A parent education anxiety prediction model using machine learning

Jialin Du; Guozhu Jia

doi:10.1117/12.2659683

30 November 2022 A parent education anxiety prediction model using machine learning

Jialin Du, Guozhu Jia

Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124561I (2022) https://doi.org/10.1117/12.2659683
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China

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.

Citation Download Citation

Jialin Du and Guozhu Jia "A parent education anxiety prediction model using machine learning", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124561I (30 November 2022); https://doi.org/10.1117/12.2659683

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