Bridging the gap: Understanding SQL learning challenges through quantitative analyses and qualitative insights

As the demand for SQL experts continues to rise, teaching SQL effectively has become increasingly important. Previous studies on SQL education have identified learning challenges, analyzed student errors, and developed learning tools. Building on this foundation, my research provided additional directions to enhance educators’ understanding of SQL learning challenges and explored tools to teach more effectively. Leveraging large-scale data collected from a public institution’s database course, I conducted a series of quantitative studies with students’ data. I developed use cases for pattern-mining techniques to track students’ submission attempts for their SQL assignments. With advanced pattern-mining techniques, such as Levenshtein Edit Distance, hierarchical clustering, and sequence alignment, I examined and captured students’ problem-solving behaviors under various conditions. Furthermore, previous studies on general education suggested several factors that can affect students’ learning ability, including study time, course modality, collaborative environments, etc. Thus, I conducted several studies to explore factors influencing students’ ability to learn SQL, including the sequence in which query languages are introduced, study time, assignment types, error feedback, and collaborative environments. Lastly, since students often struggle to solve SQL problems due to a lack of error feedback, I also investigated the use of generative AI tools to provide more effective and personalized feedback. To complement these quantitative findings, I designed and conducted a qualitative think-aloud study to gain deeper insights into students’ SQL challenges. Based on the combined findings, I provided actionable recommendations to improve teaching strategies and curriculum design. Instructors should adopt pattern mining tools to identify students needing extra support, emphasize non-timed SQL problems, provide syntax drills, integrate generative AI for error feedback, and explore an online flipped-classroom model with in-person office hours. Additionally, studying the inclusion of Parsons problems could further scaffold student learning in SQL.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Yang, Sophia S.

Contributors dc:contributor

• Alawini, Abdussalam
• Herman, Geoffrey L.
• Zilles, Craig
• Zhai, Chengxiang
• Davidson, Susan B.

• SQL
• Structured Query Language
• database
• education
• computing education
• pattern mining
• sequence alignment
• quantitative
• qualitative

Statement dc:rights

• Copyright 2025 Sophia S. Yang

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129367

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129367