Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Evaluation of Predictive Models for Early Identification of Dropout Students

  • Lee, JongHyuk (Dept. of Artificial Intelligence and Big Data Engineering, Daegu Catholic University) ;
  • Kim, Mihye (School of Computer Software, Daegu Catholic University) ;
  • Kim, Daehak (Dept. of Artificial Intelligence and Big Data Engineering, Daegu Catholic University) ;
  • Gil, Joon-Min (School of Computer Software, Daegu Catholic University)
  • Received : 2018.10.01
  • Accepted : 2020.12.22
  • Published : 2021.06.30
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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.

Keywords

Acknowledgement

This work was supported by research grants from Daegu Catholic University in 2017.

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