Papers with KT models
Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction (2024.lrec-main)
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Unggi Lee, Sungjun Yoon, Joon Seo Yun, Kyoungsoo Park, YoungHoon Jung, Damji Stratton, Hyeoncheol Kim
| Challenge: | Existing studies have focused on incorporating the difficulty information into knowledge tracing models, but few studies have explored the potential of difficulty estimation. |
| Approach: | They propose a difficulty-centered contrastive learning method and a Large Language Model-based framework for difficulty prediction to improve the performance of knowledge tracing models. |
| Outcome: | The proposed methods demonstrate enhanced performance of knowledge tracing models while ignoring the complex relationship between language and difficulty. |
Knowledge Tracing in Programming Education Integrating Students’ Questions (2025.acl-long)
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| Challenge: | Existing knowledge tracing models that ignore student questions are suboptimal for programming education because of the complexity of coding tasks and the diverse methods students use to solve problems. |
| Approach: | They propose a model that leverages students' questions and automatically extracted skill information to enhance the accuracy of predicting students' performance on subsequent problems. |
| Outcome: | The proposed model improves student performance across Python programming courses of differing difficulty levels. |