A parent education anxiety prediction model using machine learning

Abstract

Education-related search produces a large number of time series data. Effective data mining is of great significance to education decision. A new model was applicated in this paper to predict the parental education anxiety by comparing seven machine learning methods. To ensure the reliability and performance, data were collected from State Statistics Bureau. Results demonstrated that the PCA-LSTM model successfully captures the uncertainty of related policies and events that cause parent education anxiety and has better performance than other results. Our model provides early demand for dynamic real-time analysis and accurate prediction.