Research Article
Personalized Scheduling of Distributed Online Educational Resources Based on Simulated Annealing Genetic Algorithm
@INPROCEEDINGS{10.1007/978-3-031-50543-0_15, author={Xiaotang Geng and Yan Huang}, title={Personalized Scheduling of Distributed Online Educational Resources Based on Simulated Annealing Genetic Algorithm}, proceedings={Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part I}, proceedings_a={ADHIP}, year={2024}, month={3}, keywords={Distributed Individualization Dispatch Online education resources Simulated annealing genetic algorithm}, doi={10.1007/978-3-031-50543-0_15} }- Xiaotang Geng
Yan Huang
Year: 2024
Personalized Scheduling of Distributed Online Educational Resources Based on Simulated Annealing Genetic Algorithm
ADHIP
Springer
DOI: 10.1007/978-3-031-50543-0_15
Abstract
The arrival of the information age has accelerated the development of information education, and distributed online education resources have increased exponentially. However, due to the low level of scheduling service, the application effect of education resources is poor, which hinders the follow-up development of information education. A personalized scheduling method of distributed online education resources based on simulated annealing genetic algorithm is proposed. The membership relationship between knowledge points and educational resources is calculated using fuzzy logic method, and the corresponding educational resource model is constructed. Based on this, the purpose and key problems of personalized scheduling of educational resources are analyzed, and the objective function of personalized scheduling of distributed online educational resources is constructed. The objective function is solved based on simulated annealing genetic algorithm, Obtain the final personalized scheduling scheme of distributed online education resources, and realize the personalized scheduling of distributed online education resources. Experimental data shows that after the proposed method is applied, the minimum response time of distributed online education resource scheduling is 6s, and the maximum precision of distributed online education resource scheduling is 96%, which fully confirms that the proposed method has better application performance.
