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Thanks to CIPAT-ESPOL for the support and accompaniment in the development of this publication and specially to Ing. Paul Carrion-Mero (CIPAT Director), Ing. Amilkar Puris-Caceres, and Eng. Paulo Escandon for their collaboration. The preparation of this study counted with the collaboration of various scientific research projects, such as the academic projects of the ESPOL University (Escuela Superior Politecnica del Litoral) with code no. CIPAT-01-2018 Registro del Patrimonio Geologico y Minero y su incidencia en la defensa y preservacion de la geodiversidad en Ecuador (Registry of Geological and Mining Heritage and its impact on the defense and preservation of geodiversity in Ecuador); the support of NOVA Science Research Associates and UTEQ project Prediccion y valoracion del exito profesional de graduados universitarios (Prediction and assessment of the professional success of university graduates), with code 44.

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

Publicado en:Sustainability. 13 (16): 9337- - 2021-08-01 13(16), DOI: 10.3390/su13169337

Autores: Pico-Saltos, Roberto; Bravo-Montero, Lady; Montalvan-Burbano, Nestor; Garzas, Javier; Redchuk, Andres

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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.

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