Edit Based Grading of SQL Queries
Bikash Chandra, Ananyo Banerjee, Udbhas Hazra, Mathew Joseph, and S. Sudarshan

TL;DR
This paper introduces an automated method for grading SQL queries by finding minimal edits to correct student submissions, enabling partial credit and providing targeted feedback, which improves scalability and accuracy in large classes.
Contribution
It proposes a greedy heuristic approach for minimal query edits to facilitate partial marking and feedback, addressing limitations of existing formal equivalence checks.
Findings
Effective partial marking in large classes demonstrated
Greedy heuristic balances runtime and accuracy
System successfully used in IIT courses
Abstract
Grading student SQL queries manually is a tedious and error-prone process. Earlier work on testing correctness of student SQL queries, such as the XData system, can be used to test correctness of a student query. However, in case a student query is found to be incorrect there is currently no way to automatically assign partial marks. Partial marking is important so that small errors are penalized less than large errors. Manually awarding partial marks is not scalable for classes with large number of students, especially MOOCs, and is also prone to human errors. In this paper, we discuss techniques to find a minimum cost set of edits to a student query that would make it correct, which can help assign partial marks, and to help students understand exactly where they went wrong. Given the limitations of current formal methods for checking equivalence, our approach is based on finding…
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Taxonomy
TopicsScientific Computing and Data Management · Advanced Database Systems and Queries · Data Quality and Management
