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

Jiang, Luxin; Wang, Xiaohui

doi:10.1155/2020/6647683

Cited by 48 publications

(37 citation statements)

References 26 publications

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“…Recently, fuzzy mathematics is widely used in artificial intelligence, and artificial intelligence analysis methods are beginning to be applied in the fields of education. Researchers have carried out research on the application of neural networks such as BP neural networks in the evaluation of teaching quality and achieved better results than traditional methods [7,8]. Similar to the BP model, as a new technology, radial basis function networks (RBFs) are also one of the most commonly used types of neural networks.…”

Section: Introductionmentioning

confidence: 99%

The Use of Genetic Algorithm, Multikernel Learning, and Least-Squares Support Vector Machine for Evaluating Quality of Teaching

Zhang

Karamti

et al. 2022

Scientific Programming

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The educational data mining (EDM) methods are increasingly diversified. In this research, a hybrid method of multikernel learning (MKL), least-squares support vector machine (LSSVM), and genetic algorithm (GA) is employed to evaluate teaching quality through nine indicators; the reliability of our proposed method is evaluated by confidence interval and prediction interval. First, English teaching quality samples occurring from three age groups at Guizhou Normal University are collected. Next, an intelligent method MK-LSSVM is proposed. Finally, the test sets are regression by the proposed model, and regression results are evaluated by confidence interval, prediction interval, and several error calculation methods; we also develop an ablation experiment for our proposed model. The experiment indicates that the MKL-LSSVM-GA outperforms other benchmark methods at three age-group levels. Additionally, at all three age-group levels, the experiment indicates that three indicators are crucial for the evaluation of teaching quality. Therefore, the proposed model in this paper can evaluate the English teaching quality effectively.

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Section: Introductionmentioning

confidence: 99%

The Use of Genetic Algorithm, Multikernel Learning, and Least-Squares Support Vector Machine for Evaluating Quality of Teaching

Zhang

Karamti

et al. 2022

Scientific Programming

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“…Second, during the operation process, some students fill in some data and submit it, while others fill in the incorrect data, resulting in data loss or incompleteness. Finally, in the evaluation process, students' personal characteristics, including aesthetics, learning goals, personal interests, etc., are due to different personal starting points, resulting in uneven evaluation results when grading teachers [6][7][8][9][10]. Addressing the present state and existing challenges with the teaching quality evaluation system, this topic suggests a more effective technique for resolving such issues via the application of data network name (DNN) technology.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Japanese Teaching Quality Based on Deep Neural Network

Liu

2022

Security and Communication Networks

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The 21st century is an era of rapid development of information and frequent international exchanges, and Japanese language teaching has received increasing attention. Because of this, colleges and universities are now focused on improving the quality of Japanese education, both now and in the future. We need to boost the whole management of teaching quality, notably the assessment of instructors’ teaching quality, in order to improve teaching quality. However, because a number of factors influence the quality of instruction, and each factor’s weight varies, the evaluation results are difficult to express in a mathematical analytical formula, resulting in a complex nonlinear classification problem that traditional classification methods cannot solve well. As a new technology, as a result of the artificial neural networks (ANNs) fundamental qualities, it has been extensively applied in different evaluation issues for pattern recognition, nonlinear classification, and other research. This subject introduces the optimized deep neural network theory into Japanese teaching quality evaluation and completes the following work: (1) the algorithm of discrete Hopfield neural network is introduced in detail, and the neural network theory is introduced into teaching evaluation. (2) Then, based on the evaluation data of teachers’ teaching quality in a school, a large number of simulation experiments and training were carried out, and a neural network model for evaluation of teachers’ teaching effect was constructed and designed. Experiments reveal that the neural network model proposed in this paper is a nonlinear mapping method, which increases the evaluation’s dependability and makes the outcomes more effective and objective.

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“…Based on the study data, descriptive statistics, analysis of variance, and Pearson's correlation coefficient were used to test the study hypotheses and obtain valuable empirical results. By comparing this model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the normal BP model, and the PSO-BP model successfully conquers the shortcomings of the BP neural networks [1]. In the era of more and more popular online learning, it is imperative to build an education evaluation system for online courses.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Quality Evaluation of Online Teaching Courses Based on Machine Learning

Shi

2022

Journal of Sensors

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Due to the epidemic, online courses have become an important form of school courses, so it is very necessary to optimize online teaching courses. This paper analyzes the survey data by using factor analysis and ANP model and finally determines 18 evaluation indicators by scoring the evaluation indicators by the respondents. The analysis of these 18 indicators shows that the average score of goal setting is the highest, and the average score of interface design is the highest. Lowest: the low-scoring portion of the course interaction is the most important aspect of developing a suggested strategy, and setting the environment, stimulating interest, interface design, and performance evaluation are also important factors in improving the quality of online courses. It can be seen that teacher-student interaction, media presentation, and interest stimulation are three more important factors, and these three factors are relatively less important for performance evaluation, course duration, and language level. It can be seen that learners pay relatively low attention to performance evaluation and pay the highest attention to intelligent learning. These indicators with high attention are improved to optimize online teaching courses.

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

Cited by 48 publications

References 26 publications

The Use of Genetic Algorithm, Multikernel Learning, and Least-Squares Support Vector Machine for Evaluating Quality of Teaching

The Use of Genetic Algorithm, Multikernel Learning, and Least-Squares Support Vector Machine for Evaluating Quality of Teaching

Evaluation of Japanese Teaching Quality Based on Deep Neural Network

Optimization and Quality Evaluation of Online Teaching Courses Based on Machine Learning

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