PREDICTIVE MODEL OF SCORES IN STANDARDIZED HIGH SCHOOL TESTS FOR BOGOTÁ, COLOMBIA.

Main Article Content

Oscar Jardey Suarez
Edier Hernán Bustos Velazco
Jaime Duván Reyes Roncancio

Keywords

.

Abstract

Scores on standardized tests are part of the measure of educational quality. The objective is to construct a predictive model, applying linear regression and multilevel linear regression, for the scores of the Saber 11 tests, using statistically significant factors collected from surveys administered to test takers between 2019 and 2022 in Bogotá, Colombia. The methodological approach is quantitative. The information organization procedure follows the Standard Process for Data Mining across Industries. The modeling utilized the open database of the Colombian Institute for the Promotion of Higher Education, consisting of 129,087 records (over 81% of the total). Women account for 53% of the records in the study. The Inter Class Correlation in the multilevel linear regression model among the 20 localities is 1.54. The Mean Absolute Percentage Error in the linear regression model is 11.79% for the entire dataset and 11.81% when using 70% of the data for training. The statistically significant factors include gender, socio-economic status, resources for studying at home, nutrition, student employment, and the institution. In conclusion, the possession and access to technological resources, hardware, and software, as well as the urban location of the institution, had a positive impact on scores during the COVID-19 pandemic, providing empirical evidence of a wider educational gap in populations with limited technological access or residing in rural areas.

