A machine learning model to predict standardized tests in engineering programs in Colombia
This research develops a model to predict the results of Colombia’s national standardized test for Engineering programs. The research made it possible to forecast each student’s results and thus make decisions on reinforcement strategies to improve student performance. Therefore, a Learning Analytic...
- Autores:
-
Soto-Acevedo, Misorly
Zuluaga Ortiz, Rohemi Alfredo
Delahoz Domínguez, Enrique J.
Abuchar Curi, Alfredo Miguel
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12476
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12476
- Palabra clave:
- Learning Analytics
Machine Learning
Predictive Evaluation
Standardized tests
LEMB
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
| id |
UTB2_7a0fac43adf90e508b026e4a18063db3 |
|---|---|
| oai_identifier_str |
oai:repositorio.utb.edu.co:20.500.12585/12476 |
| network_acronym_str |
UTB2 |
| network_name_str |
Repositorio Institucional UTB |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
A machine learning model to predict standardized tests in engineering programs in Colombia |
| title |
A machine learning model to predict standardized tests in engineering programs in Colombia |
| spellingShingle |
A machine learning model to predict standardized tests in engineering programs in Colombia Learning Analytics Machine Learning Predictive Evaluation Standardized tests LEMB |
| title_short |
A machine learning model to predict standardized tests in engineering programs in Colombia |
| title_full |
A machine learning model to predict standardized tests in engineering programs in Colombia |
| title_fullStr |
A machine learning model to predict standardized tests in engineering programs in Colombia |
| title_full_unstemmed |
A machine learning model to predict standardized tests in engineering programs in Colombia |
| title_sort |
A machine learning model to predict standardized tests in engineering programs in Colombia |
| dc.creator.fl_str_mv |
Soto-Acevedo, Misorly Zuluaga Ortiz, Rohemi Alfredo Delahoz Domínguez, Enrique J. Abuchar Curi, Alfredo Miguel |
| dc.contributor.author.none.fl_str_mv |
Soto-Acevedo, Misorly Zuluaga Ortiz, Rohemi Alfredo Delahoz Domínguez, Enrique J. Abuchar Curi, Alfredo Miguel |
| dc.subject.keywords.spa.fl_str_mv |
Learning Analytics Machine Learning Predictive Evaluation Standardized tests |
| topic |
Learning Analytics Machine Learning Predictive Evaluation Standardized tests LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
This research develops a model to predict the results of Colombia’s national standardized test for Engineering programs. The research made it possible to forecast each student’s results and thus make decisions on reinforcement strategies to improve student performance. Therefore, a Learning Analytics approach based on three stages was developed: first, analysis and debugging of the database; second, multivariate analysis; and third, machine learning techniques. The results show an association between the performance levels in the Highschool test and the university test results. In addition, the machine learning algorithm that adequately fits the research problem is the Generalized Linear Network Model. For the training stage, the results of the model in Accuracy, AUC, Sensitivity, and Specificity were 0.810, 0.820, 0.813, and 0.827, respectively; in the evaluation stage, the results of the model in Accuracy, AUC, Sensitivity, and Specificity were 0.820, 0.820, 0.827 and 0.813 respectively. |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-09-05T19:21:36Z |
| dc.date.available.none.fl_str_mv |
2023-09-05T19:21:36Z |
| dc.date.issued.none.fl_str_mv |
2023-08 |
| dc.date.submitted.none.fl_str_mv |
2023-09-05 |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
publishedVersion |
| dc.identifier.citation.spa.fl_str_mv |
M. Soto-Acevedo, A. M. Abuchar-Curi, R. A. Zuluaga-Ortiz and E. J. Delahoz-Dominguez, "A machine learning model to predict standardized tests in engineering programs in Colombia," in IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, doi: 10.1109/RITA.2023.3301396. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/12476 |
