머신러닝을 활용한 유치원 정보공시 만족도 영향 요인 분석

Abstract

The purpose of this study is to identify the use of kindergarten notifications, which can be the realization of the information disclosure system and to predict overall satisfaction using statistical analysis methods and random forest algorithms. Based on the results of the annual kindergarten notification satisfaction survey conducted by the Korea Educational Information Service, differences in user response were analyzed, and random forest was used to identify factors that affect overall satisfaction prediction among machine learning algorithms. As a result of the t-test and one-way distribution variance analysis, the difference in use status according to gender, age, residential area, and type was statistically significant. As a result of random forest model learning, Accuracy 0.85 and F1 Score 0.84 were shown. Explanable artificial intelligence such as variable importance, SHAP plot, and partial dependence charts of the learned model was used to identify variables that affect the prediction of kindergarten information disclosure. It was confirmed that the usage status variable contributed more to the satisfaction prediction than the user's background variable, and based on the results, the direction of improvement of kindergarten information disclosure and kindergarten notification was derived.