Sensitivity of Automated SQL Grading in Computer Science Courses

Previous research has primarily relied on fixed procedures when implementing partial grading systems. As a result, the sensitivity of such systems in terms of error analysis becomes inflexible as well. In this paper, we employ a software correctness model that allows for a dynamic and flexible approach for adjusting the sensitivity of a grading system based on the user’s needs and goals. We show how partial grading can be used to award fair grades and also categorize students into groups based on their strengths and weaknesses observed in their answers. Furthermore, we show how the sensitivity of a grading system can be varied to allow such grouping. To illustrate this, we analysed more than 2000 answers for 6 SQL programming assignments. An implication of this study is that instructors can carry out more effective partial grading of SQL queries as well as adjust learning material based on the needs of a particular group of students. They can address the observed limitations, thereby bridging the gap between high-performing students and those that require additional attention.