Exploring Debugging Challenges and Strategies Using Structural Topic Model: A Comparative Analysis of High and Low-Performing Students

Debugging is essential for identifying and rectifying errors in programming, yet time constraints and students’ trivialization of errors often hinder progress. This study examines differences in debugging challenges and strategies among students with varying computational thinking (CT) competencies using weekly coding journals from an online undergraduate CT course. Participants used Scratch, a block-based programming language, and their journals from five assignments were analyzed using Term Frequency-Inverse Document Frequency and Structural Topic Modeling. High-performing students engaged with diverse topics and specific blocks tied to their weekly projects while low-performing students faced repetitive and broad challenges, such as understanding motion blocks and broadcast concepts. These patterns reveal that low-performing students struggle particularly during the ‘diagnose the fault’ phase of debugging, often hindering their progress in the final stage. Such challenges highlight the necessity for targeted educational interventions to improve the debugging proficiency and overall CT skills. The study underscores the importance of further research into students’ logical thinking processes during code review and debugging, suggesting the use of think-aloud protocols and detailed tracking of debugging practices for deeper insights. This research contributes to the field by showing that differentiated instruction and strategic support can enhance debugging skills across different student performance levels.