Behavior Analysis of Students in Preschool Mathematics Teaching Based on Deep Learning

Artigo2025

0 visualizações desta ficha 0 acessos ao texto completo Contagem real do Portal desde 25/09/2026; tráfego comum, sem testes; quem desativa o rastreamento no navegador não é contado.

Na última verificação automática (03/10/2026), não respondeu na fonte: texto completo (HTTP 404). O endereço é mantido e volta a ser verificado no próximo ciclo; se o link não abrir, tente mais tarde ou use a página oficial.

Texto completo não localizado nas fontes consultadas. O registro é mantido pela pertinência.

Resumo

Students behaviors can directly reflect the quality of the classroom. Analyzing and evaluating classroom behaviors through artificial intelligence and deep learning is conducive to improving teaching quality. The traditional methods for identifying students classroom behaviors involve that teachers directly observe students states or analyze them through surveillance videos after class. These methods are time-consuming, laborintensive, and have a low recognition rate, making it difficult to reflect the problems existing in the classroom and exams in real time. To solve this problem, this paper proposes a novel students classroom behaviors based on YOLOv8 deep learning model. Combining the channel attention mechanism with deep convolution, a dynamic channel attention convolution (DCAConv) is proposed, which can dynamically adjust the channel weights and capture key features more sensitively. It introduces multi-scale convolutional attention (MSCA) to maximize the ability of mining multi-scale convolutional features through element multiplication, and enhance the attention to spatial details. Meanwhile, a multi-scale context fusion (MSCF) module is constructed. Through convolution and self-attention mechanism, multi-scale feature fusion is enhanced. Adding a small target detection layer and extracting local features from larger-sized feature maps significantly improves the ability to recognize the behaviors of students in the back row. The experimental results show that the average recognition accuracy rate of the proposed behavior recognition method for various parts of the human body can reach up to 83.7% at most. The recognition rates for various behaviors in simple and crowded scenarios reach more than 92.1% and 86.3% respectively, and it can effectively recognize various behaviors in the classroom.

Dados da obra

Tipo: Artigo
Periódico: Journal of Applied Science and Engineering, v. 29, n. 3, p. 585-593
Editora: Tamkang University Press
Ano: 2025 (2025-07)
Idioma: en
Palavras-chave: behavior analysis; yolov8; deep learning; channel attention mechanism; multi-scale context fusion
DOI: 10.6180/jase.202603_29(3).0009
ISSN: 2708-9967, 2708-9975
Acesso: Metadados apenas
Fontes: DOAJ — Directory of Open Access Journals
Na Biblioteca desde: 01/10/2026 · verificada em 03/10/2026

Como citar

ABNT
QIN, Guangning. Behavior Analysis of Students in Preschool Mathematics Teaching Based on Deep Learning. Journal of Applied Science and Engineering, v. 29, n. 3, p. 585-593, 2025. DOI: https://doi.org/10.6180/jase.202603_29(3).0009.

APA

Qin, G. (2025). Behavior Analysis of Students in Preschool Mathematics Teaching Based on Deep Learning. Journal of Applied Science and Engineering, 29(3), 585-593. https://doi.org/10.6180/jase.202603_29(3).0009

BibTeX

@article{qin2025,
  author = {Qin, Guangning},
  title = {Behavior Analysis of Students in Preschool Mathematics Teaching Based on Deep Learning},
  year = {2025},
  journal = {Journal of Applied Science and Engineering},
  volume = {29},
  number = {3},
  pages = {585-593},
  publisher = {Tamkang University Press},
  doi = {10.6180/jase.202603_29(3).0009},
  language = {en}
}

RIS

TY  - JOUR
AU  - Qin, Guangning
TI  - Behavior Analysis of Students in Preschool Mathematics Teaching Based on Deep Learning
PY  - 2025
JO  - Journal of Applied Science and Engineering
VL  - 29
IS  - 3
SP  - 585
EP  - 593
DO  - 10.6180/jase.202603_29(3).0009
LA  - en
ER  -

Referência gerada automaticamente a partir dos metadados das fontes; confira com o documento original. Baixar BibTeX · Baixar RIS