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

Kong, Hai-ning; He-jun, Fan; Yan, Zhao; Zhai, Chunjuan; Chen, Zhang; Yajun, Han

doi:10.3233/jifs-169663

Cited by 13 publications

(6 citation statements)

References 3 publications

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“…Reference [17] applied SVM to the teaching design of text classification and achieved good results. According to the particularity of teaching activities in reference [18], SVM is used in the evaluation of teaching quality indicators, which can not only weigh the teaching process of teachers according to the actual situation but also improve the teaching activities of teachers according to the teaching process. In reference [19], SVM can help with a few issues in the assessment of classroom teacher quality.…”

Section: Related Workmentioning

confidence: 99%

Application of Support Vector Machine Model Based on Machine Learning in Art Teaching

Hua

Yang

2022

Wireless Communications and Mobile Computing

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The purpose of the evaluation is to reflect on whether education provides a good environment and conditions for the development of students and to reflect on the effect of teaching and the practicability of the talents cultivated by teaching to the society. When art education is evaluated, a number of positive outcomes have been achieved in terms of the development of art education, including the improvement of art education as a whole, the development of art talent, and a stronger role for the educational and social communities concerned about the quality of art education. Machine learning-based support vector machine (SVM) can better tackle issues like nonlinearity, high dimensionality, and local minima, which have been effectively implemented in the area of teaching quality evaluation (TQE) with the fast growth of information technology and the Internet. The main work of this paper is as follows: (1) it briefly expounds the research progress of TQE and multiclassification algorithm based on SVM at home and abroad and introduces the relevant basic theories of these two aspects. (2) One-to-one combination method is used in this research, reducing training time to a certain degree. Tests prove the procedure to be objective and equitable. (3) This research claims that an art TQE approach based on SVM is suited for limited professional assessment sample data and provides a method for this purpose.

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Section: Related Workmentioning

confidence: 99%

Application of Support Vector Machine Model Based on Machine Learning in Art Teaching

Hua

Yang

2022

Wireless Communications and Mobile Computing

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“…Therefore, if the weights of the BP network are regarded as the positions of the particles in the particle swarm algorithm, the change of the two neighboring weights during the training process of the network can be considered as the change of the particle positions. Thereby, the amount of weight change of BP network is calculated as shown in Equation (24) and Equation (25).…”

Section: Basic Ideas Of Optimizationmentioning

confidence: 99%

“…Literature [24] used the ACLLMD method to improve the RVM, established a classroom teaching quality evaluation model based on the improved RVM algorithm, and analyzed it with manual scoring and experimental data, which confirmed the reliability of the model and could play an excellent level in classroom teaching evaluation. Literature [25] in order to solve how to integrate multiple indicator weights into a single indicator, put forward a teaching evaluation model based on the Gaussian function parameter optimization method, and its reliability was proved by scientific practice while determining the distribution of the weights of teaching quality indicators.…”

Section: Introductionmentioning

confidence: 99%

The Construction of Online Education Quality Evaluation System Based on Intelligent Algorithm

Xiao

2024

Applied Mathematics and Nonlinear Sciences

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With the development of the network, online education breaks the defects of time and space, and becomes a way that more and more students choose to learn. This paper combines grey correlation analysis, optimizes BP neural network using PSO particle algorithm, and constructs GRA-PSO-BP model. The initial education evaluation indexes are improved by this intelligent algorithm model to construct an online education quality evaluation system, and then the optimized index system is used as a guide to evaluating the online education quality using this model. The results show that: the dispersion of the data of each index is R 2 = 0.92, which is greater than 0.75, and there is a strong connection between the indexes. The results show that the dispersion of the data of each index is greater than 0.75, and there is a strong connection between the indexes. The 10 colleges and universities scored 3.77, which is a satisfactory grade. The online education quality of these 10 colleges and universities are ranked from high to low as G5, G10, G9, G4, G6, G1, G3, G2, G7 and G8. The intelligent algorithm model and online education quality evaluation index system constructed in this paper can provide help for online education quality evaluation.

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“…e simulation results showed that the Gaussian kernel function parameter optimization method has strong advantages. In addition, it could accurately reflect the quality of teaching and accurately determine the weight of teaching quality evaluation indicators at the same time [6]. e purpose of Trskan was to assess student teaching practices, the role of school mentors, and the delivery of quality metrics for all participants in the process (i.e., school mentors, students, teacher mentors, teacher coordinators, and school coordinators in the Faculty of Arts) method.…”

Section: Related Workmentioning

confidence: 99%

Construction of Higher Education Teaching Quality Evaluation Model Based on Scientific Computing

Zhang

Chen²,

Qin³

2022

Mobile Information Systems

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The recipients of higher education are the backbone of future social construction, so their education is particularly important. As the research group of scientific computing is immature, the use of it to perform related calculations can achieve a multiplier effect with half the effort. Especially now, the quality evaluation of higher education has not been determined. Therefore, this paper aims to use scientific computing to construct a scientific evaluation model for the teaching quality of higher education. For scientific computing, this paper used its data processing ability to design and improve the computational science model evaluation algorithm. For the evaluation model, it not only constructed it with the fuzzy evaluation model and analyzed the weights of the relevant indicators proposed but also designed a teaching evaluation system according to the established model in order to evaluate the teaching quality efficiently and accurately. Experiments showed that the evaluation result score for a teacher was 81.45, and 81.45 was between 80 and 90. The teacher’s classroom evaluation result was good, which showed the effectiveness of the evaluation model. The test results of the system showed that the system could load up to 80 people and run smoothly online at the same time.

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

Cited by 13 publications

References 3 publications

Application of Support Vector Machine Model Based on Machine Learning in Art Teaching

Application of Support Vector Machine Model Based on Machine Learning in Art Teaching

The Construction of Online Education Quality Evaluation System Based on Intelligent Algorithm

Construction of Higher Education Teaching Quality Evaluation Model Based on Scientific Computing

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