用大模型追踪编程学习进度，自动给出个性化反馈。
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
- 基于预训练大模型构建代码知识追踪系统
- 在编程与数学间实现跨领域知识迁移，准确率提升12.3%
- 结合教学提示生成深度反馈，适合教育科技研发者
知识追踪（KT）是在线学习的关键组件，但传统方法在可解释性和跨领域适应性方面存在局限。本文提出语言模型驱动的代码知识追踪（CodeLKT），将语言模型应用于编程教育中的知识追踪任务。CodeLKT利用预训练语言模型处理学习数据，在性能上超越现有KT和代码知识追踪模型。研究探索了领域自适应预训练（DAPT）和任务自适应预训练（TAPT），验证了其在编程领域的优势，并考察了数学与编程之间的跨领域迁移能力。此外，提出一个融合大语言模型的集成系统，通过教学提示生成个性化、深入的反馈，支持学生编程学习。该工作通过语言模型扩展知识追踪的知识库，并为编程教育提供数据驱动的实践启示。
原文摘要 · Abstract (English)
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.
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