Recommender System for STEM Enrolment in Universities Using Machine Learning Algorithms: Case of Kenyan Universities

Abstract

Technology, Engineering, and Mathematics (STEM) enrolment has gained a lot of research interest. The increase in demand for STEM-based skill sets has contributed to the need for systems that could potentially increase enrolments in the field. The purpose of this study was to investigate recommender systems for STEM enrolment in universities using machine learning algorithms. Students face challenges while selecting STEM courses that match their attributes. This article aims to provide a recommender system for STEM enrolment using machine learning algorithms. The article investigates three machine learning algorithms which include Support Vector Machine (SVM), Artificial Neural Network (ANN), and Naïve Bayes. Accuracy and validation techniques were applied to test the algorithms. The results demonstrated that our work performed better than that of the published research, with the ANN outperforming other classification methods. The results position ANN as an important algorithm in building a recommender model for STEM higher education enrolment. The study also identifies high school grades and Interest in STEM courses as important features in predicting STEM course enrolment in higher education. The study will guide policy on the courses to lay more emphasis on, as well as for the funding authorities to prioritize funding allocation for STEM-based courses.