Research on Teaching Strategies of Ideological and Political Courses in Colleges and Universities from the Perspective of Educational Psychology Based on Deep Learning

This study aims to assess the impact of deep learning (DL) and behavior recognition on long-term student engagement and learning outcomes through a mixed-methods approach. Quantitative metrics such as student retention rates, participation in discussions, and performance data will be evaluated in conjunction with qualitative insights gathered from student interviews and feedback. This comparison will involve analyzing the data both prior to and following the integration of deep learning (DL) algorithms, we can determine their effectiveness in enhancing engagement and learning outcomes. By utilizing models like the neural collaborative filtering algorithm, the study examines typical student behaviors—such as sitting, listening, writing, sleeping, raising hands, and using mobile phones—as indicators of classroom engagement. It proposes an integrated teaching approach that combines principles of educational psychology with deep learning (DL) techniques and online teaching methods, including flipped classrooms and online platforms offering large-scale courses (MOOCs). This integration facilitates a more personalized learning experience and aligns with the modern educational landscape's shift towards information technology and intelligent solutions. The findings suggest that employing these innovative strategies can significantly improve students' motivation and engagement, ultimately fostering a more effective ideological and political education framework, and the paper concludes with recommendations for future research directions to further develop these approaches across various educational contexts.