Title: Evaluation and analysis of classroom teaching quality of art design specialty based on DBT-SVM

Authors: Junmei Guo

Addresses: College of Fine Arts and Art Design, Henan Vocational University of Science and Technology, Zhoukou, 466000, China

Abstract: Evaluating the quality of classroom teaching in higher education can improve teachers' teaching, but the evaluating results are currently inaccurate. The study combines the binary tree support vector machine (BT-SVM) and the Euclidean distance method to obtain the distance binary tree support vector machine (DBT-SVM) algorithm. The performance of DBT-SVM algorithm is tested and compared with one versus one (OVO) algorithm and one versus rest (OVR) algorithm. The results show that the accuracy of the DBT-SVM is 92.2% and the test time is 0.02 s; it is superior to the traditional algorithms. In the empirical analysis of the evaluation model, the accuracy rate of the DBT-SVM algorithm model is 97.85%, which is superior to TW-SVM and traditional algorithm models. The results show that the performance of the optimised DBT-SVM algorithm has greatly improved the accuracy and test time of the traditional SVM algorithm.

Keywords: teaching quality evaluation; binary tree; Euclidean distance method; support vector machine; binary tree support vector machine; BT-SVM; distance binary tree support vector machine; DBT-SVM; one versus one; OVO.

DOI: 10.1504/IJNVO.2023.133833

International Journal of Networking and Virtual Organisations, 2023 Vol.28 No.2/3/4, pp.106 - 121

Received: 24 Aug 2022
Accepted: 09 Nov 2022
Published online: 04 Oct 2023 *

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