| dc.identifier.doi.none.fl_str_mv |
10.1109/RITA.2023.3301396 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
M. Soto-Acevedo, A. M. Abuchar-Curi, R. A. Zuluaga-Ortiz and E. J. Delahoz-Dominguez, "A machine learning model to predict standardized tests in engineering programs in Colombia," in IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, doi: 10.1109/RITA.2023.3301396. 10.1109/RITA.2023.3301396 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/12476 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| rights_invalid_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.format.extent.none.fl_str_mv |
8 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.coverage.spatial.none.fl_str_mv |
Colombia |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
| dc.source.spa.fl_str_mv |
IEEE Revista Iberoamericana de Tecnologías del Aprendizaje |
| institution |
Universidad Tecnológica de Bolívar |
| bitstream.url.fl_str_mv |
https://repositorio.utb.edu.co/bitstream/20.500.12585/12476/1/A_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdf https://repositorio.utb.edu.co/bitstream/20.500.12585/12476/2/license.txt https://repositorio.utb.edu.co/bitstream/20.500.12585/12476/3/A_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdf.txt https://repositorio.utb.edu.co/bitstream/20.500.12585/12476/4/A_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdf.jpg |
| bitstream.checksum.fl_str_mv |
43badc1be2b7e4dca40f6bfb52f639b0 e20ad307a1c5f3f25af9304a7a7c86b6 3dd475353eaa3210ab27b0b2c811607f 84569cf4ebcb8a0488e6d5222ab2edaf |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional UTB |
| repository.mail.fl_str_mv |
repositorioutb@utb.edu.co |
| _version_ |
1814021722818478080 |
| spelling |
Soto-Acevedo, Misorly17358321-fc7b-4041-9a19-41ff8ce2b11aZuluaga Ortiz, Rohemi Alfredoffa49e06-fa7d-489d-ac1b-52e4cd107756Delahoz Domínguez, Enrique J.580c92b6-e798-459b-a3b7-e905126cd84fAbuchar Curi, Alfredo Miguel1c48a9a8-84ab-46f2-ad70-dcfbe11cc966500Colombia2023-09-05T19:21:36Z2023-09-05T19:21:36Z2023-082023-09-05M. Soto-Acevedo, A. M. Abuchar-Curi, R. A. Zuluaga-Ortiz and E. J. Delahoz-Dominguez, "A machine learning model to predict standardized tests in engineering programs in Colombia," in IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, doi: 10.1109/RITA.2023.3301396.https://hdl.handle.net/20.500.12585/1247610.1109/RITA.2023.3301396Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis research develops a model to predict the results of Colombia’s national standardized test for Engineering programs. The research made it possible to forecast each student’s results and thus make decisions on reinforcement strategies to improve student performance. Therefore, a Learning Analytics approach based on three stages was developed: first, analysis and debugging of the database; second, multivariate analysis; and third, machine learning techniques. The results show an association between the performance levels in the Highschool test and the university test results. In addition, the machine learning algorithm that adequately fits the research problem is the Generalized Linear Network Model. For the training stage, the results of the model in Accuracy, AUC, Sensitivity, and Specificity were 0.810, 0.820, 0.813, and 0.827, respectively; in the evaluation stage, the results of the model in Accuracy, AUC, Sensitivity, and Specificity were 0.820, 0.820, 0.827 and 0.813 respectively.8 páginasapplication/pdfengIEEE Revista Iberoamericana de Tecnologías del AprendizajeA machine learning model to predict standardized tests in engineering programs in Colombiainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Learning AnalyticsMachine LearningPredictive EvaluationStandardized testsLEMBinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cartagena de IndiasInvestigadoresJ. Aparicio, S. Perelman, y D. Santín, «Comparing the evolution of productivity and performance gaps in