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

<p>Under the progress trend of the information technology period environment of deepening study curriculum reform, personalized learning in specialized education of industrial colleges has become one of its key contents. This paper mainly studies the necessary system design based on characteristics of genetic calculation for specialized education in industrial colleges under industry-education integration. This paper first introduces the principle of genetic algorithm. It then proceeds to introduce in detail the shortcomings of The advantage of this calculation method is that the standard calculation tendency is unable to move forward in an area extremes, the iteration is relatively slow, and the low accuracy when dealing with optimization problems. Then the design and construction ideas of the personalized platform and the database design from Learners, educators (teachers), and administrators are compared by experimentally analyzing the data diversity and convergence of the three algorithms on the database. Experimentally, the diversity of the self-adaptive genetic algorithm is maintained at 0.8, and the diversity, iteration time, and convergence standard are better compared with the comparison subject. Using a self-adaptive genetic algorithm to evaluate and rank learners’ personality differences and to provide and recommend suitable learning methods for learners helps develop education and improve teaching quality and has great historical significance for China’s development and international status.</p>
</abstract>

Under the progress trend of the information technology period environment of deepening study curriculum reform, personalized learning in specialized education of industrial colleges has become one of its key contents. This paper mainly studies the necessary system design based on characteristics of genetic calculation for specialized education in industrial colleges under industry-education integration. This paper first introduces the principle of genetic algorithm. It then proceeds to introduce in detail the shortcomings of The advantage of this calculation method is that the standard calculation tendency is unable to move forward in an area extremes, the iteration is relatively slow, and the low accuracy when dealing with optimization problems. Then the design and construction ideas of the personalized platform and the database design from Learners, educators (teachers), and administrators are compared by experimentally analyzing the data diversity and convergence of the three algorithms on the database. Experimentally, the diversity of the self-adaptive genetic algorithm is maintained at 0.8, and the diversity, iteration time, and convergence standard are better compared with the comparison subject. Using a self-adaptive genetic algorithm to evaluate and rank learners’ personality differences and to provide and recommend suitable learning methods for learners helps develop education and improve teaching quality and has great historical significance for China’s development and international status.

Construction of a personalized learning platform based on genetic algorithm for specialized education in industrial colleges in the context of industry-education integration

Nanjing Vocational University of Industry Technology

Applied Mathematics and Nonlinear Sciences

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

Under the progress trend of the information technology period environment of deepening study curriculum reform, personalized learning in specialized education...

Construction of a personalized learning platform based on genetic algorithm for specialized education in industrial colleges in the context of industry-education integration

Publicado en línea: 01 jun 2023

Páginas: -

Recibido: 31 jul 2022

Aceptado: 07 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00325

Palabras clave
Big data, Industry-education integration, Genetic algorithm, Specialized education, Personalized learning platform

© 2023 Bin Fang, published by Sciendo

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

Construction of a personalized learning platform based on genetic algorithm for specialized education in industrial colleges in the context of industry-education integration

Publicado en línea: 01 jun 2023

Páginas: -

Recibido: 31 jul 2022

Aceptado: 07 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00325

Palabras claveBig data, Industry-education integration, Genetic algorithm, Specialized education, Personalized learning platform

© 2023 Bin Fang, published by Sciendo

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

Palabras clave
Big data, Industry-education integration, Genetic algorithm, Specialized education, Personalized learning platform