Research on Intelligent Decision Support System for Student Management and Mental Health Intervention in Higher Education Institutions

Abstract

The intelligent management system of colleges and universities is an important display of the informationization construction of colleges and universities. Aiming at the life trajectory and life information of students in colleges and universities, this paper combines data mining and other technologies, adopts Python language and MySQL database technology, and improves on the Flask framework to develop an intelligent decision support system for student management and mental health intervention. The study takes student achievement abnormality detection and mental health intervention as an example, uses the improved K-Means algorithm and Apriori algorithm to mine and analyze the relevant data, and discusses the specific application of the intelligent decision support system. The sample students can be divided into three different types of academic performance: excellent, ordinary, and difficult. Precision teaching can be implemented according to the different types of students. Only 3% of the students had more obvious and serious psychological problems, while 80% of the students had a relatively healthy psychological state. Moreover, the correlation rules between student information and mental health status are analyzed, which can aid in the implementation of mental health interventions. The intelligent system of student management can provide the most direct and effective evidence for academic administrators in making decisions on teaching plans, thus making student education more targeted, personalized, and predictive.

Keywords

• data mining
• k-means
• apriori
• association rules
• intelligent decision support system
• student management
• 08a02