A textual and visual features-jointly driven hybrid intelligent system for digital physical education teaching quality evaluation
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Published:2023
Issue:8
Volume:20
Page:13581-13601
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ISSN:1551-0018
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Container-title:Mathematical Biosciences and Engineering
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language:
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Short-container-title:MBE
Author:
Zeng Boyi1, Zhao Jun2, Wen Shantian2
Affiliation:
1. Institute of Sport, Xihua University, Chengdu, Sichuan 610039, China 2. School of Physical Education, Huzhou University, Huzhou, Zhejiang 313000, China
Abstract
<abstract>
<p>The utilization of intelligent computing in digital teaching quality evaluation has been a practical demand in smart cities. Currently, related research works can be categorized into two types: textual data-based approaches and visual data-based approaches. Due to the gap between their different formats and modalities, it remains very challenging to integrate them together when conducting digital teaching quality evaluation. In fact, the two types of information can both reflect distinguished knowledge from their own perspectives. To bridge this gap, this paper proposes a textual and visual features-jointly driven hybrid intelligent system for digital teaching quality evaluation. Visual features are extracted with the use of a multiscale convolution neural network by introducing receptive fields with different sizes. Textual features serve as the auxiliary contents for major visual features, and are extracted using a recurrent neural network. At last, we implement the proposed method through some simulation experiments to evaluate its practical running performance, and a real-world dataset collected from teaching activities is employed for this purpose. We obtain some groups of experimental results, which reveal that the hybrid intelligent system developed by this paper can bring more than 10% improvement of efficiency towards digital teaching quality evaluation.</p>
</abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
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