Evaluation of Traditional Culture Teaching Efficiency by Course Ideological and Political Integration Lightweight Deep Learning

Abstract

With the development of society, China pays more and more attention to cultural education. The teaching method of introducing ideological and political content into cultural teaching plays an important role in improving the overall teaching quality. However, the traditional methods used to evaluate the quality of culture teaching, curriculum ideological, and political teaching have some problems, such as strong subjectivity and unrepresentative results. Firstly, this work analyzes the connotation of curriculum thought and politics. Secondly, a teaching quality evaluation model based on an improved lightweight convolutional neural network (CNN) is proposed, which mainly judges the students' recognition of teachers' content and teaching methods by identifying the students' expressions in the classroom. Finally, the students of a senior high school in Shanghai are selected as the survey object, and the current situation of ideological and political education (IPE) in the school curriculum is preliminarily understood by issuing a questionnaire; experiments are designed to test the performance of the model. The results show that most of the students in the school do not understand the connotation of IPE, and the teachers cannot accurately and deeply teach the relevant ideological and political knowledge to the students. About 73% and 82% of students prefer that teachers can mention life experience and social skills in class. More than 50% of the students are more willing to accept the course ideological and political activities in the form of lectures and competitions. This indirectly shows that the school lacks the above contents in the current course ideological and political teaching, the teaching method is relatively single, and cannot fully mobilize the enthusiasm of students. Further improvement is needed for these problems in the follow-up. The accuracy of expression recognition of this model is more than 2.9% higher than other algorithms, and the improvement effect of the model is remarkable. To sum up, this work fully understands the current teaching situation of the surveyed schools through questionnaire survey, and puts forward corresponding improvement suggestions. The effectiveness of this model is verified by designing experiments, which proves that it is suitable for the research of teaching quality evaluation.