Utilizing Learning Analytics in an Automated Programming Assessment System to Enhance Software Practice Education

Abstract

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.