Automated Grading of SQL Queries

Abstract

Grading student SQL queries manually is a tedious and error-prone process. The XData system, developed at IIT Bombay, can be used to test if a student query is correct or not. However, in case a student query is found to be incorrect, there is currently no way to automatically assign partial marks. Manually awarding partial marks is not scalable for classes with a large number of students, especially MOOCs, and is also prone to human errors. In this paper, we discuss techniques to award partial marks to student SQL queries, in case they are incorrect, based on a weighted equivalence edit distance metric. Our goal is to find a minimal sequence of edits on the student query such that it can be transformed to a query that is equivalent to a correct query. Our system can also be used in a learning mode where query edits can be suggested as feedback to students to guide them towards a correct query. Our automated partial marking system has been successfully used in courses at IIT Bombay and IIT Dharwad.