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

This paper introduces CodeLKT, a language model-based knowledge tracing system for programming education that predicts student performance and generates personalized feedback using large language models with pedagogically-informed prompting strategies. The system tracks student knowledge states over time and provides adaptive hints and correctness feedback based on code submissions and learning history.

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

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