Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province

Roberto Pico-Saltos, Lady Bravo-Montero, Néstor Montalván-Burbano, Javier Garzás and Andrés Redchuk
Additional contact information
Roberto Pico-Saltos: Department of Statistics and Operations Research, Rey Juan Carlos University, 28933 Madrid, Spain
Lady Bravo-Montero: Centro de Investigaciones y Proyectos Aplicados a las Ciencias de la Tierra (CIPAT), ESPOL Polytechnic University, Guayaquil 9015863, Ecuador
Javier Garzás: Department of Statistics and Operations Research, Rey Juan Carlos University, 28933 Madrid, Spain
Andrés Redchuk: Department of Statistics and Operations Research, Rey Juan Carlos University, 28933 Madrid, Spain

Sustainability, 2021, vol. 13, issue 16, 1-24

Abstract: Career success and its evaluation in university graduates generate growing interest in the academy when evaluating the university according to its mission and social mandate. Therefore, monitoring university graduates is essential in measuring career success in the State Technical University of Quevedo (UTEQ, acronym in Spanish). In this sense, this article aims to identify the predictive career success factors through survey application, development of two mathematical functions, and Weka’s classification learning algorithms application for objective career success levels determination in UTEQ university graduates. Researchers established a methodology that considers: (i) sample and data analysis, (ii) career success variables, (iii) variables selection, (iv) mathematical functions construction, and (v) classification models. The methodology shows the integration of the objective and subjective factors by approximating linear functions, which experts validated. Therefore, career success can classify university graduates into three levels: (1) not successful, (2) moderately successful, and (3) successful. Results showed that from 548 university graduates sample, 307 are men and 241 women. In addition, Pearson correlation coefficient between Objective Career Success ( OCS ) and Subjective Career Success ( SCS ) was 0.297, reason why construction models were separately using Weka’s classification learning algorithms, which allow OCS and SCS levels classification. Between these algorithms are the following: Logistic Model Tree (LMT), J48 pruned tree, Random Forest Tree (RF), and Random Tree (RT). LMT algorithm is the best suited to the predictive objective career success factors, because it presented 76.09% of instances correctly classified, which means 417 of the 548 UTEQ university graduates correctly classified according to OCS levels. In SCS model, RF algorithm shows the best results, with 94.59% of instances correctly classified (518 university graduates). Finally, 67.1% of UTEQ university graduates are considered successful, showing compliance with the university’s mission.

Keywords: career success; objective and subjective career success factors; classification learning algorithms; university graduates (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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