Assessing and classification of academic efficiency in engineering teaching programs
This research uses a three-phase method to evaluate and forecast the academic efficiency of engineering programs. In the first phase, university profiles are created through cluster analysis. In the second phase, the academic efficiency of these profiles is evaluated through Data Envelopment Analysi...
- Autores:
-
De la Hoz Domínguez, Enrique José
Zuluaga Ortiz, Rohemi Alfredo
Mendoza, Adel
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/10344
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/10344
- Palabra clave:
- Efficiency
Higher education
Machine learning
Predictive evaluation
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Assessing and classification of academic efficiency in engineering teaching programs |
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Assessing and classification of academic efficiency in engineering teaching programs |
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Assessing and classification of academic efficiency in engineering teaching programs Efficiency Higher education Machine learning Predictive evaluation |
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Assessing and classification of academic efficiency in engineering teaching programs |
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Assessing and classification of academic efficiency in engineering teaching programs |
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Assessing and classification of academic efficiency in engineering teaching programs |
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Assessing and classification of academic efficiency in engineering teaching programs |
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Assessing and classification of academic efficiency in engineering teaching programs |
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De la Hoz Domínguez, Enrique José Zuluaga Ortiz, Rohemi Alfredo Mendoza, Adel |
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De la Hoz Domínguez, Enrique José Zuluaga Ortiz, Rohemi Alfredo Mendoza, Adel |
| dc.subject.keywords.spa.fl_str_mv |
Efficiency Higher education Machine learning Predictive evaluation |
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Efficiency Higher education Machine learning Predictive evaluation |
| description |
This research uses a three-phase method to evaluate and forecast the academic efficiency of engineering programs. In the first phase, university profiles are created through cluster analysis. In the second phase, the academic efficiency of these profiles is evaluated through Data Envelopment Analysis. Finally, a machine learning model is trained and validated to forecast the categories of academic efficiency. The study population corresponds to 256 university engineering programs in Colombia and the data correspond to the national examination of the quality of education in Colombia in 2018. In the results, two university profiles were identified with efficiency levels of 92.3% and 97.3%, respectively. The Random Forest model presents an Area under ROC value of 95.8% in the prediction of the efficiency profiles. The proposed structure evaluates and predicts university programs’ academic efficiency, evaluating the efficiency between institutions with similar characteristics, avoiding a negative bias toward those institutions that host students with low educational levels. |
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2021 |
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2021-07-30T12:20:57Z |
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2021-03-31 |
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2021-07-29 |
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De La Hoz, E., Zuluaga, R. and Mendoza, A. (2021) ’Assessing and Classification of Academic Efficiency in Engineering Teaching Programs’, Journal on Efficiency and Responsibility in Education and Science, vol. 14, no. 1, pp. 41-52 https://doi.org/10.7160/eriesj.2021.140104. |
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https://hdl.handle.net/20.500.12585/10344 |
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10.7160/ERIESJ.2021.140104 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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De La Hoz, E., Zuluaga, R. and Mendoza, A. (2021) ’Assessing and Classification of Academic Efficiency in Engineering Teaching Programs’, Journal on Efficiency and Responsibility in Education and Science, vol. 14, no. 1, pp. 41-52 https://doi.org/10.7160/eriesj.2021.140104. 10.7160/ERIESJ.2021.140104 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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12 páginas |
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Colombia |
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Cartagena de Indias |
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De la Hoz Domínguez, Enrique Joséd820e6e7-ec43-422f-9489-1e8c58a6a11bZuluaga Ortiz, Rohemi Alfredoe6f23b76-efe9-4169-bab5-33ea282e89c9Mendoza, Adel5a21af09-876c-499c-b7c3-5cc4cf3697f5Colombia2021-07-30T12:20:57Z2021-07-30T12:20:57Z2021-03-312021-07-29De La Hoz, E., Zuluaga, R. and Mendoza, A. (2021) ’Assessing and Classification of Academic Efficiency in Engineering Teaching Programs’, Journal on Efficiency and Responsibility in Education and Science, vol. 14, no. 1, pp. 41-52 https://doi.org/10.7160/eriesj.2021.140104.https://hdl.handle.net/20.500.12585/1034410.7160/ERIESJ.2021.140104Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis research uses a three-phase method to evaluate and forecast the academic efficiency of engineering programs. In the first phase, university profiles are created through cluster analysis. In the second phase, the academic efficiency of these profiles is evaluated through Data Envelopment Analysis. Finally, a machine learning model is trained and validated to forecast the categories of academic efficiency. The study population corresponds to 256 university engineering programs in Colombia and the data correspond to the national examination of the quality of education in Colombia in 2018. In the results, two university profiles were identified with efficiency levels of 92.3% and 97.3%, respectively. The Random Forest model presents an Area under ROC value of 95.8% in the prediction of the efficiency profiles. The proposed structure evaluates and predicts university programs’ academic efficiency, evaluating the efficiency between institutions with similar characteristics, avoiding a negative bias toward those institutions that host students with low educational levels.application/pdf12 páginasenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Efficiency and Responsibility in Education and Science, vol. 14, no. 1, pp. 41-52Assessing and classification of academic efficiency in engineering teaching programsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1EfficiencyHigher educationMachine learningPredictive evaluationCartagena de IndiasInvestigadoresAgasisti, T., Munda, G. and Hippe, R. 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(2019) ‘Measuring efficiency in education: The influence of imprecision and variability in data on DEA estimates’, Socio-Economic Planning Sciences, Vol. 68, 100698. https://doi.org/10.1016/j.seps.2019.03.004Aparicio, J., Perelman, S. and Santín, D. (2020) ‘Comparing the evolution of productivity and performance gaps in education systems through DEA: an application to Latin American countries’, Operational Research: An International Journal. https://doi.org/10.1007/s12351-020-00578-2Benicio, J. and Mello, J. C. S. de (2015) ‘Productivity Analysis and Variable Returns of Scale: DEA Efficiency Frontier Interpretation’, Procedia Computer Science, Vol. 55, pp. 341–349. https://doi.org/10.1016/j.procs.2015.07.059Berens, J., Schneider, K., Gortz, S., Oster, S. and Burghoff, J. 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(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, Vol. 46, No. 9, pp. 1595–1605. https://doi.org/10.1016/j.respol.2017.07.010http://purl.org/coar/resource_type/c_2df8fbb1ORIGINAL375-Article Text-2118-1-10-20210331 (1)_oz De la Hoz Domingu.pdf375-Article Text-2118-1-10-20210331 (1)_oz De la Hoz Domingu.pdfapplication/pdf768895https://repositorio.utb.edu.co/bitstream/20.500.12585/10344/1/375-Article%20Text-2118-1-10-20210331%20%281%29_oz%20De%20la%20Hoz%20Domingu.pdf6127a50e8d65e0e9e09af6c0bf2747a7MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/10344/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/10344/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXT375-Article 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