Analysis of interaction mode of ideological and political flipped classroom based on deep learning algorithm and student feedback data

Abstract

By abstracting the input data using deep neural networks, we can obtain the distributed representation characteristics of related data. Compared with shallow neural network, deep neural network has stronger data representation ability. Deep learning promotes the deep research and practical application of machine learning. The continuous progress and development of society not only promote the new curriculum reform, but also put forward higher requirements for all kinds of talents. At this stage, students should learn how to maintain lifelong learning, and maintain the attitude of "learning is never too old".Therefore, the concept of deep learning in the educational world was born. Ideology and politics is a comprehensive subject, which requires students to solve problems independently. The content of ideology and politics is complex and diverse, and theory is closely combined with practice, so that students can nd and analyze problems independently in the process of practice. Deep learning is manifested in higher-order thinking, that is, students should be self-conscious in learning, learn to closely discuss the problems in learning with people around them, and have the ability to criticize, question, integrate construction, transfer and apply in the learning process. Based on the deep learning algorithm, this paper uses relevant theories and educational practices for reference to analyze the interaction mode of ideological and political ipped classroom and student feedback data.