Análisis de la eficiencia de los procesos educativos en las ingenierías
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https://doi.org/10.26507/paper.2997Palabras clave:
Base de datos, Producción académica, Competencias, Procesos de aprendizajeResumen
En este estudio se presenta una función de producción académica diseñada específicamente para el proceso educativo de los ingenieros industriales en Colombia. El objetivo de esta función es examinar de manera imparcial las conexiones entre las competencias académicas adquiridas durante la educación secundaria y universitaria. Para llevar a cabo esta investigación, se utilizaron datos provenientes de pruebas estandarizadas realizadas a 4.977 estudiantes al finalizar tanto la educación secundaria como la universidad. El modelo empleado en este estudio consta de dos etapas principales. En la primera etapa, se evaluó de forma empírica la estructura de la función de producción mediante el uso de un enfoque llamado mínimos cuadrados parciales y modelización de ecuaciones estructurales. Posteriormente, en la segunda etapa, se estimó la eficiencia de las relaciones en la función de producción académica utilizando el Análisis Envolvente de Datos. Los resultados obtenidos revelaron un índice de bondad de ajuste del modelo empírico de 0,89, lo cual confirma la existencia de relaciones significativas entre las variables consideradas. Asimismo, el modelo validó cuatro relaciones de transformación y procedió a estimar la eficiencia de las interacciones en la función de producción. En cuanto a los resultados específicos, se observó que el modelo tiene una eficiencia media del 16,30%, 2,17% y 5,43% en su escala constante. En resumen, este modelo permite explicar la capacidad de las universidades para convertir los conocimientos adquiridos durante la educación secundaria en competencias académicas profesionales deseadas. Además, el modelo analiza el rendimiento profesional a partir de las interacciones entre las competencias académicas.
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Agasisti, T., Munda, G., & Hippe, R. (2019). Measuring the efficiency of European education systems by combining Data Envelopment Analysis and Multiple-Criteria Evaluation. Journal of Productivity Analysis, 51(2), 105–124. https://doi.org/10.1007/SC_11123-019-00549-6
Alabdulmenem, F. M. (2016). Measuring the Efficiency of Public Universities: Using Data Envelopment Analysis (DEA) to Examine Public Universities in Saudi Arabia. International Education Studies, 10(1), 137. https://doi.org/10.5539/ies.v10n1p137
Bernal, G. P., Villegas, L., & Toro, M. (2020). Saber Pro success prediction model using decision tree based learning. ArXiv.
Bianchi, N., & Giorcelli, M. (2020). Scientific Education and Innovation: From Technical Diplomas to University Stem Degrees. Journal of the European Economic Association, 18(5), 2608–2646. https://doi.org/10.1093/jeea/jvz049
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. 1978, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Colbert, A., Levary, R. R., & Shaner, M. C. (2000). Determining the relative efficiency of MBA programs using DEA. European Journal of Operational Research, 125(3), 656–669. https://doi.org/10.1016/S0377-2217(99)00275-1
Coll-Serrano, V., Benítez, R., & Bolós, V. (2018). Data Envelopment Analysis with deaR (1.2.0) [Computer software]. Universidad de Valencia. https://CRAN.R-project.org/package=deaR
Colombo, M. G., & Piva, E. (2020). Start-ups launched by recent STEM university graduates: The impact of university education on entrepreneurial entry. Research Policy, 49(6), 103993. https://doi.org/10.1016/j.respol.2020.103993
Cook, W. D., Ramón, N., Ruiz, J. L., Sirvent, I., & Zhu, J. (2019). DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans. Omega, 84, 45–54. https://doi.org/10.1016/j.omega.2018.04.004
De La Hoz, E., Zuluaga, R., & Mendoza, A. (2021). Assessing and Classification of Academic Efficiency in Engineering Teaching Programs. Journal on Efficiency and Responsibility in Education and Science, 14(1), 41–52. https://doi.org/10.7160/eriesj.2021.140104
de la Torre, E. M., Casani, F., & Sagarra, M. (2018). Defining typologies of universities through a DEA-MDS analysis: An institutional characterization for formative evaluation purposes. Research Evaluation, 27(4), 388–403. https://doi.org/10.1093/reseval/rvy024
Delahoz-Dominguez, E., Zuluaga, R., & Fontalvo-Herrera, T. (2020). Dataset of academic performance evolution for engineering students. Data in Brief, 30, 105537. https://doi.org/10.1016/j.dib.2020.105537
Duan, S. X. (2019). Measuring university efficiency: An application of data envelopment analysis and strategic group analysis to Australian universities. Benchmarking: An International Journal, 26(4), 1161–1173. https://doi.org/10.1108/BIJ-10-2017-0274
Galbraith, C. S., & Merrill, G. B. (2015). Academic performance and burnout: An efficient frontier analysis of resource use efficiency among employed university students. Journal of Further and Higher Education, 39(2), 255–277. https://doi.org/10.1080/0309877X.2013.858673
Gralka, S., Wohlrabe, K., & Bornmann, L. (2019). How to measure research efficiency in higher education? Research grants vs. publication output. Journal of Higher Education Policy and Management, 41(3), 322–341. https://doi.org/10.1080/1360080X.2019.1588492
Hanushek, E. A. (1979). Conceptual and Empirical Issues in the Estimation of Educational Production Functions. The Journal of Human Resources, 14(3), 351–388. https://doi.org/10.2307/145575
Hoeg, D. G., & Bencze, J. L. (2017). Values Underpinning STEM Education in the USA: An Analysis of the Next Generation Science Standards. Science Education, 101(2), 278–301. https://doi.org/10.1002/sce.21260
Jakaitiene, A., Zilinskas, A., & Stumbriene, D. (2018). Analysis of Education Systems Performance in European Countries by Means of PCA-DEA. Informatics in Education, 17(2), 245–263. https://eric.ed.gov/?id=EJ1195648
Johnes, J. (2006). Measuring teaching efficiency in higher education: An application of data envelopment analysis to economics graduates from UK Universities 1993. European Journal of Operational Research, 174(1), 443–456. https://doi.org/10.1016/j.ejor.2005.02.044
Jones, C. I. (2016). Chapter 1—The Facts of Economic Growth (J. B. Taylor & H. Uhlig, Eds.; Vol. 2, pp. 3–69). Elsevier. https://doi.org/10.1016/bs.hesmac.2016.03.002
Jones, T. H. (1981). Equal Educational Opportunity Revisited. Journal of Education Finance, 6(4), 471–484.
