The quality evaluation system of ideological and political classroom teaching in universities based on GA‐BP algorithm

Summary The advancement of teaching quality is an indispensable section of the reform and growth of universities, and ideological and political education has critical impact on ideological education. The quality of classroom education can provide data support for efficient development, and has cruci...

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Vydáno v:Concurrency and computation Ročník 36; číslo 23
Hlavní autor: Jing, Guohua
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 25.10.2024
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Témata:
Adaptive algorithms
adaptive genetic algorithm
Classrooms
Education
Education reform
entropy method
Error analysis
GA‐BP neural network
Genetic algorithms
Ideology
indicator system
Neural networks
Performance assessment
Performance evaluation
Quality assessment
teaching quality
ISSN:1532-0626, 1532-0634
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Shrnutí:Summary The advancement of teaching quality is an indispensable section of the reform and growth of universities, and ideological and political education has critical impact on ideological education. The quality of classroom education can provide data support for efficient development, and has crucial influence on achieving scientific, reasonable, and accurate evaluation of ideological and political teaching performance. Thus, a performance assessment system for ideological and political education in universities with genetic algorithm optimized neural network algorithm is put forward. First, based on existing teaching evaluation indicators and combined with actual situations, a targeted teaching quality evaluation system is proposed. Then, based on BP, an adaptive genetic algorithm is proposed for improvement, and the output results are improved using entropy method. The results indicated that the proposed model could reach its optimal state after 81 iterations in this study. In the fitting test, it reached 0.971. In actual testing, the average error was only 2.68, which was much bigger than the other three algorithms. Its accuracy was 2%–3.2% higher than that of the best existing algorithms. These results indicated that the method put forward in this study had better practical significance, lower error, more accurate evaluation results, and offered scientific data support for the education reform work of universities, which can better accelerate the development and construction of universities.
Bibliografie:ObjectType-Article-1
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.8228