Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A data analytics approach for university competitiveness: the QS world university rankings
https://orcid.org/http://orcid.org/0000-0002-2015-0562
In recent years, higher education has felt pressured to prepare its graduates for the highly competitive international market due to globalization. Thus, many institutions have turned to position themselves well in university rankings as a way to attract the best academic and student talent from all over the world. Our work presents a predictive model for measuring university performance in the QS world university rankings (QS-WUR). We used a ten-year dataset to build models with statistical and machine learning algorithms contained in the library Caret of the RStudio software tool, to forecast global university position in QS-WUR. With these tools, we designed a methodology to predict the university partners' Final Scores based on their historical performance, achieving errors in the range of one or two points out of 100. The modelling may be a useful aid for university officers to develop strategies for improving institutional processes to attract the best students, faculty, and funding, enhance international collaboration and outlook, and foster international university prestige.
A data analytics approach for university competitiveness: the QS world university rankings
https://orcid.org/http://orcid.org/0000-0002-2015-0562
In recent years, higher education has felt pressured to prepare its graduates for the highly competitive international market due to globalization. Thus, many institutions have turned to position themselves well in university rankings as a way to attract the best academic and student talent from all over the world. Our work presents a predictive model for measuring university performance in the QS world university rankings (QS-WUR). We used a ten-year dataset to build models with statistical and machine learning algorithms contained in the library Caret of the RStudio software tool, to forecast global university position in QS-WUR. With these tools, we designed a methodology to predict the university partners' Final Scores based on their historical performance, achieving errors in the range of one or two points out of 100. The modelling may be a useful aid for university officers to develop strategies for improving institutional processes to attract the best students, faculty, and funding, enhance international collaboration and outlook, and foster international university prestige.
A data analytics approach for university competitiveness: the QS world university rankings
https://orcid.org/http://orcid.org/0000-0002-2015-0562
In recent years, higher education has felt pressured to prepare its graduates for the highly competitive international market due to globalization. Thus, many institutions have turned to position themselves well in university rankings as a way to attract the best academic and student talent from all over the world. Our work presents a predictive model for measuring university performance in the QS world university rankings (QS-WUR). We used a ten-year dataset to build models with statistical and machine learning algorithms contained in the library Caret of the RStudio software tool, to forecast global university position in QS-WUR. With these tools, we designed a methodology to predict the university partners' Final Scores based on their historical performance, achieving errors in the range of one or two points out of 100. The modelling may be a useful aid for university officers to develop strategies for improving institutional processes to attract the best students, faculty, and funding, enhance international collaboration and outlook, and foster international university prestige.
A data analytics approach for university competitiveness: the QS world university rankings
Int J Interact Des Manuf
Estrada-Real, Ana Carmen (Autor:in) / Cantu-Ortiz, Francisco J. (Autor:in)
01.09.2022
21 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Data science , Predictive modelling , University rankings , Machine learning , Statistics , Educational innovation , Higher education Engineering , Engineering, general , Engineering Design , Mechanical Engineering , Computer-Aided Engineering (CAD, CAE) and Design , Electronics and Microelectronics, Instrumentation , Industrial Design
Novel World University Rankings Combining Academic, Environmental and Resource Indicators
| DOAJ | 2021
Urban Smartness Vs Urban Competitiveness: A Comparison of Italian Cities Rankings
| BASE | 2014
Urban Smartness Vs Urban Competitiveness: A Comparison of Italian Cities Rankings
| BASE | 2014
Urban Smartness Vs Urban Competitiveness: A Comparison of Italian Cities Rankings
| BASE | 2014