A machine learning approach feature to forecast the future performance of the universities in Canada

• Published in: Machine learning with applications 2024-06, Vol.16, p.100548, Article 100548
• Main Authors: Wardley, Leslie J., Rajabi, Enayat, Amin, Saman Hassanzadeh, Ramesh, Monisha
• Format: Article
• Language: English
• Subjects: Gradient Boosting; Machine learning; Random forest; University ranking; Voting Classifier
• ISSN: 2666-8270; 2666-8270
• DOI: 10.1016/j.mlwa.2024.100548

University ranking is a technique of measuring the performance of Higher Education Institutions (HEIs) by evaluating them on various criteria like student satisfaction, expenditure, research and teaching quality, citation count, grants, and enrolment. Ranking has been determined as a vital factor that helps students decide which institution to attend. Hence, universities seek to increase their overall rank and use these measures of success in their marketing communications and prominently place their ranked status on their institution's websites. Despite decades of research on ranking methods, a limited number of studies have leveraged predictive analytics and machine learning to rank universities. In this article, we collected 49 Canadian universities’ data for 2017–2021 and divided them based on Maclean's categories into Primarily Undergraduate, Comprehensive, and Medical/Doctoral Universities. After identifying the input and output components, we leveraged various feature engineering and machine learning techniques to predict the universities’ ranks. We used Pearson Correlation, Feature Importance, and Chi-Square as the feature engineering methods, and the results show that “student to faculty ratio,” “total number of citations”, and “total number of Grants” are the most important factors in ranking Canadian universities. Also, the Random Forest machine learning model for the “primarily undergraduate category,” the Voting classifier model for the “comprehensive category” and the Gradient Boosting model for the “medical/doctoral category” performed the best. The selected machine learning models were evaluated based on accuracy, precision, F1 score, and recall.