Difficulty-Focused Contrastive Learning for Knowledge Tracing with a
  Large Language Model-Based Difficulty Prediction
Unggi Lee Sungjun Yoon Joon Seo Yun Kyoungsoo Park YoungHoon Jung Damji Stratton Hyeoncheol Kim
This paper presents novel techniques for enhancing the performance of
knowledge tracing (KT) models by focusing on the crucial factor of question and
concept difficulty level. Despite the acknowledged significance of difficulty,
previous KT research has yet to exploit its potential for model optimization
and has struggled to predict difficulty from unseen data. To address these
problems, we propose a difficulty-centered contrastive learning method for KT
models and a Large Language Model (LLM)-based framework for difficulty
prediction. These innovative methods seek to improve the performance of KT
models and provide accurate difficulty estimates for unseen data. Our ablation
study demonstrates the efficacy of these techniques by demonstrating enhanced
KT model performance. Nonetheless, the complex relationship between language
and difficulty merits further investigation.