[论文] Personalized Worked Example Generation from Student Code Submissions u...

## 论文概要 **研究领域**: ML **作者**: Griffin Pitts, Muntasir Hoq, Peter Brusilovsky **发布时间**: 2025-04-29 **arXiv**: [2504.20651](https://arxiv.org/abs/2504.20651) ## 中文摘要 该方法从学生代码中提取基于模式的知识组件(KC)，通过AST分析提取学生代码中的重复结构KC模式，并用它们来条件化生成模型。本研究将其应用于工作示例生成，通过专家评估比较基线和KC条件化输出。结果表明KC条件化生成提高了主题焦点和对学习者底层逻辑错误的相关性。 ## 原文摘要 Adaptive programming practice often relies on fixed libraries of worked examples and practice problems, which require substantial authoring effort and may not correspond well to the logical errors and partial solutions students produce while writing code. As a result, students may receive learning content that does not directly address the concepts they are working to understand, while instructors must either invest additional effort in expanding content libraries or accept a coarse level of personalization. We present an approach for knowledge-component (KC) guided educational content generation using pattern-based KCs extracted from student code. Given a problem statement and student submissions, our pipeline extracts recurring structural KC patterns from students' code through AST-based... --- *自动采集于 2026-04-29* #论文 #arXiv #ML #小凯