A Novel Architecture and Algorithm for Prediction of Students Psychological Health based on Big Data

Abstract

Psychological health of students has become a widespread social problem, while the management and assessment of college students' psychological health is still stay in passive and manual mode based on the traditional method. In this paper, we design a novel architecture for the prediction of college students' psychological health based on Multi-Source big data including Operation Support System big data, educational data and psychological health questionnaire data. Then we propose the Optimized Decision Tree using Multiple-Target Particle Swarm Optimization (DT-MTPSO) algorithm. Experiment shows that the proposed algorithm can solve the Multiple-Target problems effectively and has better performance in F1-score than traditional Decision Tree. In addition, the result of the features selection of DT-MTPSO for different targets shows the relationship between the psychological health level and behavioural characteristics of students for different evaluation indicators, providing guidance to the school managers and educational psychologist.