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

• Published in: International journal on interactive design and manufacturing 2020-12, Vol.14 (4), p.1577-1592
• Main Authors: Chavez, Mario D., Ceballos, Héctor G., Cantu-Ortiz, Francisco J.
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Author productivity; CAE) and Design; Colleges & universities; Computer-Aided Engineering (CAD; Data analysis; Data mining; Datasets; Digital technology; Distance learning; Educational services; Electronics and Microelectronics; Engineering; Engineering Design; Enrollments; Higher education; Higher education institutions; Industrial Design; Instrumentation; Mechanical Engineering; Pedagogy; Research facilities; Statistical analysis; Students; Teacher evaluations; Teachers; Teaching; Technical Paper; Variance analysis
• ISSN: 1955-2513; 1955-2505
• DOI: 10.1007/s12008-020-00713-5

This research article presents a study to compare the teaching performance of teaching-only versus teaching-and-research professors at higher education institutions. It is a common belief that, generally, teaching professors outperform research professors in teaching-and-research universities according to student perceptions reflected in student surveys. This case study presents experimental evidence that shows this is not always the case and that, under certain circumstances, it can be the contrary. The case study is from Tecnologico de Monterrey (Tec), a teaching-and-research, private university in Mexico that has developed a research profile during the last two decades using a mix of teaching-only and teaching-and-research faculty members; during this time period, the university has had a growing ascendancy in world university rankings. Data from an institutional student survey called the ECOA was used. The data set contains more than 118,000 graduate and undergraduate courses for 5 semesters (January 2017 to May 2019). The results presented were derived from statistical to data mining methods, including Analysis of Variance and Logistic Regression, that were applied to this data set of more than nine thousand professors who taught those courses. The results show that teaching-and-research professors perform better or at least the same as teaching-only professors. The differences found in teaching with respect to attributes like professors’ gender, age, and research level are also presented.