The challenges of ideological and political education in universities in the era of big data and its optimization path

Abstract

Abstract How to propose effective solutions for ideological and political education in colleges and universities to adapt to the development requirements of the new era is an urgent problem to be solved. In this paper, we first study the data mining algorithm Bayesian network and describe the components of the Bayesian network. Then the parameter learning of Bayesian networks is discussed in depth, and the conditional probabilities of each node in the network are calculated by counting the prior probabilities of relevant variables according to the structure graph and the corresponding training set. Then based on the structure learning, the optimal Bayesian network is selected based on the search and conditional independent testing methods. Finally, the current situation of modern ideological and political education is described in terms of students’ learning paths, learning purposes, and the problems of ideological and political education, and suggestions for optimal reform are made. Regarding learning motivation, 48% of the students said they study for school requirements and credits and 10% for establishing correct social values. Regarding problems, 67% said the teaching format is not rich, 90% said the teaching presents utilitarianism, and 83% said practical awareness is not strong. It is important to seek effective paths to enhance ideological and political education.