Description
Title: Rankings and Objectives of Universities: A Cluster Analysis
Abstract: Rankings of universities are now used to evaluate every aspect of higher education. This research aims to explore university rankings in-depth due to the influence of rankings on evaluating universities’ performance. Rankings of universities are taken into consideration when evaluating university performance. Previous research presented various types of goals, such as output and support goals, and suggested that these two types of goals be aligned. Despite the fact that universities have varying objectives, rankings of universities use the same standards to compare all of them. The most popular university rankings in the literature, the QS world ranking dataset, were then used in this study. The universities were then clustered using unsupervised machine learning. The outcomes clustered universities into four groups. The university is placed in the appropriate cluster thanks to this study. Managers of universities can use this study to clarify their institutions’ objectives. The study suggests that universities match their output goals and support goals. The study advises universities to create international objectives and plans, and to support academic research by providing funding for scholars. The originality of this study lies in its use of management analytics in education to link university rankings and goals.
Keywords: Rankings of universities are now used to evaluate every aspect of higher education. This research aims to explore university rankings in-depth due to the influence of rankings on evaluating universities’ performance. Rankings of universities are taken into consideration when evaluating university performance. Previous research presented various types of goals, such as output and support goals, and suggested that these two types of goals be aligned. Despite the fact that universities have varying objectives, rankings of universities use the same standards to compare all of them. The most popular university rankings in the literature, the QS world ranking dataset, were then used in this study. The universities were then clustered using unsupervised machine learning. The outcomes clustered universities into four groups. The university is placed in the appropriate cluster thanks to this study. Managers of universities can use this study to clarify their institutions’ objectives. The study suggests that universities match their output goals and support goals. The study advises universities to create international objectives and plans, and to support academic research by providing funding for scholars. The originality of this study lies in its use of management analytics in education to link university rankings and goals.
Paper Quality: SCOPUS / Web of Science Level Research Paper
Subject: Economics
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