Improving QS Rank Based on The Classification of Authors Research Collaboration Using Machine Learning Techniques

The importance of universities' global ranking lies in providing a trusty resource, which helps students in choosing the right place to complete their academic future. The global ranking systems are based on several metrics that focus on the study environment, the quality of the provided services, the scientific publications, and the extent of the authors' strength. Quacquarelli Symonds (QS) is the most popular global ranking system, it has Citations Per Faculty (CPF) evaluation metric, which constitutes 20% of the total ranking score. In this research, we aim to find the effect of the research collaboration on increasing the CPF score, in which we apply descriptive analytics on a dataset for Jordan University of Science and Technology (JUST) authors, that is scrapped from the official websites of Google Scholar and Researchgate. Then, we find the authors who have a moderate collaboration through building a classification model using machine learning techniques. The results proved that the research collaboration has a significant impact in increasing authors publications that positively correlated with their total citations, which in turn gives a great opportunity to increase the CPF score. Also, the Support Vector Machine classifier has obtained a 95.27% level of accuracy, which considers as an efficient method in classifying the authors research collaboration into strong and moderate collaboration. Finally, the proposed method can be used to improve the QS ranking and obtain a high scientific standing level for academic institutes.

I. Introduction

Nowadays, the QS system is the most famous world university ranking, it is the base measure that presents the deserved rank of any university compared to the others. The rank is gathered using six different metrics that measure the performance of the universities from specific aspects, where each metric spots the light on different critical properties. When it comes to the authors, the QS system uses the publications'' outputs to measure the institutional research quality using the citations per faculty (CPF), which is a metric that evaluated in the specific way that takes into consideration the size of the institution and has 20% of the ranking value [1]. From these viewpoints, the main idea here is how to find the reasons that increase the publication outputs of the current authors. Once these reasons are detected, it can be used to improve the research abilities of the authors and take proper actions to increase their scientific citations.