A textual and visual features-jointly driven hybrid intelligent system for digital physical education teaching quality evaluation

Boyi Zeng; Jun Zhao; Shantian Wen; Boyi Zeng; Jun Zhao; Shantian Wen

doi:10.3934/mbe.2023606

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 13581-13601. doi: 10.3934/mbe.2023606

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Research article Special Issues

A textual and visual features-jointly driven hybrid intelligent system for digital physical education teaching quality evaluation

1.
Institute of Sport, Xihua University, Chengdu, Sichuan 610039, China
2.
School of Physical Education, Huzhou University, Huzhou, Zhejiang 313000, China

Academic Editor: Byung-Gyu Kim

Received: 05 April 2023 Revised: 12 May 2023 Accepted: 16 May 2023 Published: 14 June 2023

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.
- hybrid intelligent system,
- textual features,
- visual features,
- smart cities
Citation: Boyi Zeng, Jun Zhao, Shantian Wen. A textual and visual features-jointly driven hybrid intelligent system for digital physical education teaching quality evaluation[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 13581-13601. doi: 10.3934/mbe.2023606

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Abstract

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.

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