An empirical study on the effect of intelligent teaching platform based on machine learning and deep neural network on Ideological and political education in Colleges and Universities

This study focuse on the application effect of big data-driven intelligent teaching platform in ideological and political education of university courses,Especially pay close attention to its practical role in stimulating students' learning motivation, promoting cognitive deepening and improving learn results; learning motivation, promoting cognitive deepening and improving learning results. We evaluates the performance of the intelligent teaching platform in real teaching situations by combining experimental design and data analysis. The research takes many universities as the experimental object, and sets up the experimental group and the control group. Students in the experimental group use the intelligent teaching platform to participate in the ideological and political learning of the course, Students in the experimental group generally showed higher learning enthusiasm, especially significantly better than the control group in terms of course participation and interactive feedback.; learning behavior and learning effectiveness. The results showed that students in the experimental group generally showed higher learning enthusiasm, especial than the control group in terms of course participation and interactive feedback. The personalized recommendation and case-based teaching resources provided by the intelligent platform are regarded by many students as the key element to "stimulate interest";. In addition, in terms of understanding and applying the core concepts of ideological and politics, deeper thinking and value construction. It is worth noting that the overall test score of the students in the experimental group is higher than that of the control group, suggesting that the intelligent teaching platform has certain advantages in improving the learning effect. However, at the same time, we also observed that some students have adaptability problems in the early stage of using the platform, reflecting that there is still room for optimization between user experience and instructional design. In conclusion, this study verifies the positive role of intelligent teaching platform based on big data in ideological and political education of college courses. In the future research, on the basis of improving the adaptability of the platform, it can further explore its transferability and long-term teaching effect in the context of multiple specialties.