A neural network-based study on improving the interactive effect of teaching Marxist theory in Civics courses

The further deepening of education informatization has led to a significant shift in teaching methods as well as learning tools, and it is of research significance to explore how to use online learning platforms more effectively in non-traditional teaching environments. In this study, after pre-processing the online teaching data of Marxist theory in the Civics course, the Squeeze method is used to extract the relevant features of teaching interaction behavior in the data. Convolutional neural network is used to realize the prediction of teaching interaction behavior based on the input features, so as to realize the real-time intervention and effect enhancement strategy of teaching interaction. It is verified that the ICAM-ResNet neural network prediction model proposed in this paper has a good effect in making online teaching interactive behavior prediction, and the prediction accuracy can reach 0.816. After implementing the intervention strategy according to the prediction results, the average online learning time of students increased from 30.61 min (1 class period) to 44.54 min (16 class periods), and most of the students would actively answer the questions in the classroom, and the rate of answering correctly increased, so that the effect of teacher-student interactions was substantially improved. On the one hand, this study provides a new way of thinking for the teaching research of Marxist theory course, on the other hand, the results of the study are conducive to optimizing the teaching practice of the course and promoting the teaching interaction, so as to promote the development of the teaching of the course.