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

<p>E-learning is a very popular learning method at this stage, and Internet learners need to invest much time to retrieve the required e-learning network resources. In building the existing e-learning resources, the cognitive level, thinking ability, learning style, and other factors of learners are not considered. For this challenge, the paper gives the recommendation methods of the collaborative filtering algorithm model and learner model, compares the advantages and disadvantages of these models and traditional recommendation methods using the data of mean square error, response time, and accuracy, and examines the students’ suggestions for this recommendation method in the field. The collaborative filtering recommendation technique had the highest completion rate, above 0.7 for 5-20 recommendations and above 0.75 and 0.8 for 20-35. The rate of the learner model is stable between 0.7 and 0.8.</p>
<p>In contrast, the detection rate of traditional methods is between 0.6 and 0.7. The collaborative filtering recommendation method outperforms the other two methods regarding completeness, response time, accuracy, and F1 value. In terms of satisfaction, the collaborative filtering recommendation algorithm had the highest satisfaction rate, followed by the learner model; the collaborative filtering method had the best results in improving learning performance. Therefore, personalized recommendation methods can improve learning efficiency and reduce retrieval time, and the collaborative filtering recommendation method is the most effective among the two methods proposed in this paper.</p>
</abstract>

E-learning is a very popular learning method at this stage, and Internet learners need to invest much time to retrieve the required e-learning network resources. In building the existing e-learning resources, the cognitive level, thinking ability, learning style, and other factors of learners are not considered. For this challenge, the paper gives the recommendation methods of the collaborative filtering algorithm model and learner model, compares the advantages and disadvantages of these models and traditional recommendation methods using the data of mean square error, response time, and accuracy, and examines the students’ suggestions for this recommendation method in the field. The collaborative filtering recommendation technique had the highest completion rate, above 0.7 for 5-20 recommendations and above 0.75 and 0.8 for 20-35. The rate of the learner model is stable between 0.7 and 0.8.
In contrast, the detection rate of traditional methods is between 0.6 and 0.7. The collaborative filtering recommendation method outperforms the other two methods regarding completeness, response time, accuracy, and F1 value. In terms of satisfaction, the collaborative filtering recommendation algorithm had the highest satisfaction rate, followed by the learner model; the collaborative filtering method had the best results in improving learning performance. Therefore, personalized recommendation methods can improve learning efficiency and reduce retrieval time, and the collaborative filtering recommendation method is the most effective among the two methods proposed in this paper.

Research on personalized recommendation method of preschool e-learning resources

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Research on personalized recommendation method of preschool e-learning resources"}

E-learning is a very popular learning method at this stage, and Internet learners need to invest much time to retrieve the required e-learning network...

Research on personalized recommendation method of preschool e-learning resources

Online veröffentlicht: 11. Aug. 2023

Seitenbereich: -

Eingereicht: 28. Okt. 2022

Akzeptiert: 20. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00174

Schlüsselwörter
E-learning, Online learning resources, Personalized recommendation, Collaborative filtering algorithm, Learner model

© 2023 Wei Wu, published by Sciendo

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

Research on personalized recommendation method of preschool e-learning resources

Online veröffentlicht: 11. Aug. 2023

Seitenbereich: -

Eingereicht: 28. Okt. 2022

Akzeptiert: 20. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00174

SchlüsselwörterE-learning, Online learning resources, Personalized recommendation, Collaborative filtering algorithm, Learner model

© 2023 Wei Wu, published by Sciendo

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

Schlüsselwörter
E-learning, Online learning resources, Personalized recommendation, Collaborative filtering algorithm, Learner model