论文标题
Code-DKT：用于编程任务的基于代码的知识跟踪模型
Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks
论文作者
论文摘要
知识追踪（KT）模型是一种流行的方法，可以通过先前尝试预测学生在实践问题上的未来表现。尽管在KT中进行了许多创新，但是大多数模型在内，包括最先进的Deep KT（DKT）主要利用每个学生的响应是正确或不正确的，而忽略了其内容。在这项工作中，我们提出了基于代码的深知识跟踪（Code-DKT），该模型使用注意机制自动提取并选择特定领域的代码功能来扩展DKT。我们比较了Code-DKT对贝叶斯和深度知识跟踪（BKT和DKT）的有效性，该数据集的50名学生试图解决5个介绍性编程任务。我们的结果表明，Code-DKT在5个任务中始终优于DKT的AUC 3.07-4.00％AUC，与DKT相对于其他最先进的域中KT模型的可比改进。最后，我们通过一组案例研究来分析特定问题的性能，以证明代码何时以及如何改善Code-DKT的预测。
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In this work, we propose Code-based Deep Knowledge Tracing (Code-DKT), a model that uses an attention mechanism to automatically extract and select domain-specific code features to extend DKT. We compared the effectiveness of Code-DKT against Bayesian and Deep Knowledge Tracing (BKT and DKT) on a dataset from a class of 50 students attempting to solve 5 introductory programming assignments. Our results show that Code-DKT consistently outperforms DKT by 3.07-4.00% AUC across the 5 assignments, a comparable improvement to other state-of-the-art domain-general KT models over DKT. Finally, we analyze problem-specific performance through a set of case studies for one assignment to demonstrate when and how code features improve Code-DKT's predictions.
