Cuff-KT: Tackling Learners' Real-time Learning Pattern Adjustment via Tuning-Free Knowledge State Guided Model Updating

2025-05-26

AI Summary

Existing knowledge tracing (KT) models struggle to handle abrupt knowledge state shifts caused by cognitive fatigue, motivational fluctuations, and other transient learner factors, exhibiting limited dynamic adaptability to real-time learning pattern adjustment (RLPA). To address this, we propose a fine-tuning-free knowledge-state-guided model update framework featuring a novel controller-generator architecture. Leveraging meta-learning and explicit knowledge state modeling, it enables instantaneous, personalized parameter generation without retraining. We formally define the RLPA task for the first time and introduce a dynamic value-scoring controller alongside cross- and single-learner adaptive strategies. Evaluated on five subject-domain datasets, our method improves the AUC of five mainstream KT models by 10% (intra-group) and 4% (inter-group), with negligible computational overhead. All code and data are publicly released.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

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

Abstract

Knowledge Tracing (KT) is a core component of Intelligent Tutoring Systems, modeling learners' knowledge state to predict future performance and provide personalized learning support. Traditional KT models assume that learners' learning abilities remain relatively stable over short periods or change in predictable ways based on prior performance. However, in reality, learners' abilities change irregularly due to factors like cognitive fatigue, motivation, and external stress -- a task introduced, which we refer to as Real-time Learning Pattern Adjustment (RLPA). Existing KT models, when faced with RLPA, lack sufficient adaptability, because they fail to timely account for the dynamic nature of different learners' evolving learning patterns. Current strategies for enhancing adaptability rely on retraining, which leads to significant overfitting and high time overhead issues. To address this, we propose Cuff-KT, comprising a controller and a generator. The controller assigns value scores to learners, while the generator generates personalized parameters for selected learners. Cuff-KT controllably adapts to data changes fast and flexibly without fine-tuning. Experiments on five datasets from different subjects demonstrate that Cuff-KT significantly improves the performance of five KT models with different structures under intra- and inter-learner shifts, with an average relative increase in AUC of 10% and 4%, respectively, at a negligible time cost, effectively tackling RLPA task. Our code and datasets are fully available at https://github.com/zyy-2001/Cuff-KT.

Problem

• Addressing learners' dynamic learning pattern changes in real-time
• Overcoming adaptability limitations of traditional Knowledge Tracing models
• Reducing overfitting and time costs in model retraining processes

Innovation

• Tuning-free knowledge state guided model updating
• Controller assigns value scores to learners
• Generator creates personalized parameters dynamically