Gated Recurrent Unit Framework for Ideological and Political Teaching System in Colleges

Abstract

College ideological and political education has always been the primary content of national spiritual civilization construction. The current teaching methods are more flexible, resulting in the quality of ideological and political teaching not being reasonably assessed. To address this problem, we propose a method for assessing the quality of ideological and political teaching based on the gated recurrent unit (GRU) network and construct an automatic assessment system for ideological and political teaching. We draw on the migration learning model to improve the loss function by using the generalized intersection set over the joint loss function to compensate for the shortcoming of the small number of ideological and political teaching datasets. We use a masking algorithm to enhance the local features of teaching data sequences for different classes of ideological and political teaching quality assessment metrics. In addition, we use the minimum outer matrix algorithm to extract the sequence features of different assessment dimensions to improve the accuracy of the model for the quality assessment of ideological and political teaching. To meet the quality assessment conditions of ideological and political teaching, we compiled and produced ideological and political teaching datasets according to the teaching data coverage. The experimental results proved that our method performed best in comprehensive quality assessment accuracy in ideological and political teaching, with the assessment accuracy rate above 90%. Compared with traditional machine learning methods and deep learning methods, our method has higher accuracy and better robustness.