从源代码快照随机校正检测

Proceedings of the 2019 8th International Conference on Software and Computer Applications Pub Date : 2019-02-19 DOI:10.1145/3316615.3316621

Yuki Ohno, H. Uwano, S. Uchida

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引用次数: 0

摘要

对学生情况进行分类有助于教师提高教学效果。在本文中，作者提出了两个指标来分类学生的“随机更正”。随机纠错是在不理解练习内容的情况下对源代码进行纠错的行为。以在线裁判系统为研究对象，分析了基于记录快照的随机校正特性。实验结果表明，成绩达不到满分的学生，两项指标的价值都较高;1)源代码行之间的不平衡修正程度，2)提交的修订数量。

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Detection of Random Correction from Source Code Snapshots

Classifying student's situation helps teachers to improve educational effect. In this paper, authors propose two metrics to classify the student's "random correction." Random Correction is an action that source code correction without understanding the exercise contents. We select a programming course with Online Judge System as a target, then analyze the characteristics of random correction from recorded snapshots. The result of the experiment showed that students who cannot reach perfect score have high value of both metrics; 1) a degree of imbalance corrections between source code lines, 2) the number of submitted revisions.

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Proceedings of the 2019 8th International Conference on Software and Computer Applications

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