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

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yun Yang
Keyword(s):  
Genetic Algorithm ◽  
Mathematics Teaching ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Teaching Quality ◽  
Teaching Model ◽  
College Mathematics ◽  
Improved Genetic Algorithm ◽  
Training Process ◽  
Convolutional Network

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.

Research on Evaluation and Analysis Model of Business English Teaching Based on Fuzzy Comprehensive Judgment

2014 ◽  
Vol 644-650 ◽  
pp. 5611-5614
Author(s):  
Chun Hua Mao
Keyword(s):  
Quality Evaluation ◽  
Subjective Experience ◽  
Evaluation Method ◽  
Expert Knowledge ◽  
Teaching Quality ◽  
Classroom Teaching ◽  
Traditional Teaching ◽  
Business English ◽  
English Teaching ◽  
Comprehensive Judgment

Compared with the traditional teaching quality evaluation method, Fuzzy Comprehensive Judgment Model. Business English classroom teaching evaluation is an important part of English teaching quality management in institutions of higher learning, and it is of vital significance for us to improve the quality of foreign language teaching. Compared with the traditional teaching quality evaluation method, fuzzy comprehensive judgment Model, based on expert knowledge and subjective experience, can use mathematical methods with rigorous logic to remove subjective elements as much as possible, and to reasonably determine the evaluation index weight; it may take advantage of scientific quantitative methods to characterize the qualitative issues in classroom teaching qualitative evaluation, so that the qualitative and quantitative analysis can get a better integration, which helps to overcome the subjective arbitrariness in English teaching quality evaluation, thus improving the reliability, accuracy and impartiality of the fuzzy comprehensive evaluation.

scholarly journals An Applicable Way of Teaching Quality Evaluation Based on MOOC Platform

2017 ◽  
Vol 12 (03) ◽  
pp. 57 ◽  
Author(s):  
Chen Zhuo ◽  
Xiaoming Dong
Keyword(s):  
Quality Evaluation ◽  
Evaluation Method ◽  
Control Experiment ◽  
Teaching Quality ◽  
Assessment Model ◽  
Fuzzy Evaluation ◽  
Teaching Management ◽  
Comprehensive Fuzzy Evaluation ◽  
Important Means

The MOOC-based education is an important means to improve the quality of education as the increasing development of internet; meanwhile, the assessment of teaching quality is an indispensable aspect in teaching management, and it has been more and more important as the scale of the students' expansion, In order to deal with the challenges of big data processing effectively in the field of education, we designed a teaching quality assessment model on the MOOC platform based on comprehensive fuzzy evaluation. To verify the effectiveness of our method, a control experiment was adopted to explore the significance of our evaluation method, the results show that it can help teacher to prepare their teaching contents and students to improve their learning efficiency.

Simulation of English teaching quality evaluation model based on gaussian process machine learning

2020 ◽  
pp. 1-11
Author(s):  
Huang Wenming
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Teaching Quality ◽  
Distribution Characteristics ◽  
English Teaching ◽  
Traditional Evaluation

The efficiency of traditional English teaching quality evaluation is relatively low, and evaluation statistics are very troublesome. Traditional evaluation method makes teaching evaluation a difficult project, and traditional evaluation method takes a long time and has low efficiency, which seriously affects the school’s efficiency. In order to improve the quality of English teaching, based on machine learning technology, this study combines Gaussian process to improve the algorithm, use mixed Gaussian to explore the distribution characteristics of samples, and improve the classic relevance vector machine model. Moreover, this study proposes an active learning algorithm that combines sparse Bayesian learning and mixed Gaussian, strategically selects and labels samples, and constructs a classifier that combines the distribution characteristics of the samples. In addition, this study designed a control experiment to analyze the performance of the model proposed in this study. It can be seen from the comparison that this research model has a good performance in the evaluation of the English teaching quality of traditional models and online models. This shows that the algorithm proposed in this paper has certain advantages, and it can be applied to the practice of English intelligent teaching system.

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