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

<p>This paper uses Bayesian networks for the state combination of random variables to describe the relationship between prior probability and posterior probability by likelihood. Meanwhile, the elimination of complex variables is achieved by combining the operation results of multivariate non-independent joint conditional probability decomposition. Based on this, Bayes’ theorem is used to calculate the learning efficiency, the comprehensive pass rate of any interactive courseware, and the growing value of the visualized learning process to improve the sharing of resources for senior education. To verify the feasibility of the model, a teaching effectiveness test was conducted, and the results showed that 66.6% of older adults believed that participation in senior education could strengthen their bodies and minds. The percentage of medical and health care knowledge courses was 63.20%. The direct effect of the learning environment on the participation behavior of senior education was 0.143, while the indirect effect of educational resources on the participation behavior of senior education was 0.083. In addition, 61.78% of seniors thought that attitude towards senior education was very important. Therefore, it can be concluded that the model of the elderly education system fills the gap in the structure of the lifelong education system, enriches the uppermost structure of the lifelong education system, and promotes the improvement and development of the lifelong education system.</p>
</abstract>

This paper uses Bayesian networks for the state combination of random variables to describe the relationship between prior probability and posterior probability by likelihood. Meanwhile, the elimination of complex variables is achieved by combining the operation results of multivariate non-independent joint conditional probability decomposition. Based on this, Bayes’ theorem is used to calculate the learning efficiency, the comprehensive pass rate of any interactive courseware, and the growing value of the visualized learning process to improve the sharing of resources for senior education. To verify the feasibility of the model, a teaching effectiveness test was conducted, and the results showed that 66.6% of older adults believed that participation in senior education could strengthen their bodies and minds. The percentage of medical and health care knowledge courses was 63.20%. The direct effect of the learning environment on the participation behavior of senior education was 0.143, while the indirect effect of educational resources on the participation behavior of senior education was 0.083. In addition, 61.78% of seniors thought that attitude towards senior education was very important. Therefore, it can be concluded that the model of the elderly education system fills the gap in the structure of the lifelong education system, enriches the uppermost structure of the lifelong education system, and promotes the improvement and development of the lifelong education system.

The significance of senior education in the Internet era for the construction of lifelong education system

Applied Mathematics and Nonlinear Sciences

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

This paper uses Bayesian networks for the state combination of random variables to describe the relationship between prior probability and posterior...

The significance of senior education in the Internet era for the construction of lifelong education system

Publié en ligne: 07 oct. 2023

Pages: -

Reçu: 07 déc. 2022

Accepté: 17 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00523

Mots clés
Geriatric education, Bayesian network, Random variables, Joint conditional probability, Variable elimination

© 2023 Jingying Qi et al., published by Sciendo

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

The significance of senior education in the Internet era for the construction of lifelong education system

Publié en ligne: 07 oct. 2023

Pages: -

Reçu: 07 déc. 2022

Accepté: 17 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00523

Mots clésGeriatric education, Bayesian network, Random variables, Joint conditional probability, Variable elimination

© 2023 Jingying Qi et al., published by Sciendo

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

Mots clés
Geriatric education, Bayesian network, Random variables, Joint conditional probability, Variable elimination