{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129367"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129367","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bridging the gap: Understanding SQL learning challenges through quantitative analyses and qualitative insights","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Yang, Sophia S."],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Alawini, Abdussalam","Herman, Geoffrey L.","Zilles, Craig","Zhai, Chengxiang","Davidson, Susan B."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-07","date_published":"2025-02-07","updated_at":"2026-07-22T22:25:05Z","subjects":["SQL","Structured Query Language","database","education","computing education","pattern mining","sequence alignment","quantitative","qualitative"],"languages":["en","eng"],"rights":["Copyright 2025 Sophia S. 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Yang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129367"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Sophia Yang, accepted the attached license on 2025-02-04 at 19:51.","The student, Sophia Yang, submitted this Dissertation for approval on 2025-02-04 at 20:13.","This Dissertation was approved for publication on 2025-02-07 at 11:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21633 on 2025-10-19 at 18:17:21","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Bridging the gap: Understanding SQL learning challenges through quantitative analyses and qualitative insights"]}]}],"canonical_facts":{"dc:contributor":["Alawini, Abdussalam","Herman, Geoffrey L.","Zilles, Craig","Zhai, Chengxiang","Davidson, Susan B."],"dc:creator":["Yang, Sophia S."],"dc:date":["2025-02-07","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Sophia Yang, accepted the attached license on 2025-02-04 at 19:51.","The student, Sophia Yang, submitted this Dissertation for approval on 2025-02-04 at 20:13.","This Dissertation was approved for publication on 2025-02-07 at 11:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21633 on 2025-10-19 at 18:17:21","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129367"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Sophia S. Yang"],"dc:subject":["SQL","Structured Query Language","database","education","computing education","pattern mining","sequence alignment","quantitative","qualitative"],"dc:title":["Bridging the gap: Understanding SQL learning challenges through quantitative analyses and qualitative insights"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}