Abstract
Automated programming assessment systems are useful tools to track the learning progress of students automatically and thereby reduce the workload of educators. They can also be used to gain insights into how students learn, making it easier to formulate strategies aimed at enhancing learning performance. Rather than functional code which is always inspected, code quality remains an essential aspect to which not many educators consider when designing an automated programming assessment system. In this study, we applied data mining techniques to analyze the results of an automated assessment system to reveal unexpressed patterns in code quality improvement that are predictive of final achievements in the course. Cluster analysis is first utilized to categorize students according to their learning behavior and outcomes. Cluster profile analysis is then leveraged to highlight actionable factors that could affect their final grades. Finally, the same factors are employed to construct a classification model by which to make early predictions of the students' final results. Our empirical results demonstrate the efficacy of the proposed scheme in providing valuable insights into the learning behaviors of students in novice programming courses, especially in code quality assurance, which could be used to enhance programming performance at the university level.
Highlights
Assessment and feedback are essential tasks of educational activities that enable students and lecturers to keep track of the performance of the learning process
We employed two techniques based on graphical Exploration data analysis (EDA), to elucidate the behavioral patterns exhibited by students in the submission of homework and how the patterns affect their final grades
The purpose of this paper is to find out the relationship between student behaviors in code quality improvement and learning achievement
Summary
Assessment and feedback are essential tasks of educational activities that enable students and lecturers to keep track of the performance of the learning process. Students can understand their strengths and weaknesses in certain learning objectives, they can improve them based on provided feedback. In practical courses such as computer programming, these tasks are even more important because students rarely achieve acceptable solutions in some first tries. Giving frequent assessments and detailed feedbacks significantly increases the workload of lecturers. Many educators have developed Automated Programming Assessment Systems (APASs) to enhance programming assessments in their courses automatically.
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