education systems through DEA: an application to Latin American countries», Oper. Res., jun. 2020, doi: 10.1007/s12351-020-00578-2.D. Visbal-Cadavid, M. Martínez-Gómez, y F. Guijarro, «Assessing the efficiency of public universities through DEA. A case study», Sustainability, vol. 9, n.o 8, p. 1416, 2017.M. Campo, «Capital humano para el avance colombiano, Editorial en Educación superior 20». p. 1, 2012.L. Valencia, H. Trefftz, y I. Delgado-González, «Acreditación Internacional de Carreras de Ingeniería», Educ. En Ing., vol. 15, n.o 29, pp. 28-33, 2020.R. Hoyos Martínez, M. Borja Maturana, R. Gómez Lorduy, y G. Casadiegos Aponte, «Calidad en la escuela vs. prácticas pedagógicas: los relatos como medio para la reflexión y la emancipación de los maestros en tiempos de la eficiencia», Esfera, vol. 5, n.o 2, p. 16, 2015.J. Guerrero, «La acreditación de alta calidad en Colombia», 2018.L. A. Sanabria James, M. C. Pérez Almagro, y L. E. Riascos Hinestroza, «Pruebas de evaluación Saber y PISA en la Educación Obligatoria de Colombia», Educ. Siglo XXI, vol. 38, n.o 3 Nov-Feb, pp. 231-254, 2020, doi: 10.6018/educatio.452891.L. A. Melo-Becerra, J. E. Ramos-Forero, y P. O. Hernández-Santamaría, «La educación superior en Colombia: situación actual y análisis de eficiencia», Desarro. Soc., vol. 2017, n.o 78, pp. 59-111, 2017, doi: 10.13043/DYS.78.2.Y . Bernal y C. Rodríguez, «Factores que Inciden en el Rendimiento Escolar de los Estudiantes de la Educación Básica Secundaria», Universidad Cooperativa de Colombia, 2017.R. Timarán-Pereira, J. Caicedo-Zambrano, y A. Hidalgo-Troya, «Árboles de decisión para predecir factores asociados al desempeño académico de estudiantes de bachillerato en las pruebas Saber 11°», Rev. Investig. Desarro. E Innov., vol. 9, n.o 2, pp. 363-378, 2019, doi: 10.19053/20278306.v9.n2.2019.9184A. Pentel y L. L. Kaiva, «Predicting Students' State Examination Results based on Previous Grades and Demographics», 11th Int. Conf. Inf. Intell. Syst. Appl. IISA 2020, 2020, doi: 10.1109/IISA50023.2020.9284401.F. Yang y F. W. B. Li, «Study on student performance estimation, student progress analysis, and student potential prediction based on data mining», Comput. Educ., vol. 123, n.o April, pp. 97-108, 2018, doi: 10.1016/j.compedu.2018.04.006.S. Zhang, X. Li, M. Zong, X. Zhu, y R. Wang, «Efficient kNN Classification With Different Numbers of Nearest Neighbors», IEEE TransNeural Netw. Learn. Syst., vol. 29, n.o 5, pp. 1774-1785, may 2018, doi: 10.1109/TNNLS.2017.2673241.A. Moldagulova y R. Bte. Sulaiman, «Using KNN algorithm for classification of textual documents», en 2017 8th International Conference on Information Technology (ICIT), Amman, Jordan, may 2017, pp. 665- 671. doi: 10.1109/ICITECH.2017.8079924.P. K. Dunn y G. K. Smyth, «Chapter 5: Generalized Linear Models: Structure», en Generalized Linear Models With Examples in R, P. K. Dunn y G. K. Smyth, Eds. New York, NY: Springer, 2018, pp. 211-241. doi: 10.1007/978-1-4419-0118-7_5.D. Zhang, «A Coefficient of Determination for Generalized Linear Models», Am. Stat., vol. 71, n.o 4, pp. 310-316, oct. 2017, doi: 10.1080/00031305.2016.1256839.E. De La Hoz, R. Zuluaga, y A. Mendoza, «Assessing and Classification of Academic Efficiency in Engineering Teaching Programs», J. Effic. Responsib. Educ. Sci., vol. 14, n.o 1, Art. n.o 1, mar. 2021, doi: 10.7160/eriesj.2021.140104.G. Louppe, «Understanding Random Forests: From Theory to Practice», ArXiv14077502 Stat, jul. 2014, Accedido: 23 de julio de 2019. [En línea]. Disponible en: http://arxiv.org/abs/1407.7502S. Suthaharan, «Support Vector Machine», en Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, S. Suthaharan, Ed. Boston, MA: Springer US, 2016, pp. 207-235. doi: 10.1007/978-1-4899-7641-3_9.D. Buzic y J. Dobsa, «Lyrics classification using Naive Bayes», en 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, may 2018, pp. 1011-1015. doi: 10.23919/MIPRO.2018.8400185.K. David Kolo, S. Adepoju, y J. Kolo Alhassan, «A Decision Tree Approach for Predicting Students Academic Performance», Int. J. Educ. Manag. Eng., vol. 5, n.o 