Building a Better SQL Automarker for Database Courses

Abstract

This work introduces and demonstrates the viability of a novel SQL automarking tool (“SQAM”) that: (1) provides a fair grade to the student, one which matches the student’s effort and understanding of the course material, and (2) to provide personalized feedback, allowing the student to remain engaged in the material and learn from their mistakes while still being in that headspace. Additionally, we strive to ensure that our tool maintains the same standards (grade and feedback) that a highly qualified member of teaching staff would produce, so we compare and contrast our automarker’s results to that of teaching assistants over several historic offerings of the same database course at a large research intensive public institution, while reducing the grading time, thus enabling the teaching staff to channel more time into instruction. Furthermore, we describe SQAM’s design and our model which applies the aggregate result of four different string similarity metrics to compute solution similarity in conjunction with our discretization process to fairly evaluate a student’s submission. Our results show that SQAM produces very similar grades to those which were historically given by teaching assistants.