A Method for Evaluating the Teaching Quality of Ideological and Political Courses using Machine Learning Networks

Assessing ideological and political course effectiveness is intricate with content and different student interpretations. Traditional evaluation techniques are restricted by human bias and are incapable of measuring subtle learning outcomes. This research uses a Backpropagation (BP) neural network to simulate the non-linear relationships among evaluating criteria like student participation, comprehension, exam performance, and feedback. The neural network is trained using past data to make forecasting of teaching quality scores that match judgments. The input layers manage multiple predictors, and the hidden layers display complex dependencies. Supervised learning with backpropagation for optimization is used for training. Accuracy, precision, and F1 score are used to evaluate the model. It has a 97.7% of accuracy better than the conventional linear models. Such a system facilitates more objective, measurable, and flexible teaching appraisal, which aids in ensuring continuous quality improvement in ideological and political education.