Correlating Students' Class Performance Based on GitHub Metrics: A Statistical Study

Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 Pub Date : 2023-06-29 DOI:10.1145/3587102.3588799

Jiali Cui, Runqiu Zhang, Ruochi Li, Yang Song, Fangtong Zhou, E. Gehringer

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Abstract

What skills does a student need to succeed in a programming class? Ostensibly, previous programming experience may affect a student's performance. Most past studies on this topic use self-reporting questionnaires to query students about their programming experience. This paper presents a novel, unified, and replicable way to measure previous programming experience using students' pre-class GitHub contributions. To our knowledge, we are the first to use GitHub contributions in this way. We conducted a comprehensive statistical study of students in an object-oriented design and development class from 2017 to 2022 (n = 751) to explore the relationships between GitHub contributions (commits, comments, pull requests, etc.) and students' performance on exams, projects, designs, etc. in the class. Several kinds of contributions were shown to have statistically significant correlations with performance in the class. A set of two-samplet -tests demonstrate statistical significance of the difference between the means of some contributions from the high-performing and low-performing groups.

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基于GitHub指标的学生课堂表现相关性的统计研究

学生需要什么技能才能在编程课上取得成功?表面上看，以前的编程经验可能会影响学生的表现。过去关于这一主题的大多数研究使用自我报告问卷来询问学生的编程经验。本文提出了一种新颖的，统一的，可复制的方法来衡量以前的编程经验，使用学生的课前GitHub贡献。据我们所知，我们是第一个以这种方式使用GitHub贡献的。我们对2017年至2022年的一门面向对象设计与开发课程的学生进行了全面的统计研究(n = 751)，探讨GitHub贡献(提交、评论、pull requests等)与学生在课堂上的考试、项目、设计等成绩之间的关系。有几种类型的贡献被证明在统计上与课堂表现有显著的相关性。一组双样本检验证明了高绩效组和低绩效组的一些贡献的均值之间的差异具有统计学意义。

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Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1

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