An Algorithm for Generating Explainable Corrections to Student Code

An Algorithm for Generating Explainable Corrections to Student Code
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一种对学生代码生成可解释的更正的算法

DOI:
10.1145/3564721.3564731
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发表时间:
2022
期刊:
Proceedings of the 22nd Koli Calling International Conference on Computing Education Research
影响因子:
--
通讯作者:
Kelleher, Caitlin
Kelleher, Caitlin
中科院分区:
--
文献类型:
--
作者:
Malysheva, Yana;Kelleher, Caitlin

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学习计算机科学入门课程的学生在解决编程问题时常常需要个性化的帮助。但提供此类帮助可能非常耗时且需要大量思考,因此随着计算机科学课程规模的扩大而难以扩展。自动生成的带有解释的修复程序有可能集成到各种机制中,为陷入编程问题的学生提供帮助。在本文中,我们提出了一种数据驱动的算法,用于为学生代码生成可解释的修复。我们通过将算法不同阶段的输出与具有相似目标的最先进系统进行比较来评估该算法的 Python 实现。我们的算法优于可以分析和修复初学者编写的 Python 代码的现有系统。此外,它生成的修复非常符合人类专家为现有代码校正质量基准编写的校正。
Students in introductory computer science courses often need individualized help when they get stuck solving programming problems. But providing such help can be time-consuming and thought-intensive, and therefore difficult to scale as Computer Science classes grow larger in size. Automatically generated fixes with explanations have the potential to integrate into a variety of mechanisms for providing help to students who are stuck on a programming problem. In this paper, we present a data-driven algorithm for generating explainable fixes to student code. We evaluate a Python implementation of the algorithm by comparing its output at different stages of the algorithm to state-of-the-art systems with similar goals. Our algorithm outperforms existing systems that can analyze and fix beginner-written Python code. Further, fixes it generates conform very well to corrections written by human experts for an existing benchmark of code correction quality.
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