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

dc.contributor.authorPico-Saltos, Roberto
dc.contributor.authorBravo-Montero , Lady
dc.contributor.authorMontalván-Burbano , Néstor
dc.contributor.authorGarzás, Javier
dc.contributor.authorRedchuk, Andrés
dc.date.accessioned2025-01-29T08:23:27Z
dc.date.available2025-01-29T08:23:27Z
dc.date.issued2021-08-20
dc.description.abstractCareer 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.
dc.identifier.citationPico-Saltos, Roberto; Bravo-Montero, Lady; Montalvan-Burbano, Nestor; Garzas, Javier; Redchuk, Andres (2021). Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Rios Province. Sustainability, 13(16), 9337-. DOI: 10.3390/su13169337
dc.identifier.doi10.3390/su13169337
dc.identifier.issn2071-1050
dc.identifier.urihttps://hdl.handle.net/10115/67117
dc.language.isoen_US
dc.publisherMDPI
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCareer success
dc.subjectObjective and subjective career success factors
dc.subjectClassification learning algorithms
dc.subjectUniversity graduates
dc.titleCareer Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province
dc.typeArticle

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
Career Success in University Graduates Evidence from an Ecuadorian Study in Los Ríos Province.pdf
Tamaño:
1.31 MB
Formato:
Adobe Portable Document Format