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.



Machine learning algorithms

Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL