scholarly journals Prediction of College Students’ Psychological Crisis Based on Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jingjing Liu ◽  
Guangyuan Shi ◽  
Jing Zhou ◽  
Qiumei Yao
Keyword(s):  
College Students ◽  
Data Mining ◽  
Early Warning ◽  
Management System ◽  
Risk Groups ◽  
Public Opinions ◽  
Student Management ◽  
Psychological Management ◽  
Current Psychological ◽  
Big Data Technology

The development of a college students’ psychological management system has become an essential indicator to monitor and prevent the psychological crisis. University student management databases accumulate massive data, but the conventional data processing tasks are restricted to simple statistical analysis, storage, and query management. This paper discusses the application of big data technology for the current psychological management system by investigating psychological crisis screening indicators. Data mining techniques are used to realize the dynamic management of psychological early warning data, real-time monitoring of high-risk groups’ psychology, and improvement of the accuracy and effectiveness of early identification and warning of students’ psychological crisis. Based on a combination of qualitative and quantitative analysis, we conduct a series of studies on three typical types of network public opinions, i.e., Internet rumors, online public opinions of college students, and emergent public health incidents in terms of the transmission mechanism, early warning, and decision-making mechanism, as well as the evolution mechanism of the network public opinion.

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.

Improved Data Mining Algorithms Based on an Early Warning System of College Students

2013 ◽  
Vol 8 (9) ◽  
Author(s):  
Lijuan Zhou ◽  
Yuyan Chen ◽  
Shuang Li
Keyword(s):  
College Students ◽  
Data Mining ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Data Mining Algorithms ◽  
Mining Algorithms

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.

scholarly journals Design of Early Warning Platform for College Students’ Achievement Based on Data Mining

2021 ◽  
Vol 2066 (1) ◽  
pp. 012078
Author(s):  
Yuanfu Mao ◽  
Zhifeng Yu
Keyword(s):  
College Students ◽  
Data Mining ◽  
Performance Management ◽  
Student Performance ◽  
Early Warning ◽  
Colleges And Universities ◽  
University Management ◽  
Accuracy Rate ◽  
Mining Technology ◽  
Class B

Abstract With the acceleration of the application of information technology in Colleges and universities in China, the efficiency of higher education is constantly improving. The focus of teaching management in Colleges and universities is to continuously improve the teaching level of colleges and universities, and the key is to strengthen the management of students’ performance. Performance warning is a form of student performance management. In recent years, data mining technology is more and more mature, and its application is also very wide. Many students have applied data mining technology to university management. In this paper, we apply data mining technology to college students’ performance early warning, and use Apriori algorithm in association rules to design and build college students’ performance early warning platform, and select two classes of students as the research object to verify. In this study, we choose the English course scores of two classes as the test data, and define the performance warning, which is based on the score below 60. The results show that six students in class a will be subject to performance warning, while seven students in class B will be subject to performance warning. In addition, the performance early warning platform designed by this method, the early warning accuracy rate is as high as 92.85%, the accuracy rate is high, has certain application advantages.

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