Abstract 111 | Pdf Downloads 40

References

1. Atlam, E. S., Ewis, A., El-Raouf, M. M. A., Ghoneim, O., & Gad, I. (2022). A new approach to identifying the psychological impact of COVID-19 on university student’s academic performance. Alexandria Engineering Journal, 61(7), 5223–5233. https://doi.org/10.1016/j.aej.2021.10.046
2. Alcaldía de Bogotá. (2021). Estratificación socioeconómica. Recuperado de https://www.bogota.gov.co/sisjur/normas/Norma1.jsp?i=94978
3. Baashar, Y., Alkawsi, G., Mustafa, A., Alkahtani, A. A., Alsariera, Y. A., Ali, A. Q., Hashim, W., & Tiong, S. K. (2022). Toward Predicting Student's Academic Performance Using Artificial Neural Networks (ANNs). Applied Sciences, 12(3). https://doi.org/10.3390/app12031289
4. Beckham, N. R., Akeh, L. J., Mitaart, G. N. P., & Moniaga, J. V. (2023). Determining factors that affect student performance using various machine learning methods. Procedia Computer Science, 216, 597–603. https://doi.org/10.1016/j.procs.2022.12.174
5. Bocanegra-Acosta, H., & Huertas-Bustos, A. P. (2018). La política de jornada única escolar: los referentes y la experiencia de una Institución Educativa Distrital. Revista Republicana, 25, 199–240. https://doi.org/https://doi.org/10.21017/rev.repub.2018.v25.a56
6. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.O step-by-step data mining guide. NCR System Engineering Copenhagen, DaimlerChrysler AG, SPSS and Verzekeringen en Bank Groep B.V. http://www.crisp-dm.org/CRISPWP-0800.pdf
7. Congreso de Colombia. (1994). Ley 142 de 1994. Por la cual se establece el régimen de los servicios públicos domiciliarios y se dictan otras disposiciones. Diario Oficial No. 41.257, de 11 de julio de 1994
8. Contreras Bravo, L. E., Nieves-Pimiento, N., & Gonzalez-Guerrero, K. (2022). Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods. Ingeniería, 28(1). https://doi.org/10.14483/23448393.19514
9. De-Myttenaere, A., Golden, B., Le-Grand, B., & Rossi, F. (2016). Mean Absolute Percentage Error for regression models. Neurocomputing, 192, 38–48. https://doi.org/10.1016/j.neucom.2015.12.114
10. Departamento Nacional de Planeación. (2014). Decreto Nacional 298 de 2014. Por el cual se reglamenta la estratificación socioeconómica. Diario Oficial No. 49.172, de 23 de diciembre de 2014.
11. Donner, A., & Koval, J. J. (1980). The Estimation of Intraclass Correlation in the Analysis of Family Data. Biometrics, 36(1), 19. https://doi.org/10.2307/2530491
12. Ekubo, E. A., & Esiefarienrhe, B. M. (2022). Using machine learning to predict low academic performance at a Nigerian university. The African Journal of Information and Communication (AJIC), 30. https://doi.org/10.23962/ajic.i30.14839
13. Galster, G., Santiago, A., Stack, L., & Cutsinger, J. (2016). Neighborhood effects on secondary school performance of Latino and African American youth: Evidence from a natural experiment in Denver. Journal of Urban Economics, pp. 93, 30–48. https://doi.org/10.1016/j.jue.2016.02.004
14. Giannakas, F., Troussas, C., Voyiatzis, I., & Sgouropoulou, C. (2021). A deep learning classification framework for early prediction of team-based academic performance. Applied Soft Computing, 106. https://doi.org/10.1016/j.asoc.2021.107355
15. Gimeno-Tena, A., & Esteve-Clavero, A. (2021). Relación entre los hábitos saludables y el rendimiento académico en los estudiantes de la Universitat Jaume I. Nutricion Clinica y Dietética Hospitalaria, 41(2), 99–106. https://doi.org/https://doi.org/10.12873/412gimeno
16. Ibourk, A., & Amaghouss, J. (2016). Convergence éducative et déterminants socioéconomiques: Analyse spatiale sur des données marocaines. Mondes en Developpement, 176(4), 93–116. https://doi.org/10.3917/med.176.0093
17. Kumari, P., Jain, P., & Pamula, R. (2018). An Efficient use of Ensemble Methods to Predict Students Academic Performance. 4th Int’l Conf. on Recent Advances in Information Technology. https://doi.org/10.1109/RAIT.2018.8389056
18. Lisboa- Bartholo, T., & Da-Costa, M. (2016). Evidence of a school composition effect in Rio de Janeiro public schools. Ensaio, 24(92), 498–521. https://doi.org/10.1590/S0104-40362016000300001
19. Maisarah-Samsudin, N., Milleana-Shaharudin, S., Filza-Sulaiman, N., Mohd-Fuad, M., Fareezuan-Zulfikri, M., & Hila-Zainuddin, N. (2021). Modeling student’s academic performance during Covid-19 based on classification in support vector machine. Turkish Journal of Computer and Mathematics Education, 12(5), 1798–1804. https://doi.org/10.17762/turcomat.v12i5.2190
20. Martínez-Mateus, W. (2015). Análisis de distribución geográfica y espacial de los resultadosde las Pruebas Saber 11 del Instituto Colombiano para el Fomento de la Educación Superior -ICFES-. 2005-2012. Colombia. Cuadernos Latinoamericanos de Administración, 11(21), 39–50. https://doi.org/10.18270/cuaderlam.v11i21.1618
21. Masci, C., Johnes, G., & Agasisti, T. (2018). Student and school performance across countries: A machine learning approach. European Journal of Operational Research, 269(3), 1072–1085. https://doi.org/10.1016/j.ejor.2018.02.031
22. Mora, J. I., Mosqueira, C. M. H., & Ventura-Vall-Llovera, C. (2019). Hábitos alimentarios y rendimiento académico en escolares adolescentes de Chile. Revista Española de Nutricion Humana y Dietética, 23(4), 292–301. https://doi.org/https://doi.org/10.14306/renhyd.23.4.804
