A Course Exam Scheduling Approach based on Data Mining

Abstract

We propose an exam scheduling approach to deal with problems that may appear in some oral exams, such as the cases when student turnout is considerably above or below expectation. As opposed to similar approaches, we focus on predicting the number of students applying for an exam by performing data mining on student records. Our predictive model considers previous student scores, attendance records, and past exam attempts. We evaluate the prediction segment of this approach on a real-world data set containing university records for a pair of database-related courses.