Evaluation of Predictive Models for Early Identificationof Dropout Students


JongHyuk Lee, Mihye Kim, Daehak Kim, Joon-Min Gil, Journal of Information Processing Systems Vol. 17, No. 3, pp. 630-644, Jun. 2021  

10.3745/JIPS.04.0218
Keywords: Educational Data Analysis, Student Dropout, Predictive model
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Abstract

Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.


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Cite this article
[APA Style]
JongHyuk Lee, Mihye Kim, Daehak Kim, & Joon-Min Gil (2021). Evaluation of Predictive Models for Early Identificationof Dropout Students. Journal of Information Processing Systems, 17(3), 630-644. DOI: 10.3745/JIPS.04.0218.

[IEEE Style]
J. Lee, M. Kim, D. Kim and J. Gil, "Evaluation of Predictive Models for Early Identificationof Dropout Students," Journal of Information Processing Systems, vol. 17, no. 3, pp. 630-644, 2021. DOI: 10.3745/JIPS.04.0218.

[ACM Style]
JongHyuk Lee, Mihye Kim, Daehak Kim, and Joon-Min Gil. 2021. Evaluation of Predictive Models for Early Identificationof Dropout Students. Journal of Information Processing Systems, 17, 3, (2021), 630-644. DOI: 10.3745/JIPS.04.0218.