Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.