Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province
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 Quev...
| Main Authors: | , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2021
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| Online Access: | http://hdl.handle.net/10835/12097 https://doi.org/10.3390/su13169337 |
| _version_ | 1789406738726780928 |
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| author | Pico Saltos, Roberto Bravo Montero, Lady Montalván Burbano, Néstor Garzás, Javier Redchuk, Andrés |
| author_facet | Pico Saltos, Roberto Bravo Montero, Lady Montalván Burbano, Néstor Garzás, Javier Redchuk, Andrés |
| author_sort | Pico Saltos, Roberto |
| collection | DSpace |
| description | 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. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-12097 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2021 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-120972023-04-12T19:10:37Z Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province Pico Saltos, Roberto Bravo Montero, Lady Montalván Burbano, Néstor Garzás, Javier Redchuk, Andrés career success objective and subjective career success factors classification learning algorithms university graduates 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. 2021-09-06T09:56:55Z 2021-09-06T09:56:55Z 2021-08-20 info:eu-repo/semantics/article 2071-1050 http://hdl.handle.net/10835/12097 https://doi.org/10.3390/su13169337 en https://www.mdpi.com/2071-1050/13/16/9337 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | career success objective and subjective career success factors classification learning algorithms university graduates Pico Saltos, Roberto Bravo Montero, Lady Montalván Burbano, Néstor Garzás, Javier Redchuk, Andrés Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province |
| title | Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province |
| title_full | Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province |
| title_fullStr | Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province |
| title_full_unstemmed | Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province |
| title_short | Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province |
| title_sort | career success in university graduates: evidence from an ecuadorian study in los ríos province |
| topic | career success objective and subjective career success factors classification learning algorithms university graduates |
| url | http://hdl.handle.net/10835/12097 https://doi.org/10.3390/su13169337 |
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