<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study focuses on the status quo of group exercise interest differences and similarities among college students, and proposes the interest scalar law of group exercise for the group exercise data of college students, which measures the frequency of group exercise based on the parameter and activity. Based on the interest degree indexes of exercise duration and time, the interest degree model of group exercise is constructed to explore the laws of group exercise behavior. An association rule mining algorithm based on genetic algorithms and interest degrees is proposed, and an improved fitness function is proposed to optimize the algorithm. Taking China’s province S as an example, the statistics on group sports interests of college students show that the central tendency among group sports interests is ball group sports. Among the influencing factors of interest dissimilarity, the gender influencing factor significantly differs in the dimensions of psychological state and peer influence (p&lt;0.05). There were significant differences (p&lt;0.05) and highly significant differences (p&lt;0.01) in the p-values of the place of origin influence factor in the course offerings and psychological state dimensions, respectively. The group sport type influence factor, on the other hand, showed significant differences (p&lt;0.01) in all four sizes, including psychological state and family status.</p>
</abstract>

This study focuses on the status quo of group exercise interest differences and similarities among college students, and proposes the interest scalar law of group exercise for the group exercise data of college students, which measures the frequency of group exercise based on the parameter and activity. Based on the interest degree indexes of exercise duration and time, the interest degree model of group exercise is constructed to explore the laws of group exercise behavior. An association rule mining algorithm based on genetic algorithms and interest degrees is proposed, and an improved fitness function is proposed to optimize the algorithm. Taking China’s province S as an example, the statistics on group sports interests of college students show that the central tendency among group sports interests is ball group sports. Among the influencing factors of interest dissimilarity, the gender influencing factor significantly differs in the dimensions of psychological state and peer influence (p<0.05). There were significant differences (p<0.05) and highly significant differences (p<0.01) in the p-values of the place of origin influence factor in the course offerings and psychological state dimensions, respectively. The group sport type influence factor, on the other hand, showed significant differences (p<0.01) in all four sizes, including psychological state and family status.

A statistical study on the similarities and differences of sports interests among college students

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study focuses on the status quo of group exercise interest differences and similarities among college students, and proposes the interest scalar law of...

A statistical study on the similarities and differences of sports interests among college students

Pubblicato online: 03 mag 2024

Pagine: -

Ricevuto: 08 apr 2024

Accettato: 25 apr 2024

DOI: https://doi.org/10.2478/amns-2024-0951

Parole chiave
Interest scaling law, Interest degree model, Genetic algorithm, Association rule, Group sports

© 2024 Xinbao Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A statistical study on the similarities and differences of sports interests among college students

Pubblicato online: 03 mag 2024

Pagine: -

Ricevuto: 08 apr 2024

Accettato: 25 apr 2024

DOI: https://doi.org/10.2478/amns-2024-0951

Parole chiaveInterest scaling law, Interest degree model, Genetic algorithm, Association rule, Group sports

© 2024 Xinbao Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Interest scaling law, Interest degree model, Genetic algorithm, Association rule, Group sports