An English teaching quality evaluation model based on Gaussian process machine learning

2021 ◽  
Author(s):  
Shi Qi ◽  
Lei Liu ◽  
B. Santhosh Kumar ◽  
A. Prathik
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Teaching Quality ◽  
English Teaching ◽  
Model Based ◽  
Quality Evaluation Model

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.

Design of teaching quality evaluation model based on fuzzy mathematics and SVM algorithm

2018 ◽  
Vol 35 (3) ◽  
pp. 3091-3099 ◽  
Author(s):  
Kong Haining ◽  
Fan Hejun ◽  
Zhao Yan ◽  
Zhai Chunjuan ◽  
Zhang Chen ◽  
...  
Keyword(s):  
Quality Evaluation ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Fuzzy Mathematics ◽  
Model Based ◽  
Svm Algorithm ◽  
Quality Evaluation Model

Fuzzy mathematics and machine learning algorithms application in educational quality evaluation model

2020 ◽  
Vol 39 (4) ◽  
pp. 5583-5593
Author(s):  
Jian Wang ◽  
Weizhong Zhang
Keyword(s):  
Machine Learning ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Learning Algorithms ◽  
Teaching Quality ◽  
Machine Learning Algorithms ◽  
Educational Quality ◽  
Evaluation Index ◽  
Fuzzy Mathematics ◽  
Quality Evaluation Model

Teaching quality evaluation is a complex non-linear system fitting problem under the influence of many factors. The establishment of teaching quality evaluation is to construct a functional relationship between teaching quality evaluation index and teaching effect. In this paper, the authors analyze the fuzzy mathematics and machine learning algorithms application in educational quality evaluation model. Machine learning method has been well applied in complex problems such as classification, fitting, pattern recognition and so on. It can be used to realize a more comprehensive, reasonable and effective evaluation of the classroom teaching quality of university teachers. The simulation results show that the model can well express the complex relationship between the teaching quality evaluation index and the evaluation results. The theoretical values of the evaluation results are in the corresponding confidence interval, which proves that the machine learning algorithm has good reliability for different teaching quality evaluation problems.

Sign in / Sign up

Export Citation Format

Share Document