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Título : Student Desertion Prediction Using Kernel Relevance Analysis
Autor : Fernández, Jorge
Rojas, Angelica
Daza, Genaro
Gómez, Diana
Álvarez, Andrés
Orozco, Álvaro
Fecha de publicación : 2018
Editorial : Springer
Citación : 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
Resumen : 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.
URI : https://repositorio.uci.cu/jspui/handle/123456789/9469
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