Analysis of Existing ML Techniques for Students Success Prediction

Providing good academic environment, infrastructure and various learning facilities is not sufficient for Higher Educational Institutions in this competitive era. HIEs have to adopt more trending technologies that can help students to achieve best professional growth. Students’ performance prediction in advance using Supervised and Unsupervised Machine Learning techniques is very much trending for learning contexts in HIEs as it helps administrators to design strategies for improving final results. This is possible as high-volume data about student’s personal, academic performance at school level and at the admission time is available in school, college and universities. Historical data can be utilized to analyze various learning capabilities of different students in different environment and that analysis can be used to predict performance of a newly admitted student. This research mentions an analysis of existing ML techniques in higher education for prediction of students’ success based on previous work done in the related area. We have studied a total of 40 relevant papers in which ML techniques are being proposed or implemented for students’ success prediction. A systematic analysis was done to synthesize and report the main results.