Title: Multi-dimensional dynamic evaluation method of English MOOCS autonomous learning based on multiple intelligences theory

Authors: Lizhen Shi

Addresses: Zhoukou Vocational and Technical College, Zhoukou, 466000, China

Abstract: In order to overcome the problems of traditional English autonomous learning evaluation methods, such as poor multi-dimensional evaluation effect, long evaluation time and inaccurate evaluation results, this paper proposes a multi-dimensional dynamic evaluation method of English autonomous learning based on the theory of multiple intelligences. The optimal multi-dimensional evaluation solution of English MOOCS autonomous learning is obtained by the theory of multiple intelligences. The optimal classification surface of English MOOCS autonomous learning is obtained by Lagrange function, and the multi-dimensional dynamic evaluation matrix is obtained by binary tree. This paper uses Euclidean distance binary tree support vector machine (DBT-SVM) multi class classification algorithm to evaluate English MOOCS learning. The experimental results show that the multi-dimensional dynamic evaluation of autonomous learning takes the least time, only 4.2 s, and the accuracy rate of multi-dimensional dynamic evaluation is as high as 97%.

Keywords: binary tree; theory of multiple intelligences; Lagrange function; multidimensional dynamic evaluation matrix.

DOI: 10.1504/IJCEELL.2023.132412

International Journal of Continuing Engineering Education and Life-Long Learning, 2023 Vol.33 No.4/5, pp.511 - 521

Received: 20 Jul 2021
Accepted: 07 Oct 2021
Published online: 19 Jul 2023 *

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