5, pp. 12-19, oct. 2015, doi: 10.5815/ijeme.2015.05.02.T. Chen y C. Guestrin, «XGBoost: A Scalable Tree Boosting System», en Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16, San Francisco, California, USA, 2016, pp. 785-794. doi: 10.1145/2939672.2939785.A. J. Hung, J. Chen, y I. S. Gill, «Automated Performance Metrics and Machine Learning Algorithms to Measure Surgeon Performance and Anticipate Clinical Outcomes in Robotic Surgery», JAMA Surg., vol. 153, n.o 8, p. 770, ago. 2018, doi: 10.1001/jamasurg.2018.1512.Z. H. Hoo, J. Candlish, y D. Teare, «What is an ROC curve?», Emerg. Med. J., vol. 34, n.o 6, pp. 357-359, jun. 2017, doi: 10.1136/emermed-2017-206735.D. Gašević, V. Kovanović, y S. Joksimović, «Piecing the learning analytics puzzle: a consolidated model of a field of research and practice», Learn. Res. Pract., vol. 3, n.o 1, pp. 63-78, 2017, doi: 10.1080/23735082.2017.1286142.R Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2013. Accedido: 15 de abril de 2020. [En línea]. Disponible en: http://www.polsci.wvu.edu/duval/PS603/Notes/R/fullrefman.pdfE. Delahoz-Dominguez, R. Zuluaga, y T. Fontalvo-Herrera, «Dataset of academic performance evolution for engineering students», Data Brief, vol. 30, p. 105537, jun. 2020, doi: 10.1016/j.dib.2020.105537.P. Herrera-Idárraga, E. López-Bazo, y E. Motellón, «Regional Wage Gaps, Education and Informality in an Emerging Country: The Case of Colombia», Spat. Econ. Anal., vol. 11, n.o 4, pp. 432-456, oct. 2016, doi: 10.1080/17421772.2016.1190462.J. Moreno-Gómez, J. Calleja-Blanco, y G. Moreno-Gómez, «Measuring the efficiency of the Colombian higher education system: a two-stage approach», Int. J. Educ. Manag., vol. 34, n.o 4, pp. 794-804, ene. 2020, doi: 10.1108/IJEM-07-2019-0236.E. J. Delahoz-Dominguez, S. Guillen-Ibarra, T. Fontalvo-Herrera, E. J. Delahoz-Dominguez, S. Guillen-Ibarra, y T. Fontalvo-Herrera, «Análisis de la acreditación de calidad en programas de ingeniería industrial y los resultados en las pruebas nacionales estandarizadas, en Colombia», Form. Univ., vol. 13, n.o 1, pp. 127-134, feb. 2020, doi: 10.4067/S0718- 50062020000100127.P. Kaur, M. Singh, y G. S. Josan, «Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector», Procedia Comput. Sci., vol. 57, pp. 500-508, 2015, doi: 10.1016/j.procs.2015.07.372.S. T. Jishan, R. I. Rashu, N. Haque, y R. M. Rahman, «Improving accuracy of students' final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique», Decis. Anal., vol. 2, n.o 1, p. 1, dic. 2015, doi: 10.1186/s40165-014-0010-2.E. T. Lau, L. Sun, y Q. Yang, «Modelling, prediction and classification of student academic performance using artificial neural networks», SN Appl. Sci., vol. 1, n.o 9, p. 982, ago. 2019, doi: 10.1007/s42452-019-0884-7.http://purl.org/coar/resource_type/c_2df8fbb1ORIGINALA_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdfA_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdfapplication/pdf740693https://repositorio.utb.edu.co/bitstream/20.500.12585/12476/1/A_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdf43badc1be2b7e4dca40f6bfb52f639b0MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/12476/2/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD52TEXTA_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdf.txtA_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdf.txtExtracted texttext/plain48490https://repositorio.utb.edu.co/bitstream/20.500.12585/12476/3/A_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdf.txt3dd475353eaa3210ab27b0b2c811607fMD53THUMBNAILA_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdf.jpgA_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdf.jpgGenerated Thumbnailimage/jpeg9914https://repositorio.utb.edu.co/bitstream/20.500.12585/12476/4/A_machine_learning_model_to_predict_standardized_tests_in_engineering_programs_in_Colombia.pdf.jpg84569cf4ebcb8a0488e6d5222ab2edafMD5420.500.12585/12476oai:repositorio.utb.edu.co:20.500.12585/124762024-04-10 15:11:52.968Repositorio Institucional UTBrepositorioutb@utb.edu.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 |