Evaluation of Predictive Models for Early Identification of Dropout Students
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Educational data analysis is attracting increasing attention with the rise of the big data industry. The amountsand types of learning data available are increasing steadily, and the information technology required to analyzethese 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 dataand generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayesmethod, and a multilayer perceptron. The multilayer perceptron model using independent variables selected viathe variance analysis showed better performance than the other models. In addition, we experimentally foundthat not only grades but also extracurricular activities were important in terms of preventing student dropout.
Abstract
Educational data analysis is attracting increasing attention with the rise of the big data industry. The amountsand types of learning data available are increasing steadily, and the information technology required to analyzethese 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 dataand generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayesmethod, and a multilayer perceptron. The multilayer perceptron model using independent variables selected viathe variance analysis showed better performance than the other models. In addition, we experimentally foundthat not only grades but also extracurricular activities were important in terms of preventing student dropout.
참고문헌 (22)
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