-symbolic System under Statistical Relational Learning [47.30190559449236]
  We propose a neural-symbolic framework based on statistical relational learning, referred to as NSF-SRL.<n>Results of symbolic reasoning are utilized to refine and correct the predictions made by deep learning models, while deep learning models enhance the efficiency of the symbolic reasoning process.<n>We believe that this approach sets a new standard for neural-symbolic systems and will drive future research in the field of general artificial intelligence.
  arXiv  Detail & Related papers  (2023-09-16T09:15:37Z)
• Differentiating Student Feedbacks for Knowledge Tracing [28.669001606806525]
  We propose a framework to reweight the contribution of different responses based on their discrimination in training.<n>We also introduce an adaptive predictive score fusion technique to maintain accuracy on less discriminative responses.
  arXiv  Detail & Related papers  (2022-12-16T13:55:07Z)
• Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations [21.74631969428855]
  Interpretable Knowledge Tracing (IKT) is a simple model that relies on three meaningful latent features. IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes (TAN) IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.
  arXiv  Detail & Related papers  (2021-12-15T19:05:48Z)
• Counterfactual Representation Learning with Balancing Weights [74.67296491574318]
  Key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. We develop an algorithm for flexible, scalable and accurate estimation of causal effects.
  arXiv  Detail & Related papers  (2020-10-23T19:06:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.