Using string similarity metrics for automated grading of SQL statements

Manual grading of structured query language (SQL) statements after an exam can be tedious and time consuming for the teaching assistant. Additionally, it can also be subjective to her current state of mind and, thus, prone to errors. In this paper we propose an automated method for grading individual SQL statements. The method uses several common and simple string similarity metrics for comparing the student devised statements against the reference statements. These are then used, along with the manually assigned grades, for building the predictive logistic regression model. The proposed method was evaluated on a dataset consisting of 314 pairs of student-reference statements, along with the discretized average grade assigned by three independent evaluators. The model achieved the expected classification accuracy of 78% on a binary class, thus exhibiting its potential for real-life application. The model can be used as is with the suggested calculated features and reported learnt parameters, or adapted to other examiners' evaluation criteria, presuming their willingness to build manually graded datasets of their own.