Abstract
This paper presents a kernel-based relevance analysis to support student desertion prediction. Our approach, termed KRA-SD, is twofold: (i) A feature ranking based on centered kernel alignment to match demographic, academic, and biopsychosocial measures with the output labels (deserter/not deserter), and (ii) classification stage based on k-nearest neighbors and support vector machines to predict the desertion. For concrete testing, the student desertion database of the Universidad Tecnologica de Pereira is employed to assess the KRA-SD under a training, validation, and testing scheme. Attained results show that the proposed approach can recognize the main features related to the student desertion achieving an 85.64% of accuracy. Therefore, the proposed system aims to serve as a handy tool for planning strategies to prevent students from leaving the university without finishing their studies.
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Alvarez-Meza, A.M., Orozco-Gutierrez, A., Castellanos-Dominguez, G.: Kernel-based relevance analysis with enhanced interpretability for detection of brain activity patterns. Front. Neurosci. 11, 550 (2017)
Argote, I., Jimenez, R.: Detección de patrones de deserción en los programas de pregrado de la universidad mariana de san juan de pasto, aplicando el proceso de descubrimiento de conocimiento sobre base de datos (kdd) y su implementación en modelos matemáticos de predicción. In: Congresos CLABES (2016)
Brockmeier, A.J., et al.: Information-theoretic metric learning: 2-D linear projections of neural data for visualization. In: EMBC, pp. 5586–5589. IEEE (2013)
Gartner Isaza, M.L., Gallego Giraldo, C.: La deserción estudiantil en la universidad de caldas: sus características, factores determinantes y el impacto de las estrategias institucionales de prevención. In: Conferência latino-americana sobre o abandono, V, Talca, Peru (2015)
Peralta, B., Poblete, T., Caro, L.: Automatic feature selection for desertion and graduation prediction: a chilean case. In: SCCC, pp. 1–8. IEEE (2016)
Pereira, R.T., Romero, A.C., Toledo, J.J.: Aplicación de la minería de datos en la extracción de perfiles de deserción estudiantil [application of data mining in extracting student dropout profiles]. Ventana Informática (28), 31–47 (2013)
Spositto, O., Etcheverry, M., Ryckeboer, H., Bossero, J.: Aplicación de técnicas de minería de datos para la evaluación del rendimiento académico y la deserción estudiantil. In: Novena Conferencia Iberoamericana en Sistemas, Cibernética e Informática, CISCI, vol. 29, p. 6 (2010)
Torres, C.Z., Ramos, C.A., Moraga, J.L.: Estudio de variables que influyen en la deserción de estudiantes universitarios de primer año, mediante minería de datos. Ciencia Amazónica:(Iquitos) 6(1), 73–84 (2016)
Wang, Y.: Tracking neural modulation depth by dual sequential monte carlo estimation on point processes for brain-machine interfaces. IEEE Trans. Biomed. Eng. 63(8), 1728–1741 (2016)
Acknowledgments
Under grants provided by the Colciencias project: “ATTENDO” - code: FP44842-424-2017. Also, we would like to thank the support of the UTP’s Vicerrectoria de Responsabilidad Social y Bienestar Universitario.
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Fernández, J., Rojas, A., Daza, G., Gómez, D., Álvarez, A., Orozco, Á. (2018). Student Desertion Prediction Using Kernel Relevance Analysis. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science(), vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_30
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DOI: https://doi.org/10.1007/978-3-030-01132-1_30
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