用教师经验构建技能分类，提前发现编程困难学生
Detecting Struggling Student Programmers using Proficiency Taxonomies
- 基于师生共建的技能分类体系，分析编码历史识别学生能力
- 在两个初级课程数据集上预测准确率超越现有最优模型
- 适合教育AI、学习分析领域研究者参考
早期发现编程学习困难的学生对提供个性化支持至关重要。尽管已有多种基于AI的方法用于该问题，但它们未在模型中显式地推理学生的编程能力。本研究与教育工作者合作，构建了一个技能分类体系，用于刻画学生解决编程任务的方式，并将其嵌入检测模型中。提出的熟练度分类模型（PTM）能够同时根据学生的编码历史学习其编程能力，并预测其在新任务中是否会出现困难。我们在两个来自初学者Java和Python课程的数据集上对PTM模型进行了全面评估。实验结果表明，PTM在预测学习困难学生方面优于当前最先进的模型。论文展示了结合教师结构化洞见，在编程学习初期识别需要帮助学生方面的潜力。
原文摘要 · Abstract (English)
Early detection of struggling student programmers is crucial for providing them with personalized support. While multiple AI-based approaches have been proposed for this problem, they do not explicitly reason about students' programming skills in the model. This study addresses this gap by developing in collaboration with educators a taxonomy of proficiencies that categorizes how students solve coding tasks and is embedded in the detection model. Our model, termed the Proficiency Taxonomy Model (PTM), simultaneously learns the student's coding skills based on their coding history and predicts whether they will struggle on a new task. We extensively evaluated the effectiveness of the PTM model on two separate datasets from introductory Java and Python courses for beginner programmers. Experimental results demonstrate that PTM outperforms state-of-the-art models in predicting struggling students. The paper showcases the potential of combining structured insights from teachers for early identification of those needing assistance in learning to code.
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