Quality Evaluation of Ideological and Political Education for College Students Based on Artificial Neural Networks with Hyperparameter Tuning

Ideological and political education has become a significant focus of teaching reform in colleges and universities. However, the development of efficient teaching evaluation approaches has yet to keep pace. Moreover, most college students are still in a development stage in large network environment. Thus, it is important to consider evaluation analysis for ideological education and psychological training. Through considering this, this research proposes the Hyperparameter Tuning based Artificial Neural Network (HTANN) for the quality evaluation of Ideological and Political Education for College students. This research estimates an impact of teaching and ideological and political education quality on students' satisfactions through implementing a set of scales, acquiring approximately 3800 questionnaires. The proposed approach which combines teaching and evaluation for ideological and political education, allowing online information interaction. The aim is to improve teaching quality and make sure a more coordinated online learning experience. The experimental results demonstrates that the proposed HT-ANN approach attains the better standard total effect of 0.832 on Ideological and Political education.