Partial SQL Query Assessment

Abstract

Automated grading systems in education have been around for sixty years. They have found applications in areas such as online learning systems where virtually an unlimited number of users can test their knowledge that would not be possible to evaluate manually. Implementations within Massive Open Online Courses are a good practice in which users can do the self-testing, and get instant feedback, making the learning process more efficient. Within universities, automated grading systems allow teachers to evaluate solutions and provide feedback for thousands of submissions in a short time. This paper presents an overview of methods used in automatic SQL query evaluation systems, from early implementations when the goal was only to evaluate solutions binary, to today when they enable functionalities like partial and configurable evaluation, rich and customized feedback, learning analytics, learning pattern detection, code quality check, plagiarism detection. These methods are not exclusive, and combining different approaches makes an automated grading system more comprehensive and applicable. Automatic assessment system of SQL queries developed at Algebra University College will be presented as an example of a system which uses dynamic and static analysis, awards partial points, and gives feedback to students based on the wrong parts of their solutions.