Predicting Collaborative Performance at Assessment Level using Machine Learning

Abstract

Most of the machine learning-based educational data mining (EDM) studies in university education merely focus on the predication of individual students' performance at institutional/program and course levels. To predict collaborative performance, this study demonstrates the application of a rough set theory-based machine learning technique at the assessment level of a university course. It unveils if-then rules comprising key factors affecting assessment scores and categorizes them into performance classes of ‘Low’, ‘Medium’ and ‘High’. The results are applicable in chalking out strategies related to teaching and student advising to improve academic performance.