Predicting Collaborative Performance at Assessment Level using Machine Learning

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.