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Enhancing SQL Learning Through Generative AI and Student Error Analysis
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DOI：10.1007/978-3-032-05727-3_12.png)
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Abstract
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Despite the widespread use of SQL in both academic and professional contexts, students often struggle with writing correct queries due to a range of syntactic and semantic misconceptions. In this work, we propose a framework supporting SQL learning centered around errors as central to the learning process. The framework integrates generative AI and automated error categorization to foster metacognitive engagement and support personalization.
Keywords:
SQL learning
generative AI
error analysis
metacognitive engagement
personalized learning
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Cited Papers
Evaluating Student Performance and Interactions in Generative AI-Integrated SQL Practical Tests
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ShareSaveStudents' Semantic Mistakes in Writing Seven Different Types of SQL Queries.png)
Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education
Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science EducationIF0
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