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The Architecture of College Psychological Teaching Management System Based on Data Mining Technology.
- Source :
- Security & Communication Networks; 11/17/2022, p1-12, 12p
- Publication Year :
- 2022
-
Abstract
- In order to meet the requirements of the development of educational informatization, combined with data mining technology, this article studies the architecture of the college psychological teaching management system. To experience the benefits brought by the Internet age, in order to further improve performance and meet the needs of the Internet information age, many colleges and universities have begun to strengthen the development of teaching management. Based on this background, this article proposes a standardized mode of mental health management in colleges and universities based on information technology. With the help of data warehouse and data mining technology, the ID3 algorithm, organizational rules, and other algorithms are used to develop a psychology subject evaluation system. Based on the improvement of grades and the behavioral characteristics of college students, this article formulates cross-platform operation rules, determines the B/S type as the normal state, and solves the best problem through comparison and selection. Then, a more specific system architecture design is carried out. The architecture of the software system selects the classic three-tier structure, which makes the client independent of the use platform and meets the requirements of user diversity and system portability. The design of functional modules such as the system fully meets the requirements of the ideological education management system for college students in the above analysis, with reasonable structure and clear logic. Finally, the advantages of the designed system architecture are summarized, including clarifying the grading idea of ideological education and training. It expands the transplantation ability of the system in a cross-platform way. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19390114
- Database :
- Complementary Index
- Journal :
- Security & Communication Networks
- Publication Type :
- Academic Journal
- Accession number :
- 160295818
- Full Text :
- https://doi.org/10.1155/2022/7968756