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.

Using version control tools is an indispensable skill for engineers in the software industry. This study introduces a gamification approach together with a serious game called the Git Education Game (GEG) to teach Git concepts and usage, intending to improve students’ motivation and learning performance compared to traditional lectures. An experiment was designed with two classes of the same course to compare the effect of GEG. A post-test was designed to verify whether the game could help students achieve better learning outcomes and higher motivation. The results show that our approach had a positive effect on students’ motivation, so the experimental group had a higher pass rate than the control group for most items in the post-test. Based on this study’s results, we emphasize the impact of interactive learning environments in software engineering education.

Project-based learning is among the most common learning approaches aimed at conveying professional standards and best practices to students in programming courses. However, team projects commonly impose problems related to responsibility sharing, such as low effort or inequality in contributions. This paper presents a collaborative programming assessment system featuring a code quality assessment function with specific metrics to measure individual contributions. Student engagement data is used to detect nonconformities in collaboration using a learning analytical approach. Latent profile analysis was used to detect four theoretical team profiles differentiated by team effort (2 levels) and within-team collaboration (2 levels). We demonstrated the efficacy of assessing code to evaluate team dynamics and student behaviour, wherein efforts to resolve coding style failures can be used as a proxy by which to estimate the taskwork awareness of team members. Submission data from 146 students in 41 web-programming projects revealed four behavioural patterns that could potentially hinder the effective functioning of programming teams: free-riding, social loafing, the bystander effect, and lone wolves. We also demonstrated the applicability of automated programming assessment systems to the monitoring of learning progress, thereby facilitating timely interventions to correct difficulties at the team level.

This chapter explores the integration of learning analytics (LA) into ProgEdu, an automated programming assessment system (APAS), with a focus on improving students' programming education outcomes by considering code quality. Through the analysis of code quality improvements, metric calculation, visualization analysis, latent profile analysis, clustering and prediction can be performed. In individual assignments, LA enables the monitoring of student engagement, identification of student profiles, and early detection of at-risk students. For team projects, LA facilitates the assessment of individual contributions, tracking of team members' participation, identification of discrepancies in teamwork-sharing, detection of student profiles based on their contributions, and recognition of free-riders. The approach promotes the integration of LA into APAS to facilitate instructors in understanding students' learning behaviors and enable them to diagnose issues and provide targeted interventions, by which programming education in university settings is enhanced.