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WURS

WURS simulator helps you to formulate hypothetical scenarios featuring changes to your institution’s core data and then shows you how these changes would have affected your performance in the ranking systems.

Although the number of university rankings is growing every year, the most popular and the influential ones are possibly The Academic Ranking of World Universities (ARWU), Times Higher Education University Ranking (THE) and Quacquarelli Symonds World University Ranking (QS). Ranking lists of THE, QS and ARWU are based on the several performance indicators that measure an institution’s performance across some areas such as teaching, research or international outlook, etc. All of these three ranking systems present the scaled scores of the key indicators, an overall score aggregating the scaled scores of the indicators with their assigned weights, and the respective rank. WURS simulates all the three of them.

Despite the several criticisms, many people think that a university’s position in a given ranking is regarded as some kind of criteria for educational quality. Especially, prospective students planning to go abroad for higher education have been inclined to use the positions of the universities in the rankings for their decision-making processes. Based on these facts, university administrators have felt pressure to improve management practices and been occupied with the problem of how to advance in these rankings. In order to develop any strategy for climbing in rankings, the decision maker of an institution can be able to compute the scaled scores of the indicators for each of the rankings and overall score as well, given the raw data. However, because of some difficulties like in obtaining some data or in understanding and applying the methodologies of rankings, it may not be possible to do these computations exactly. Nevertheless, one can try to obtain estimates with great accuracy, possibly with a software. WURS is designed to perform such estimation processes.

WURS offers a range of interactive smart tools helping the user to see how changing various indicators and the variables influence ranking, and to formulate an optimal strategy for achieving higher rankings, given the scaled and raw data.

Download brochure for the detailed features of WURS

For the technical details of WURS, see the paper at:
https://link.springer.com/article/10.1007/s11192-019-03269-8


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