Quantitative Analysis of Learning Data in a Programming Course

Yu Bai, Liqian Chen, Gang Yin, Xinjun Mao, Ye Deng, Tao Wang, Yao Lu, Huaimin Wang

Abstract: Online learning platform, which has taken higher education by storm, provides an opportunity to track students’ learning behaviors. The vast majority of educational data mining research has been carried out based on the online learning platform in Europe and America but few of them use the data from programming courses with large scale. In this paper, we track students’ code submissions for assignments in a programming course and collect totally 17,854 submissions with the help of Trustie, a famous online education platform in China. We perform a preliminary exploratory inspect for code quality by SonarQube from the code submissions. The analysis results reveal several interesting observations over the programming courses. For example, results show that logical training is more important than grammar training. Moreover, the analysis itself also provides useful feedback of students’ learning effect to instructors for them to improve their teaching in time.