Adaptive Multi-Modal CNN Framework for Intelligent Evaluation of Ideological and Political Classroom Teaching in Higher Education

Evaluation of ideological and political course teaching quality in colleges and universities is of great importance to improve educational achievements and comprehensive development of students. The traditional evaluation approaches are largely based on subjective judgments, manual observation, and scarce data sources, which may cause inconsistencies and insufficient timely feedback. Current automated systems usually center on single-modal data, like video or audio analysis, and end up lacking a proper understanding of intricate classroom dynamics. To overcome these constraints, this study suggests an Adaptive Multi-Modal CNN-Attention Hybrid Model for intelligent assessment of ideological and political classroom instruction. The model combines visual, audio, and metadata inputs to reflect a detailed representation of teaching quality. Through the application of convolutional neural networks for feature learning and attention mechanisms for selection of key classroom interactions, the model delivers precise real-time evaluations with enhanced interpretability. Experimental results indicate better performance over conventional CNN-based and single-modal methods with increased accuracy and robustness in measuring teaching quality. Comparative analysis validates the efficacy of the proposed model in multimodal data fusion handling as well as extraction of key features related to expert scores. This research presents a new framework for smart classroom assessment, providing real-world implications for teachers and institutions seeking to enhance ideological and political education quality through state-of-the-art AI-based methods.