Application and Effectiveness Assessment of Big Data Analysis Algorithm in College Students' Mental Health Education

Abstract

This study investigates the application and effectiveness assessment of K-means clustering in college students' mental health education. Leveraging a dataset comprising demographic information, academic records, and mental health indicators from 500 college students, they conducted a comprehensive analysis to identify distinct student profiles based on similarities in their mental health profiles, demographic attributes, and academic performance. The K-means clustering algorithm was employed to partition the dataset into clusters, revealing three distinct groups of students with varying levels of academic achievement and psychological distress. Mean calculations within each cluster provided insights into the average characteristics of students, including age, GPA, and self-reported mental health scores. The findings highlight the potential of K-means clustering in informing targeted intervention strategies tailored to the unique needs of different student populations. Discussion of the results emphasizes the importance of addressing academic-related stressors, promoting early intervention and prevention efforts, and acknowledging the limitations and future directions for research in this domain. Overall, this study contributes to the growing body of research on leveraging big data analytics to support college students' mental health and well-being.