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The use of smart devices in the digital reform of physical education and its impact on learning outcomes

Rong Pang

Rong Pang

Tianping College, Suzhou University of Science and TechnologySuzhou, China
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Pang, Rong
 y 
Xiaoniu Jiang

Xiaoniu Jiang

Suzhou University of Science and TechnologySuzhou, China
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Jiang, Xiaoniu
  
19 mar 2025
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Publicado en línea: 19 mar 2025
Recibido: 26 oct 2024
Aceptado: 11 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0369
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© 2025 Rong Pang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Schematic diagram of the user modeling process
Schematic diagram of the user modeling process

Figure 2.

Schematic diagram of CB recommendation algorithm
Schematic diagram of CB recommendation algorithm

Figure 3.

Intelligent equipment analysis of students’ clustering
Intelligent equipment analysis of students’ clustering

Figure 4.

The weighted scores of the four students’ sports teaching sports
The weighted scores of the four students’ sports teaching sports

Figure 5.

Intelligent classroom teaching evaluation
Intelligent classroom teaching evaluation

Figure 6.

Research results of interest in pre-experiment study
Research results of interest in pre-experiment study

Figure 7.

Research results of interest in post-experiment study
Research results of interest in post-experiment study

Figure 8.

Results of the results of the experimental group and the control group
Results of the results of the experimental group and the control group

Statistical results of physical education scores

Variable Lung capacity 50m run Fixed jump Predisposition Jumping rope in a minute,
Pre-test Mean(CK) 68.24 71.63 67.54 70.29 60.77
Mean(T) 67.9 71.21 67.99 69.78 61.13
p value 0.375 0.46 0.452 0.519 0.398
Post-test Mean(CK) 70.89 74.09 71.98 74.43 63.22
Mean(T) 77.21 80.11 76.10 77.18 71.53
p value 0.025 0.014 0.017 0.026 0.02

Regression analysis of intelligent equipment and learning effect

Variable Correlation coefficient Standard error P value
Teacher qualification 0.0013 0.001 0.123
Student interest 0.118*** 0.026 0.000
Teaching evaluation 0.237*** 0.031 0.000
Teaching content 0.0394 0.015 0.073
Teaching model 0.156*** 0.019 0.000
Online learning activities 0.274*** 0.037 0.000
Interactive discussion 0.212*** 0.022 0.000
Personalized exercise recommendation 0.347*** 0.038 0.000
Student physical monitoring 0.319*** 0.026 0.000
Instructional feedback 0.276*** 0.03 0.000
R2 0.493
F 32.715

Specific descriptions of clustering groups

Cluster group Describe Proportion
Full development(green) The physical indicators of all kinds of physical fitness are reached or above the average, and they have a strong interest in sports activities. 12%
Skilled type(purple) In some specific means of good performance, high technical level, have strong interest in certain sports projects. 30%
Interest driver(orange) The physical quality, the skill level has a certain foundation, have higher interest in sports activities, willing to try and learn different sports. 24%
Potential type(blue) Lack of physical quality, physical exercise skills require further learning and training, and not too much interest in sports. 34%
Idioma:
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Temas de la revista:
Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros
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