A data analytics approach to contrast the performance of teaching (only) vs. research professors

Chavez, Mario D.; Ceballos, Héctor G.; Cantú-Ortiz, Francisco J.

doi:10.1007/s12008-020-00713-5

Cited by 1 publication

(2 citation statements)

References 22 publications

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“…These results are published annually by ranking organizations, and the media coverage of these rankings intensifies public interest, praise, and critique. In fact, in 2007, the OECD published a document describing how rankings influence HEI: (1) 50% of respondents used rankings for publicity, (2) 70% wanted to be in the top 10% nationally, (3) 71% wanted to be in the top 25% internationally, (4) over 50% had a formal process of reviewing results, (5) and 68% used rankings as a strategic tool for management and academic improvement [4]. Chavez et al analysed the importance of having research professors with excellent teaching abilities to attract good students [5].…”

Section: Introductionmentioning

confidence: 99%

“…In fact, in 2007, the OECD published a document describing how rankings influence HEI: (1) 50% of respondents used rankings for publicity, (2) 70% wanted to be in the top 10% nationally, (3) 71% wanted to be in the top 25% internationally, (4) over 50% had a formal process of reviewing results, (5) and 68% used rankings as a strategic tool for management and academic improvement [4]. Chavez et al analysed the importance of having research professors with excellent teaching abilities to attract good students [5]. Cantu et al elaborates about the importance of watching artificial intelligence technology trends and its impact in higher education [6].…”

Section: Introductionmentioning

confidence: 99%

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A data analytics approach for university competitiveness: the QS world university rankings

Estrada-Real

Cantú-Ortiz

2022

Int J Interact Des Manuf

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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.

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Section: Introductionmentioning

confidence: 99%