The Practical Dilemma and Path Optimization of Big Data Integration into Ideological and Political Teaching in Private Colleges and Universities

Abstract

The promotion and application of big data have promoted the reform of quality classes in Colleges and universities, enriched the teaching content, and changed the traditional education mode. However, at present, due to the difficulties in funding, understanding teachers, and system construction, the integration of data into teaching often presents an embarrassing situation of separation between form and content, the separation between theory and practice, and the distance between teaching results and teaching objectives. For this reason, many private colleges and universities should make full use of data technology to change the quality of education mode according to the new characteristics of college students in the data environment, use the network information carrier to strengthen the interaction and exchange with college students, do a good job in guiding the ideological dynamics, actively promote positive capacity, and improve the networking level of quality education. At the same time, in order to change the current situation of incomplete integration and break through the dilemma, private colleges and universities should change their ideas and strengthen their attention to the practical ideological and political teaching. In view of the above problems, this study analyzes the internal logic of the integration of private colleges and big data, combines the actual difficulties, and uses LSTM neural network to put forward reasonable optimization strategies and suggestions, aiming to expand and cultivate high-quality practical teaching teams of Ideological and political courses, so as to improve the system and establish and improve the relevant teaching system and mechanism.