Intelligent online teaching system based on SVM algorithm and complex network

2020 ◽  
pp. 1-11
Author(s):  
Chuanxin Fang
Keyword(s):  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Research Work ◽  
Principal Component ◽  
Teaching Quality ◽  
Support Vector ◽  
School Teaching ◽  
The Impact ◽  
Quality Evaluation Model

English Online teaching quality evaluation refers to the process of using effective technical means to comprehensively collect, sort and analyze the teaching status and make value judgments to improve teaching activities and improve teaching quality. The research work of this paper is mainly around the design of teaching quality evaluation model based on machine learning theory and has done in-depth research on the preprocessing of evaluation indicators and the construction of support vector machine teaching quality evaluation model. Moreover, this study uses improved principal component analysis to reduce the dimensionality of the evaluation index, thus avoiding the impact of the overly complicated network model on the prediction effect. In addition, in order to verify that the model proposed in this study has more advantages in evaluating teaching quality than other shallow models, the parameters of the model are tuned, and a control experiment is designed to verify the performance of the model. The research results show that this research model has a certain effect on the evaluation of school teaching quality, and it can be applied to practice.

scholarly journals Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Luxin Jiang ◽  
Xiaohui Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Particle Swarm ◽  
Teaching Quality ◽  
Particle Swarm Algorithm ◽  
Bp Model ◽  
Quality Evaluation Model

In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process (AHP) and particle swarm optimization BP neural network (PSO-BP) is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network.

Research on Information System for Teaching Quality Evaluation Model of Business English Translation Based on SVM

2014 ◽  
Vol 886 ◽  
pp. 552-555
Author(s):  
Chun Hua Mao
Keyword(s):  
English Translation ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Support Vector ◽  
Traditional Teaching ◽  
Business English ◽  
Applied Technology ◽  
Quality Evaluation Model

The teaching quality evaluation of business English translation is a key basis to discover the teaching problems of business English translation and to promote the teaching quality. Compared with the traditional teaching quality evaluation method, support vector machine which is a type of information applied technology has many unique advantages, such as high accuracy, easily operation and fast implementation. This paper studies the current teaching quality on the basis of business English translation, and establishes the teaching quality evaluation model of business English translation based on SVM, and the experimental results show the superiority and validity of this method in the teaching quality assessment of business English translation.

Online teaching quality evaluation based on emotion recognition and improved AprioriTid algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hui Yu
Keyword(s):  
Data Mining ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Practical Significance ◽  
Expression Recognition

The association rule algorithm in data mining is used to study the factors that may affect students’ performance, to make suggestions for teaching work, and to provide decision-making basis for teachers and teaching administrators, which has practical significance. There are many potential applications for facial expression recognition technology. For example, in the teaching process, facial expression recognition technology helps teachers understand students and judge students’ reactions to certain things. Based on the current research status of emotion recognition and data mining algorithms, this paper improves the AprioriTid algorithm and constructs an online teaching quality evaluation model based on teaching needs. In addition, this article applies the model constructed in this article to the evaluation of English online teaching quality and evaluates teaching quality through data mining. The experimental research shows that the model constructed in this paper has good performance.

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