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Toward Understanding Students’ Learning Performance in an Object-Oriented Programming Course: The Perspective of Program Quality

机译:了解学生在面向对象的编程过程中的学习表现:程序质量的视角

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This pilot study examines how students& x2019; performance has evolved in an Object-oriented (OO) programming course and contributes to the learning analytic framework for similar programming courses in university curriculum. First, we briefly introduce the research background, a novel OO teaching practice with consecutive and iterative assignments consisting of programming and testing assignments. We propose a planned quantitative method for assessing students& x2019; gains in terms of programming performance and testing performance. Based on real data collected from students who engaged in our course, we use trend analysis to observe how students& x2019; performance has improved over the whole semester. By using correlation analysis, we obtain some interesting findings on how students& x2019; programming performance correlates with testing performance, which provides persuasive empirical evidence in integrating software testing practices into an Object-oriented programming curriculum. Then, we conduct an empirical study on how students& x2019; design competencies are represented by their program code quality changes over consecutive assignments by analyzing their submitted source code in the course system and the GitLab repository. Three different kinds of profiles are found in the students& x2019; program quality in the OO design level. The group analysis results reveal several significant differences in their programming performance and testing performance. Moreover, we conduct systematical explanations on how students& x2019; programming skill improvement can be attributed to their object-oriented design competency. By performing principal component analysis on software statistical data, a predictive OO metrics suite for both students& x2019; programming performance and their testing performance is proposed. The results show that these quality factors can serve as useful predictors of students& x2019; learning performance and can provide effective feedback to the instructors in the teaching practices.
机译:这项试验研究审查了学生和X2019的方式;表现已在面向对象(OO)编程课程中发展,并有助于大学课程中类似规划课程的学习分析框架。首先,我们简要介绍了一种新的OO教学实践,其中包括由编程和测试分配组成的连续和迭代作业。我们提出了一项规划的评估学生和X2019的定量方法;在编程性能和测试性能方面获得增长。根据从事我们课程的学生收集的真实数据,我们使用趋势分析观察学生和X2019的学生;性能在整个学期内得到了改善。通过使用相关性分析,我们获得了学生和X2019的一些有趣的结果;编程性能与测试性能相关联,这提供了有说服力的经验证据,将软件测试实践集成到面向对象的编程课程中。然后,我们对学生和X2019的方式进行实证研究;设计能力由他们的程序代码质量通过在课程系统和Gitlab存储库中分析他们提交的源代码来表示连续分配。在学生和X2019中发现了三种不同的概况; OO设计水平的程序质量。组分析结果显示其编程性能和测试性能的几个显着差异。此外,我们对学生和X2019的系统进行了系统解释;编程技能改进可归因于其面向对象的设计能力。通过对软件统计数据进行主成分分析,对学生兼X2019的预测OO度量标准套件;提出了编程性能及其测试性能。结果表明,这些质量因素可以作为学生的有用预测因子和X2019;学习绩效并可以为教学实践中的教师提供有效的反馈。

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