JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Evaluation of Predictive Models for Early Identification of Dropout Students |
| 영문제목(English Title) |
Evaluation of Predictive Models for Early Identification of Dropout Students |
| 저자(Author) |
JongHyuk Lee
Mihye Kim
Daehak Kim
Joon-Min Gil
|
| 원문수록처(Citation) |
VOL 17 NO. 03 PP. 0630 ~ 0644 (2021. 06) |
한글내용
(Korean Abstract) |
|
영문내용
(English 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. |
| 키워드(Keyword) |
Educational Data Analysis
Student Dropout
Predictive Model
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| 파일첨부 |
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