Analysis on the Influencing Factors of College Students' Mental Health Based on Data Mining

2021 ◽  
Author(s):  
Jun Qi
Keyword(s):  
Mental Health ◽  
College Students ◽  
Data Mining ◽  
Influencing Factors

scholarly journals Evaluation of the Effect of Ideological and Political Education on Psychological Crisis Intervention for University Students Based on Data Mining Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hexia Yao ◽  
Mohd. Dahlan Hj. A. Malek
Keyword(s):  
Mental Health ◽  
College Students ◽  
Data Mining ◽  
Health Education ◽  
University Students ◽  
Civic Education ◽  
Psychological Health ◽  
Mental Health Education ◽  
Ideological Education

The mental health level of university students not only directly affects their own growth, but also affects the stability of the campus, which in turn affects the harmony of society and the improvement of the quality of all people. The combination of ideological education and mental health education is an important educational project in contemporary universities. To enhance the quality of psychological health education of college students can promote the overall development of students’ comprehensive quality; the two are closely integrated together, so as to successfully promote the effective combination of ideological education and psychological education, thus realizing the role of ideological education and psychological health education in promoting the physical and mental health development of contemporary college students. This paper explains the technology of data mining and the current situation of the psychological impact of Civic Education on college students and analyzes in depth the feasibility of introducing data mining technology in Civic Education to intervene in the psychological crisis of college students. The results show that the application of the technology provides a new idea for the mental health education of college students and a new way for the construction of a preventive college student mental health education model.

scholarly journals Research Progress of College Students' Learning Burnout

2021 ◽  
Vol 4 (4) ◽  
pp. 43
Author(s):  
Yongmei Hou
Keyword(s):  
Mental Health ◽  
College Students ◽  
Academic Achievement ◽  
Social Development ◽  
Influencing Factors ◽  
Psychological Problem ◽  
Research Progress ◽  
Physical And Mental Health ◽  
Measurement Tools ◽  
Definition Of

Learning burnout is a common psychological problem of college students, which seriously affects college students' academic achievement and physical and mental health, wastes educational resources, and brings various hidden dangers to talent growth and social development. Starting from the definition of the concept of learning burnout, this paper introduces the dimension composition and measurement tools of college students' learning burnout, analyzes the influencing factors of college students' learning burnout, and puts forward the corresponding research prospects in view of the shortcomings of previous research.

scholarly journals Data Analysis of College Students’ Mental Health Based on Clustering Analysis Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yichen Chu ◽  
Xiaojian Yin
Keyword(s):  
Mental Health ◽  
College Students ◽  
Data Mining ◽  
Clustering Analysis ◽  
Management System ◽  
Clustering Algorithm ◽  
Analysis Algorithm ◽  
Advantages And Disadvantages ◽  
Clustering Model ◽  
Psychological Management

Mental health is an important basic condition for college students to become adults. Educators gradually attach importance to strengthening the mental health education of college students. This paper makes a detailed analysis and research on college students’ mental health, expounds the development and application of clustering analysis algorithm, applies the distance formula and clustering criterion function commonly used in clustering analysis, and makes a specific description of some classic algorithms of clustering analysis. Based on expounding the advantages and disadvantages of fast-clustering analysis algorithm and hierarchical clustering analysis algorithm, this paper introduces the concept of the two-step clustering algorithm, discusses the algorithm flow of clustering model in detail, and gives the algorithm flow chart. The main work of this paper is to analyze the clustering algorithm of students’ mental health database formed by mental health assessment tool test, establish a data mining model, mine the database, analyze the state characteristics of different college students’ mental health, and provide corresponding solutions. In order to meet the needs of the psychological management system based on the clustering analysis method, the clustering analysis algorithm is used to cluster the data. Based on the original database, this paper establishes the methods of selecting, cleaning, and transforming the data of students’ psychological archives. Finally, it expounds on the application of data mining in students’ psychological management system and summarizes and prospects the implementation of the system.

scholarly journals Application of Association Rule Data Mining in Statistical Analysis of College Students' Mental Health

CONVERTER ◽  
2021 ◽  
pp. 716-724
Author(s):  
Hongrui Zhang
Keyword(s):  
Mental Health ◽  
College Students ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Statistical Data ◽  
Knowledge Map ◽  
Statistical Indicators ◽  
Big Data Technology

Strengthening the application of big data technology in data analysis can effectively improve the service capability and level of relevant statistics, and provide comprehensive and reliable information support for macro decision-making and trend analysis. This paper comprehensively reviews the research status of big data technology in the field of college students' mental health at home and abroad. Combining with the characteristics of college students' mental health statistical data and the weaknesses in statistical analysis, the feasibility of using knowledge mapping technology is demonstrated. On this basis, the blood relationship graph and influence analysis among the statistical indicators of college students' mental health were constructed through the knowledge map. The application of the knowledge map of college students' mental health statistical indicators in statistical data analysis, statistical indicator identification and statistical data quality management is proposed. Specifically, based on the concept of big data, we can establish a decision analysis platform for college students' mental health. Based on the big data technology, the data mining and analysis ability can be enhanced. In addition, it can change the traditional thinking of college students' mental health statistics and strengthen the construction of statistical team.

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