Artículos
Publicado 2020-01-04
Palabras clave
- datos estudiantiles,
- educación superior,
- minería de datos,
- modelos de predicción,
- deserción
Cómo citar
Perez-Gutierrez, B. R. (2020). Comparación de técnicas de minería de datos para identificar indicios de deserción estudiantil, a partir del desempeño académico. Revista UIS Ingenierías, 19(1), 193–204. https://doi.org/10.18273/revuin.v19n1-2020018
Derechos de autor 2020 Revista UIS Ingenierías
Esta obra está bajo una licencia internacional Creative Commons Atribución-SinDerivadas 4.0.
Resumen
Uno de los grandes retos en las instituciones educativas consiste en poder establecer la posibilidad de retiro o deserción de sus estudiantes. En este artículo se presentan los resultados de un estudio de comparación de técnicas para apoyar la identificación de deserción estudiantil a partir del registro académico de los estudiantes de una Universidad en Colombia para el programa de Ingeniería de Sistemas. El registro académico se estableció para un periodo de 7 años. Árboles de decisión, regresión logística y Naive Bayes, fueron comparados para lograr establecer la mejor técnica de detección de desertores. Adicionalmente, la herramienta Watson Analytics de IBM fue utilizada para comparar su usabilidad y precisión para un usuario no experto. Nuestra experimentación demostró que el uso de algoritmos simples es suficiente para alcanzar niveles ideales de precisión. Estos resultados son presentados a la comunidad académica para ayudar en la disminución de la deserción estudiantil.
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Referencias
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[18] A. Tekin, “Early Prediction of Students’ Grade Point Averages at Graduation: A Data Mining Approach,” Eurasian Journal of Educational Research, vol. 54, pp. 207–226, 2014. [En línea]. Disponible en: https://eric.ed.gov/?id=EJ1057301
[19] Q. A. Al-Radaideh, E. M. Al-Shawakfa, and M. I. Al-Najjar, “Mining student data using decision trees,” in International Arab Conference on Information Technology (ACIT’2006), Yarmouk University, Jordan, 2006, pp. 1–5.
[20] L. Jing, “Data mining and its applications in higher education,” New Directions for Institutional Research, vol. 2002, no. 113, pp. 17–36, 2002. doi: 10.1002/ir.35
[21] C. Romero and S. Ventura, “Educational data mining: A survey from 1995 to 2005,” Expert Systems with Applications, vol. 33, no. 1, pp. 135 – 146, 2007. [En línea]. Disponible en: http://www. sciencedirect.com/science/article/pii/S0957417406001266
[22] H. Serge, “Estimating student retention and degreeâcompletion time: Decision trees and neural networks vis-á-vis regression,” New Directions for Institutional Research, vol. 2006, no. 131, pp. 17–33, 2006. doi: 10.1002/ir.185
[2] J. J. Brunner, J. Gacel-Avilà, M. Laverde, J. Puukka, J. Rubio, S. Schwartzman, Ó. Valiente et al., Higher Education in Regional and City Development: Antioquia, Colombia 2012. OECD, 2012. [En línea]. Disponible en: https://www.oecd-ilibrary.org/ content/publication/9789264179028-en
[3] S. d. O. Durso, J. V. A. d. Cunha, “Determinant Factors for Undergraduate Student’s Dropout in an Accounting Studies Department of a Brazilian Public University,” Educação em Revista, vol. 34, 00 2018. [En línea]. Disponible en: http://www.scielo.br/scielo.php?script=sci_arttext&pid= S0102-46982018000100142&nrm=iso
[4] T. Mishra, D. Kumar, and S. Gupta, “Mining students’ data for prediction performance,” in Fourth International Conference on Advanced Computing & Communication Technologies, ser. ACCT ’14. Washington, DC, USA: IEEE Computer Society, 2014, pp. 255–262. doi: 10.1109/ ACCT.2014.105
[5] C. Márquez-Vera, A. Cano, C. Romero, A. Y. M. Noaman, H. Mousa Fardoun, and S. Ventura, “Early dropout prediction using data mining: a case study with high school students,” Expert Systems, vol. 33, no. 1, pp. 107–124, feb 2016. doi: 10.1111/exsy.12135
[6] A. Seidman, “Retention revisited: R= e, id+ e & in, iv.” College and University, vol. 71, no. 4, pp. 18–20, 1996.
