THE USE OF MACHINE LEARNING IN HIGHER EDUCATION

Abstract

The ability to have machines draw accurate conclusions has had powerful implications in today’s education setting. Despite the powerful applications of machine learning, the higher education industry has been slow to adapt its usage. The acceptance and proper use of machine learning techniques are still in their infancy stages within the higher education industry. The objective of this paper is to examine the proper usage of machine learning in higher education from both students’ perspective and institutional perspective. The Bayesian Modeling Averaging (BMA) method was used to study the usage of machine learning at a student level whereas, the Monte Carlo Simulations (MCS) method was used to study the usage of machine learning at an institutional level. From the students’ perspective, the results allowed a comparison between actual and projected retention. From the institutional perspective, the results allowed establishing a competitive strategy to improve the university’s rank among other research universities possible.