Conceptual understanding and cognitive patterns construction for physical education teaching based on deep learning algorithms

• Published in: Scientific reports Vol. 14; no. 1; pp. 31409 - 13
• Main Authors: Zhao, Long, Wu, Guoping, Shao, Weining, Ma, Xu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.12.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/301; 639/705; Accuracy; Algorithms; Association learning; Associative learning; Cognition; Cognition - physiology; Cognitive patterns; Comprehension - physiology; Conceptual understanding; Data mining; Deep Learning; Humanities and Social Sciences; Humans; Hypergraphic convolution; Learning algorithms; multidisciplinary; Neural networks; Neural Networks, Computer; Physical education; Physical Education and Training - methods; Science; Science (multidisciplinary); Students; Students - psychology; Teaching; Deep learning; Conceptual understanding; Hypergraphic convolution; Physical education; Association learning; Cognitive patterns
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-83028-9

To improve students’ understanding of physical education teaching concepts and help teachers analyze students’ cognitive patterns, the study proposes an association learning-based method for understanding physical education teaching concepts using deep learning algorithms, which extracts image features related to teaching concepts using convolutional neural networks. Moreover, a neurocognitive diagnostic model based on hypergraph convolution is constructed to mine the data of students’ long-term learning sequences and identify students’ cognitive outcomes. The findings revealed that the highest accuracy of the association graph convolutional neural network was 0.84 when the number of training samples was 90,000. In each of the three datasets, the cognitive diagnostic model’s accuracy was 0.76, 0.77, and 0.75, respectively. The use of the association graph convolutional neural network model resulted in an increase of 29% in the mastery of students in the concepts and knowledge of sports. The predictive accuracy of the cognitive schema diagnostic model ranged from 0.6 to 1.0 with a mean value of 0.81. The study reveals that the model proposed in the study has high accuracy and stability in predicting cognitive patterns, which can better identify students’ cognitive states and provide strong support for instructional guidance and personalized learning.