Using Deep Neural Networks to Evaluate Ideological and Political Learning in Educational Environments

The combination of ideological and political learning in educational settings is critical in molding people’s values, ethics, and societal responsibilities. This approach encourages the formation of knowledgeable, socially conscious, and civically involved students by including political and ideological ideas in the educational curriculum. This research constructs a deep learning (DL) based evaluation model to assess the teaching quality of ideological and political learning in the educational environment. The research proposed a novel intelligent African buffalo- optimized resilient deep neural network (IABO-RDNN) to evaluate the education quality of ideological and political education (IPE). The suggested approach is integrated classroom interactions, student feedback, and digital learning behaviors to consider a wide-ranging analysis of learning outcomes. The data was preprocessed using noise removal, irrelevant entry removal, and feature standardization to guarantee consistency across datasets. IABO optimizes model parameters, ensuring a well-organized, robust assessment structure for the IPE quality, capturing diverse aspects. The resilient deep neural network (RDNN) is engaged to ensure teaching efficiency and identify areas for improvement in IPE. A comparative analysis of the results using DL algorithms and conventional algorithms highlights the superior accuracy (98.9%), precision (98%), recall (98.5%), and adaptability of the proposed model. The conclusion shows that the inventive approach achieves efficient and reliable evaluation, offering valuable insights for improving IPE quality and contributing to education environments.