Conceptual understanding and cognitive patterns construction for physical education teaching based on deep learning algorithms.
Journal:
Scientific reports
PMID:
39732971
Abstract
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.