Enhancing Feedback Generation for Autograded SQL Statements to Improve Student Learning
Abstract
Several tools to support autograding of student provided SQL statements have already been introduced. The full potential of such tools can only be leveraged, if they extend beyond grading efficiency by also providing tutoring capabilities to the students. With that, tools become really useful by offering self-paced and individually timed learning experiences. In this paper we present an extension for an SQL autograder which improves the hints generated for students in cases where their solution is not entirely correct. Our approach is to compare the student's solution with the model solution structurally to identify differences between the syntax trees describing the statements. This complements comparing the student's query with a model solution based on query results. In addition to improving the quality of hints generated for the students, this concept can also be used easily for data manipulation language (DML) or data definition language (DDL) statements, thus extending the applicability of the autograder. Along with details about the concept we present some example hints generated to illustrate the usefulness of the approach. We also report anecdotally on experiences with the system in two different level database courses. Results from different instances of one of them show improvements of student learning as well as student involvement by using the newly generated hints.
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- Enhancing Feedback Generation for Autograded SQL Statements to Improve Student Learning
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July 2024
776 pages
ISBN:9798400706004
DOI:10.1145/3649217
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