From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education
<|reference_start|>From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education: Knowledge Tracing (KT) is a critical component in online learning, but traditional approaches face limitations in interpretability and cross-domain adaptability. This paper introduces Language Model-based Code Knowledge Tracing (CodeLKT), an innovative application of Language model-based Knowledge Tracing (LKT) to programming education. CodeLKT leverages pre-trained language models to process learning data, demonstrating superior performance over existing KT and Code KT models. We explore Domain Adaptive Pre-Training (DAPT) and Task Adaptive Pre-Training (TAPT), showing enhanced performance in the coding domain and investigating cross-domain transfer between mathematics and coding. Additionally, we present an theoretically-informed integrated system combining CodeLKT with large language models to generate personalized, in-depth feedback to support students' programming learning. This work advances the field of Code Knowledge Tracing by expanding the knowledge base with language model-based approach and offering practical implications for programming education through data-informed feedback.<|reference_end|>
@article{lee2024from,
    title={From Prediction to Application: Language Model-based Code Knowledge
  Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with
  Pedagogical Prompting for Comprehensive Programming Education},
    author={Unggi Lee, Jiyeong Bae, Yeonji Jung, Minji Kang, Gyuri Byun, Yeonseo
  Lee, Dohee Kim, Sookbun Lee, Jaekwon Park, Taekyung Ahn, Gunho Lee,
  Hyeoncheol Kim},
    journal={arXiv preprint arXiv:2409.00323},
    year={2024},
    doi={10.13140/RG.2.2.25134.11847},
    archivePrefix={arXiv},
    eprint={2409.00323},
    primaryClass={cs.CL cs.SE}
}