Effectiveness Evaluation of Ideological and Political Education Based on Improved Deep Neural Networks

Accurately measuring student outcomes is essential for ensuring the quality and credibility of Ideological and Political (IAP) education in higher institutions. This research centres on forecasting student assessment results to gain deeper insights into learning progress, highlight potential problem areas, and guide the development of focused support strategies to improve academic performance. This article builds a Deep Learning (DL) assisted evaluation model to assess the teaching quality of ideological and political learning in educational environment. Thus, this research proposes the Improved Deep Neural Network (IDNN) for the effectiveness evaluation of IAP education. The experimental results demonstrates that the proposed IDNN approach reaches a greater accuracy 99.02 %, precision of 99.12 % and recall of 99.07 % respectively as compared to other DL based quality evaluation approaches. The findings illustrates that novel method attains an effective as well as precise evaluation, providing a greater insight for enhancing IAP quality as well as providing to education environments.