Scholar Publishing Group The best way to conference proceedings by Scholar Publishing Group

Scholar Publishing Group

Risk Assessment and Early Warning Methods of First-class Discipline Construction in Universities

Download as PDF

DOI: 10.38007/Proceedings.0001927


Lang Liu

Corresponding Author

Lang Liu


Abstract: With the implementation of the strategy of invigorating the country through science and education and the strategy of strengthening the country with talents, the importance of discipline construction in universities is increasingly obvious. This paper mainly studies the risk assessment and early warning methods of first-class discipline construction in Colleges and universities. In order to determine the weight, it is necessary to rank the indicators according to the human judgment to compare the relative importance of the indicators at the same level, and then further calculate the weight coefficient of the indicators. In this paper, the risk factors identified are described in natural language, and the risk in the list is the initial node in the risk Bayesian analysis network model. The questionnaire survey method is used to conduct a pre survey on a certain number of samples. The reliability and validity of the pre survey results are tested. The initial index set is modified and adjusted to extract specific indicators of different dimensions, thus forming an initial evaluation index system. Finally, through large-scale and large-scale questionnaire survey, the final evaluation index system is obtained through confirmatory factor analysis of various indicators by statistical analysis. The data showed that the absolute value of each index deviation was less than or equal to 1.768, which met the standard of less than 3. The results show that the posterior probability of the node is obtained by Bayesian network inference of the first-class discipline construction risk early warning system.


Keywords: First-Class Discipline Construction, Risk Assessment, Early Warning System, Element Characteristics