Quality Evaluation Method of a Mathematics Teaching Model Reform Based on an Improved Genetic Algorithm

The poor comprehensiveness of the evaluation indexes of quality evaluation methods for the traditional college mathematics teaching model reform results in low accuracy of the evaluation outcomes. In this paper, aiming at this problem, a quality evaluation method for the college mathematics teaching...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Scientific programming Jg. 2021; S. 1 - 10
1. Verfasser: Yang, Yun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 2021
John Wiley & Sons, Inc
Schlagworte:
Accuracy
Active learning
Addition & subtraction
Back propagation
Classrooms
College students
Colleges & universities
Data mining
Educational technology
Genetic algorithms
Goodness of fit
Indicators
Innovations
Mathematical analysis
Mathematics education
Multiple objective analysis
Mutation
Neural networks
Optimization
Population
Quality assessment
Simulated annealing
Social change
Teaching
Training
ISSN:1058-9244, 1875-919X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The poor comprehensiveness of the evaluation indexes of quality evaluation methods for the traditional college mathematics teaching model reform results in low accuracy of the evaluation outcomes. In this paper, aiming at this problem, a quality evaluation method for the college mathematics teaching model reform, based on the genetic algorithm, is proposed. The simulated annealing algorithm uses the weighted comprehensive objective evaluation multiobjective optimization effect that can effectively improve the accuracy of the evaluation results. In the training process, the gradient descent back-propagation training method is used to obtain new weights for the quality evaluation of college mathematics teaching mode reforms and to score various indicators and evaluate the indicators. The mean value of the outcomes is the result of mathematics teaching quality evaluation. The experimental results show that the training error of the convolutional network of the proposed method is significantly small. Based on the genetic algorithm that improves the convolutional network training process, the obtained quality evaluation outcomes are higher in accuracy, better in goodness of fitness function, and considerably lower than other state-of-the-art methods. We observed that the improved genetic algorithm has a more than 90% goodness of fit and the error is significantly lower, that is, 0.01 to 0.04, than the classical genetic algorithm.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1058-9244
1875-919X
DOI:10.1155/2021/6395349