The Practical Effects of Political Education Courses Based on Artificial Neural Network Model
Xiaojuan Chen
Digital Signal Processing and Artificial Intelligence for Automatic Learning, Vol. 3, No. 3, 2024.
DOI: https://doi.org/10.6025/dspaial/2024/3/3/104-109
Link: https://www.dline.info/dspai/fulltext/v3n3/dspaiv3n3_4.pdf
Abstract:
Constructing an evaluation system for the practical effects of political education
courses is beneficial for assessing existing educational work and serves as a
â€œcompassâ€ for improving corresponding work. Therefore, this study explores the
practical effects of college educational courses based on a computer artificial neural
network model. The article first establishes a â€œthree-dimensional index systemâ€ for
evaluating educational courses in colleges based on the characteristics of artificial
neural networks. Then, it introduces the computer artificial backpropagation (BP)
neural network evaluation method and obtains strong empirical support through
simulation experiments. Finally, a feasible â€œcollege artificial neural network evaluation
modelâ€ is constructed. The simulation experiments show that the modelâ€™s actual
output is very close to the expected results, and satisfactory results are achieved
when testing the model with sample data. Using the BP neural network model
effectively eliminates traditional evaluation methods, significantly increasing the
implementation efficiency of the evaluation. The improvement of the BP neural
network enhances its flexibility and performs excellently in training. The modelâ€™s
expected performance in simulation experiments is highly consistent with actual
performance, and it also achieves satisfactory results when measuring its
performance using test data. In college education, it is a widely used evaluation
method.