Supervised data mining techniques for the analysis of the academic performance of the students of the Universidad Nacional amazónica de Madre de Dios
DOI:
https://doi.org/10.55873/racba.v1i2.190Keywords:
CRISP, data mining, Support Vector Machines, association rules, predictive techniquesAbstract
The purpose of this study was to identify the supervised data mining technique with the best performance for the analysis of the academic performance of university students. The non-experimental cross-sectional design was chosen. The initial data set for the experiments consisted of 17,771 records of academic processes, after preprocessing a final data set of 17,035 records were obtained. The data mining methodology used was Knowledge Discovery in Databases (KDD). Binary logistic regression techniques, Classification and Regression Trees (CART), C4.5, Support Vector Machines, and K-Nearest Neighbors were used. The results show that the C5.0 algorithm obtains an accuracy of 93%, AUC of 0.9797 and a training time of 0.87 seconds, is the most efficient in relation to the other algorithms compared.
References
Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177–194. https://doi.org/10.1016/J.COMPEDU.2017.05.007
Enke, D., & Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4), 927–940. https://doi.org/10.1016/J.ESWA.2005.06.024
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). Knowledge Discovery and Data Mining: Towards a Unifying Framework.
Garbanzo, G. M. (2007). Factores asociados al rendimiento académico en estudiantes universitarios. Revista Mexicana de Orientación Educativa, 31(1), 1–25. https://doi.org/10.31206/rmdo072018
Gironés, J., Casas, J., Minguillón, J., & Caihuelas, R. (2017). Minería de datos: modelos y algoritmos (UOC (ed.); Primera).
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques. Elsevier Inc. https://doi.org/10.1016/C2009-0-61819-5
Khor, E. T. (2022). A data mining approach using machine learning algorithms for early detection of low-performing students. International Journal of Information and Learning Technology, 39(2), 122–132. https://doi.org/10.1108/IJILT-09-2021-0144
Landis, J. R., & Koch, G. G. (1977). An Application of Hierarchical Kappa-type Statistics in the Assessment of Majority Agreement among Multiple Observers. Biometrics, 33(2), 363. https://doi.org/10.2307/2529786
Lemay, D. J., Baek, C., & Doleck, T. (2021). Comparison of learning analytics and educational data mining: A topic modeling approach. Computers and Education: Artificial Intelligence, 2, 100016. https://doi.org/10.1016/J.CAEAI.2021.100016
Nabil, A., Seyam, M., & Elfetouh, A. A. (2022). Predicting students’ academic performance using machine learning techniques: a literature review. International Journal of Business Intelligence and Data Mining, 20(4), 456. https://doi.org/10.1504/IJBIDM.2022.123214
Norabuena, R. (2011). UNIVERSIDAD NACIONAL MAYOR DE SAN MARCOS. Universidad Nacional Mayor de San Marcos.
Rosado Gómez, A. A., & Verjel Ibáñez, A. (2015). Minería de datos aplicada a la demanda del transporte aéreo en Ocaña, Norte de Santander. Tecnura, 19(45), 101–113. https://doi.org/10.14483/UDISTRITAL.JOUR.TECNURA.2015.3.A08
Trakunphutthirak, R., & Lee, V. C. S. (2021). Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data: Https://Doi.Org/10.1177/07356331211048777, 60(3), 742–776. https://doi.org/10.1177/07356331211048777
Valcárcel, V. (2004). DATA MINING Y EL DESCUBRIMIENTO DEL CONOCIMIENTO (1)-9993 (electrónico). Revista de La Facultad de Ingeniería Industrial, 7(2), 1810.
Yağcı, M. (2022). Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1). https://doi.org/10.1186/S40561-022-00192-Z
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Copyright (c) 2022 Nelly Ulloa-Gallardo, Luis Alberto Holgado-Apaza, Yban Vílchez-Navarro, Diego Raul Quispe-Barra
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