Comparison of Student Learning Outcomes Among SQL Problem-Solving Patterns

Structured Query Language (SQL) plays a pivotal role in the effective management of relational databases and is a key skill across domains that engage with database systems, including research, development, and business management. However, mastering SQL can be challenging. To comprehend the approaches employed by students when solving SQL problems and address the challenges they faced during the learning process, our study analyzes submissions from the Database Systems course at the University of Illinois Urbana-Champaign during the Fall 2022 semester. We extend prior research involving line chart visualizations that facilitate instructors in identifying struggling students and understanding their submission behaviors. Yet, we acknowledge the limitations of this approach in providing timely feedback and actionable insights due to the sheer volume of visualizations. To address this, we developed an innovative technique using global sequence alignment scores and regular expression algorithms to compress student submission sequences. Our approach reveals submission patterns and pattern elements, leading to recommendations for instructors to enhance database education. By integrating student performance data, such as the number of submission attempts on a particular SQL problem and whether the student arrived at a correct final solution query, we aim to empirically support these recommendations, thereby enabling instructors to more accurately differentiate between struggling and excelling students.