Open-ended Knowledge Tracing for Computer Science Education
Open-ended Knowledge Tracing for Computer Science Education
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DOI：
10.18653/v1/2022.emnlp-main.254
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Naiming Liu;Zichao Wang
中科院分区：
文献类型：
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作者：
Naiming Liu;Zichao Wang
在教育应用中，知识追踪是指估计学生过去对问题的回答和预测未来表现的估计时间变化的概念/技能掌握水平的问题。大多数现有知识追踪方法的一个关键限制是，他们将学生对待学生对学生的回应二进制评估的问题，即是正确的还是不正确的。掌握，尤其是对于开放式问题。相比之下，确切的学生回答可以提供更多信息。在本文中，我们通过研究预测学生确切开放的新任务来进行第一个探索int Int Int Int Int Int开放式知识跟踪（OKT） - 对问题的回答。我们的工作基于计算机科学教育的领域，我们为OKT问题开发了一种最初的解决方案，这是一种学生知识引导的代码生成方法使用语言模型与学生知识追踪方法结合了程序综合方法。
In educational 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 is straightforward, but it ignores important information regarding mastery, especially for open-ended questions.In contrast, exact student responses can provide much more information.In this paper, we conduct the first exploration int 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 and demonstrate the promise of OKT.