[7] “Spadies - sistema de prevención y análisis a la deserción en las instituciones de educación superior,” Ministerios de Educación, [En línea]. Disponible en: www.mineducacion.gov.co/ 1621/article-156292.html
[8] V. Tinto, “Dropout from higher education: A theoretical synthesis of recent research,” Review of educational research, vol. 45, no. 1, pp. 89–125, 1975.
[9] B. K. Bhardwaj and S. Pal, “Data mining: A prediction for performance improvement using classification,” (IJCSIS) International Journal of Computer Science and Information Security, vol. 9, no. 4, 2011.
[10] B. K. Baradwaj and S. Pal, “Mining educational data to analyze students’ performance,” International Journal of Advanced Computer Science and Applications, vol. 2, no. 6, 2011.
[11] Z. Kovacic, “Early prediction of student success: Mining students’ enrolment data,” in Informing Science & IT Education Conference (InSITE), vol. 10, 2010, pp. 647–665. doi: 10.28945/1281
[12] T. Devasia, Vinushree T P, and V. Hegde, “Prediction of students performance using Educational Data Mining,” in 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) IEEE, mar 2016, pp. 91–95. [En línea]. Disponible en: http://ieeexplore.ieee.org/document/7684167/
[13] P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth, “Crisp-dm 1.0,” CRISP-DM Consortium, vol. 76, 2000. [En línea]. Disponible en: ftp://ftp.software.ibm.com/software/analytics/spss/support/ Modeler/Documentation/14/UserManual/CRISP-DM.pdf
[14] R. Wirth, “Crisp-dm: Towards a standard process model for data mining,” in Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, 2000, pp. 29–39.
[15] L. Aulck, N. Velagapudi, J. Blumenstock, and J. West, “Predicting Student Dropout in Higher Education,” in 2016 ICML Workshop on Data4Good: Machine Learning in Social Good Applications, 2016, pp. 16–20. [En línea]. Disponible en: http://arxiv.org/abs/1606.06364
[16] G. W. Dekker, M. Pechenizkiy, and J. M. Vleeshouwers, “Predicting Students Drop Out: A Case Study,” in International Conference on Educational Data Mining (EDM), 2009, pp. 41–50. [En línea]. Disponible en: http://www.educationaldatamining. org/EDM2009/uploads/proceedings/dekker.pdf
[17] E. Yukselturk, S. Ozekes, and Y. K. Türel, “Predicting dropout student: an application of data mining methods in an online education program,” European Journal of Open, Distance and Elearning, vol. 17, no. 1, pp. 118–133, 2014.
[18] A. Tekin, “Early Prediction of Students’ Grade Point Averages at Graduation: A Data Mining Approach,” Eurasian Journal of Educational Research, vol. 54, pp. 207–226, 2014. [En línea]. Disponible en: https://eric.ed.gov/?id=EJ1057301
[19] Q. A. Al-Radaideh, E. M. Al-Shawakfa, and M. I. Al-Najjar, “Mining student data using decision trees,” in International Arab Conference on Information Technology (ACIT’2006), Yarmouk University, Jordan, 2006, pp. 1–5.
[20] L. Jing, “Data mining and its applications in higher education,” New Directions for Institutional Research, vol. 2002, no. 113, pp. 17–36, 2002. doi: 10.1002/ir.35
[21] C. Romero and S. Ventura, “Educational data mining: A survey from 1995 to 2005,” Expert Systems with Applications, vol. 33, no. 1, pp. 135 – 146, 2007. [En línea]. Disponible en: http://www. sciencedirect.com/science/article/pii/S0957417406001266
[22] H. Serge, “Estimating student retention and degreeâcompletion time: Decision trees and neural networks vis-á-vis regression,” New Directions for Institutional Research, vol. 2006, no. 131, pp. 17–33, 2006. doi: 10.1002/ir.185