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
Abstract
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.
Cite
@article{arxiv.2409.00323,
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 and Jiyeong Bae and Yeonji Jung and Minji Kang and Gyuri Byun and Yeonseo Lee and Dohee Kim and Sookbun Lee and Jaekwon Park and Taekyung Ahn and Gunho Lee and Hyeoncheol Kim},
journal= {arXiv preprint arXiv:2409.00323},
year = {2024}
}
Comments
9 pages, 2 figures