Zhao Long, Wu Guoping, Shao Weining, Ma Xu
Faculty of Medicine and Health, Al-Farabi Kazakh National University, Al-Farabi Avenue 71, Almaty, 050040, Kazakhstan.
Department of Public Sports, Yantai Early Childhood Normal College, Yantai, 265600, China.
Sci Rep. 2024 Dec 28;14(1):31409. 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.
为提高学生对体育教学概念的理解，并帮助教师分析学生的认知模式，该研究提出了一种基于关联学习的方法，利用深度学习算法理解体育教学概念，该方法使用卷积神经网络提取与教学概念相关的图像特征。此外，构建了基于超图卷积的神经认知诊断模型，以挖掘学生长期学习序列的数据并识别学生的认知结果。研究结果显示，当训练样本数量为90000时，关联图卷积神经网络的最高准确率为0.84。在三个数据集中，认知诊断模型的准确率分别为0.76、0.77和0.75。使用关联图卷积神经网络模型使学生在体育概念和知识的掌握上提高了29%。认知图式诊断模型的预测准确率在0.6至1.0之间，平均值为0.81。该研究表明，研究中提出的模型在预测认知模式方面具有较高的准确性和稳定性，能够更好地识别学生的认知状态，并为教学指导和个性化学习提供有力支持。
