Open-ended tasks, such as coding problems that are common in computer science education, provide detailed insights into student knowledge. However, training large language models (LLMs) to simulate and predict possible student errors in their responses to these problems can be challenging: they often suffer from mode collapse and fail to fully capture the diversity in syntax, style, and solution approach in student responses. In this work, we present KASER (Knowledge-Aligned Student Error Simulator), a novel approach that aligns errors with student knowledge. We propose a training method based on reinforcement learning using a hybrid reward that reflects three aspects of student code prediction: i) code similarity to the ground-truth, ii) error matching, and iii) code prediction diversity. On two real-world datasets, we perform two levels of evaluation and show that: At the per-student-problem pair level, our method outperforms baselines on code and error prediction; at the per-problem level, our method outperforms baselines on error coverage and simulated code diversity.

翻译：开放式任务（如计算机科学教育中常见的编程问题）能够提供关于学生知识的详细洞察。然而，训练大型语言模型（LLMs）来模拟和预测学生在这些问题回答中可能出现的错误具有挑战性：这些模型常遭受模式崩溃问题，且难以充分捕捉学生回答在语法、风格及解题方法上的多样性。本文提出KASER（知识对齐学生错误模拟器），这是一种将错误与学生知识对齐的创新方法。我们提出一种基于强化学习的训练方法，采用混合奖励机制来反映学生代码预测的三个维度：i) 代码与标准答案的相似度，ii) 错误匹配度，以及iii) 代码预测的多样性。在两个真实数据集上，我们进行了两个层面的评估并证明：在“学生-问题”配对层面，本方法在代码与错误预测方面优于基线模型；在单问题层面，本方法在错误覆盖率和模拟代码多样性方面均超越基线表现。