mlsanityData to 7 Oct 2026

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

arXiv 2409.00323 ↗First posted 31 Aug 2024cs.CL cs.SEcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 31 Aug 2024.

Builds on

21 of 40 references.

References · 21

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9 more
  1. 2016Dynamic Key-Value Memory Networks for Knowledge Tracingcited by 170
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  4. 2021A Survey of Knowledge Tracing: Models, Variants, and Applicationscited by 29
  5. 2024Language Model Can Do Knowledge Tracing: Simple but Effective Method to Integrate Language Model and Knowledge Tracing Taskcited by 20
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  1. 2025A Fuzzy Logic Prompting Framework for Large Language Models in Adaptive and Uncertain Taskscited by 1