Amelioration of Teaching Strategies by Exploring Code Quality and Submission Behavior

Online learning platforms provide an opportunity to better understand students’ weaknesses by tracking both their learning behavior and knowledge. This information can then be used to assist teachers in making instructional decisions and to further guide those who are at risk of failure. In this paper, we tracked student learning data from a C++ programming course over a whole semester of their freshman year via the Trustie platform. A total of 17,854 code submissions were collected. We then used CppCheck, SonarQube and Trustie to capture the quality characteristics and submission characteristics of the code, including lineOfCode, cyclomaticComplexity, codeSmell, syntacticError, averageScore, submission, and logicError, and analyzed the impact of code quality on the assignment work results. Several factors were discovered that we believe can help teachers to develop more effective teaching strategies.