Kalapouti, K., Petridis, K., Malesios, C., & Dey, P. K. (2020). Measuring efficiency of innovation using combined Data Envelopment Analysis and Structural Equation Modeling: Empirical study in EU regions. Annals of Operations Research, 294(1), 297–320. https://doi.org/10.1007/s10479-017-2728-4
Law, L., & Fong, N. (2020). Applying partial least squares structural equation modeling (PLS-SEM) in an investigation of undergraduate students' learning transfer of academic English. Journal of English for Academic Purposes, 46, 100884. https://doi.org/10.1016/j.jeap.2020.100884
Long, P., & Siemens, G. (2014). Penetrating the fog: Analytics in learning and education. Italian Journal of Educational Technology, 22(3), 132–137. https://www.learntechlib.org/p/183382/
Lorcu, F., & Bolat, B. A. (2015). Comparison of secondary education PISA results in European member states and Turkey via DEA and SEM. Journal of WEI Business and Economics, 4(3), 7.
Madria, W. F., Miguel, A. S., & Li, R. C. (2019). Quality-Oriented Network DEA Model for the Research Efficiency of Philippine Universities. 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 596–600. https://doi.org/10.1109/IEEM44572.2019.8978816
Nazarko, J., & Šaparauskas, J. (2014). Application of DEA method in efficiency evaluation of public Higher Education Institutions. Technological and Economic Development of Economy, 20(1), 25–44. https://doi.org/10.3846/20294913.2014.837116
Ondé, D., & Alvarado, J. (2018). Scale Validation Conducting Confirmatory Factor Analysis: A Monte Carlo Simulation Study With LISREL. https://doi.org/10.3389/fpsyg.2018.00751
Puertas, R., & Marti, L. (2019). Sustainability in Universities: DEA-GreenMetric. Sustainability, 11(14), 3766. https://doi.org/10.3390/su11143766
Sanchez, G. (2013). PLS Path Modeling with R (0.4.9) [R]. https://github.com/gastonstat/plspm
Santín, D., & Sicilia, G. (2018). Using DEA for measuring teachers' performance and the impact on students' outcomes: Evidence for Spain. Journal of Productivity Analysis, 49(1), 1–15. https://doi.org/10.1007/SC_11123-017-0517-3
Shambaugh, J., Nunn, R., & Portman, B. (2017). Eleven Facts about Innovation and Patents. Economic Facts, 28.
Shamohammadi, M., & Oh, D. (2019). Measuring the efficiency changes of private universities of Korea: A two-stage network data envelopment analysis. Technological Forecasting and Social Change, 148, 119730. https://doi.org/10.1016/j.techfore.2019.119730
Suh, S. C., Bandi, H., Kim, J., & Tanik, U. J. (2020). Case Study: STEM Contribution in Indian IT Clusters. In C. Zintgraff, S. C. Suh, B. Kellison, & P. E. Resta (Eds.), STEM in the Technopolis: The Power of STEM Education in Regional Technology Policy (pp. 285–296). Springer International Publishing. https://doi.org/10.1007/978-3-030-39851-4_15
Visbal-Cadavid, D., Martínez-Gómez, M., & Guijarro, F. (2017). Assessing the efficiency of public universities through DEA. A case study. Sustainability, 9(8), 1416.
Wolszczak-Derlacz, J. (2017). An evaluation and explanation of (in)efficiency in higher education institutions in Europe and the U.S. with the application of two-stage semi-parametric DEA. Research Policy, 46(9), 1595–1605. https://doi.org/10.1016/j.respol.2017.07.010
Yang, G., Fukuyama, H., & Song, Y. (2018). Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model. Journal of Informetrics, 12(1), 10–30. https://doi.org/10.1016/j.joi.2017.11.002
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Derechos de autor 2023 Asociación Colombiana de Facultades de Ingeniería - ACOFI
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