23. Murillo-Torrecilla, J. (2008). Los modelos multinivel como herramienta para la investigación educativa. Magis Revista Internacional de Investigación en Educación, 1(1), 45–62. https://www.redalyc.org/pdf/2810/281021687004.pdf
24. Murillo, J., & Carrillo, S. (2021). Incidencia de la Segregación Escolar por Nivel Socioeconómico en el Rendimiento Académico. Un Estudio desde Perú. Archivos analíticos de políticas educativas, 29(49), 3–11. https://doi.org/10.14507/epaa.29.5129
25. Navarro, R. E. (2003). EL rendimiento académico: concepto, investigación y desarrollo. REICE - Revista Electrónica Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 1(2), 1–15. https://doi.org/2152
26. Orjuela, J. (2014). Análisis del Desempeño Estudiantil en las Pruebas de Estado para Educación Media en Colombia mediante Modelos Jerárquicos Lineales. Ingeniería, 18(2). https://doi.org/10.14483/udistrital.jour.reving.2013.2.a04
27. Parra-Castillo, A., Morales-Canedo, L. M., & Medina-Valencia, M. M. (2021). Relación entre los hábitos alimentarios y el rendimiento académico en estudiantes de universidades públicas y privadas de la localidad de Chapinero, Bogotá. Perspectivas en Nutrición Humana, 23(2), 183–195. https://doi.org/https://doi.org/10.17533/udea.penh.v23n2a05
28. Pedraza-Avella, A. C., & Ribero-Medina, R. (2006). El trabajo infantil y juvenil en Colombia y algunas de sus consecuencias claves. Revista Latinoamericana de Ciencias Sociales, Niñez y Juventud, 4(1), 7. https://dialnet.unirioja.es/servlet/articulo?codigo=4657561&info=resumen&idioma=SPA
29. Peris, M., Maganto, C., & Garaigordobil, M. (2018). Escala de riesgo de adicción-adolescente a las redes sociales e internet: fiabilidad y validez (ERA-RSI). Revista de psicología Clínica con Niños y Adolescentes, 5(2), 30–36. https://doi.org/10.21134/rpcna.2018.05.2.4
30. Posada, J., & Mendoza, F. (2014). Determinantes del logro académico de los estudiantes de grado 11 en el periodo 2008 – 2010 . Una perspectiva de género y región. Universidad del Valle, 1–48.
31. Qazdar, A., Er-Raha, B., Cherkaoui, C., & Mammass, D. (2019). A machine learning algorithm framework for predicting students performance: A case study of baccalaureate students in Morocco. Education and Information Technologies, 24(6), 3577–3589. https://doi.org/10.1007/s10639-019-09946-8
32. Qiu, X., & Wu, S. sheng. (2019). Contextual variables of student math proficiency and their geographic variations in Missouri. Applied Geography, 109, 102040. https://doi.org/10.1016/j.apgeog.2019.102040
33. Ramírez, C. E., & Teichler, T. U. (2014). Factores socioeconómicos y educativos asociados con el desempeño académico, según nivel de formación y género de los estudiantes que presentaron la prueba SABER PRO 2009. 26.
34. Rebai, S., Ben Yahia, F., & Essid, H. (2019). A graphically based machine learning approach to predict secondary schools performance in Tunisia. Socio-Economic Planning Sciences, 70(August 2018), 100724. https://doi.org/10.1016/j.seps.2019.06.009
35. Romero, C. (2009). Eficacia aprendizaje y de instituciones saber 2009 -1 Análisis de la eficacia de aprendizaje y eficacia de las instituciones educativas mediante el uso de los datos de la Prueba Censal SAB. SaberInvestigar.
36. Salal, Y., & Abdullaev, S. (2020). Deep learning based Ensemble Approach to Predict Student Academic Performance: Case Study. En 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS). https://doi.org/10.1109/ICISS49785.2020.9316044
37. Santos-Holguín, S. A., & Barros-Rivera, S. E. (2022). Influencia del Estado Nutricional en el Rendimiento Académico en una institución educativa. Revista De investigación en salud Vive, 5(13), 154–169. https://doi.org/https://doi.org/10.33996/revistavive.v5i13.138
38. Santos Holguín, S. A., & Barros Rivera, S. E. (2022). Influencia del Estado Nutricional en el Rendimiento Académico en una institución educativa. Revista De investigación en salud Vive, 5(13), 154–169. https://doi.org/https://doi.org/10.33996/revistavive.v5i13.138
39. Shah, M., Kaistha, M., & Gupta, Y. (2019). Student Performance Assessment and Prediction System using Machine Learning. 4th International Conference on Information Systems and Computer Networks, ISCON 2019, 386–390. https://doi.org/10.1109/ISCON47742.2019.9036250
40. Suárez, O., Urbina-Cárdenas, J., & Suárez-Riveros, D. (2022). Factores de Riesgo en Jóvenes Escolarizados Asociados al Uso de las Redes Sociales y la Internet. revista Perspectivas, 7(1), 87–97. https://revistas.ufps.edu.co/index.php/perspectivas/article/view/3392
41. Taras, H. (2005). Sleep and student performance at school. Journal of School Health, 75(6), 199.
42. Wandera, H., Marivate, V., & Sengeh, M. (2019). Predicting national school performance for policy making in South Africa. 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019, 23–28. https://doi.org/10.1109/ISCMI47871.2019.9004323
43. Woodhouse, A., & Lamport, M. (2012). The Relationship of Food and Academic Performance: A Preliminary Examination of the Factors of Nutritional Neuroscience, Malnutrition, and Diet Adequacy. Christian Perspectives in Education, 5(1), 1.

Most read articles by the same author(s)