In education applications, knowledge tracing refers to the problem of estimating students' time-varying concept/skill mastery level from their past responses to questions and predicting their future performance. One key limitation of most existing knowledge tracing methods is that they treat student responses to questions as binary-valued, i.e., whether they are correct or incorrect. Response correctness analysis/prediction ignores important information on student knowledge contained in the exact content of the responses, especially for open-ended questions. In this paper, we conduct the first exploration into open-ended knowledge tracing (OKT) by studying the new task of predicting students' exact open-ended responses to questions. Our work is grounded in the domain of computer science education with programming questions. We develop an initial solution to the OKT problem, a student knowledge-guided code generation approach, that combines program synthesis methods using language models with student knowledge tracing methods. We also conduct a series of quantitative and qualitative experiments on a real-world student code dataset to validate OKT and demonstrate its promise in educational applications.

翻译：在教育应用中，知识追踪指根据学生过往对问题的作答情况，估计其随时间变化的概念/技能掌握水平，并预测其未来表现的任务。现有大多数知识追踪方法的一个关键局限在于，它们将学生对问题的作答视为二元值，即仅关注正确或错误。作答正确性分析/预测忽略了作答具体内容中所蕴含的学生知识信息，尤其是针对开放式问题时。本文首次探索开放式知识追踪（OKT），通过研究预测学生对问题精确开放式作答这一新任务展开工作。我们的研究以计算机科学教育领域中的编程问题为基础。针对OKT问题，我们提出了一种初始解决方案——学生知识引导的代码生成方法，该方法将基于语言模型的程序合成方法与知识追踪方法相结合。我们还利用真实学生代码数据集进行了一系列定量与定性实验，以验证OKT的有效性，并展示其在教育应用中的潜力。