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

<p>With the deep integration of the Internet and education, the personalized development of education has become a new trend in education, and it also increasingly emphasizes the learner’s subject position in learning. In this study, a smart teaching model for learners’ individuality is developed by integrating WEB data mining technology, SOM neural network, and multiple recommendation mechanisms. The model achieves personalized recommendations for learning resources through the collection of user characteristics and then according to the recommendation algorithm. Then, using college English courses at Zhengzhou Shengda University as an example, the SOM neural network is utilized to diagnose the teaching cognition of the experiment. The experimental results show that the SOM neural network cognitive diagnosis results in a high judgment rate, with a high judgment rate of 84.842%. It has certain feasibility in teaching small sample diagnostics. In terms of efficiency, the time of cognitive diagnosis can be controlled within 1 second, which is real-time in teaching applications. The significance test of students’ performance after the experiment shows that the personalized wisdom teaching model constructed in this paper has a significant effect on improving teaching performance.</p>
</abstract>

With the deep integration of the Internet and education, the personalized development of education has become a new trend in education, and it also increasingly emphasizes the learner’s subject position in learning. In this study, a smart teaching model for learners’ individuality is developed by integrating WEB data mining technology, SOM neural network, and multiple recommendation mechanisms. The model achieves personalized recommendations for learning resources through the collection of user characteristics and then according to the recommendation algorithm. Then, using college English courses at Zhengzhou Shengda University as an example, the SOM neural network is utilized to diagnose the teaching cognition of the experiment. The experimental results show that the SOM neural network cognitive diagnosis results in a high judgment rate, with a high judgment rate of 84.842%. It has certain feasibility in teaching small sample diagnostics. In terms of efficiency, the time of cognitive diagnosis can be controlled within 1 second, which is real-time in teaching applications. The significance test of students’ performance after the experiment shows that the personalized wisdom teaching model constructed in this paper has a significant effect on improving teaching performance.

Modeling Personalized Smart Teaching for Learner Needs

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Modeling Personalized Smart Teaching for Learner Needs"}

With the deep integration of the Internet and education, the personalized development of education has become a new trend in education, and it also...

Modeling Personalized Smart Teaching for Learner Needs

Data publikacji: 07 cze 2024

Zakres stron: -

Otrzymano: 10 lut 2024

Przyjęty: 24 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1384

Słowa kluczowe
Web data mining, SOM neural network, Collaborative filtering algorithm, Hybrid recommendation algorithm, Personalized teaching

© 2024 Juanjuan Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Modeling Personalized Smart Teaching for Learner Needs

Data publikacji: 07 cze 2024

Zakres stron: -

Otrzymano: 10 lut 2024

Przyjęty: 24 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1384

Słowa kluczoweWeb data mining, SOM neural network, Collaborative filtering algorithm, Hybrid recommendation algorithm, Personalized teaching

© 2024 Juanjuan Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Web data mining, SOM neural network, Collaborative filtering algorithm, Hybrid recommendation algorithm, Personalized teaching