<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>In this paper, firstly, a collaborative filtering algorithm based on users is used to mine the past behaviors of the target users. Items are glanced and composed to gain insight into the user’s preferred items and to mine preference information, followed by searching for users with similar preferences to the user in the system and calculating the similarity between users and users using cosine similarity and pearson similarity, and predicting the target user’s rating of an element based on the rating value of an item given by a nearby user. Then the personalized teaching system is designed by the actual situation of the online teacher teaching courses on the platform and the course demands of the platform students, and the path of high-quality improvement of teacher training and teaching is studied, mainly focusing on three aspects: clear value orientation, good personalized system teaching planning, and implementation of multiple evaluations. When analyzing the student-teacher ratio of teacher education institutions, the student-teacher ratio of Central China Normal University has the largest value, 24.79%, while the values of East China Normal University and Nanjing Normal University are relatively small, 15.97% and 15.39%, respectively. Either based on theory or based on practice, any institution of higher education with an excessive student-teacher ratio. This study helps teachers grow professionally and provides a good foundation for eventual individualized student development.</p>
</abstract>

In this paper, firstly, a collaborative filtering algorithm based on users is used to mine the past behaviors of the target users. Items are glanced and composed to gain insight into the user’s preferred items and to mine preference information, followed by searching for users with similar preferences to the user in the system and calculating the similarity between users and users using cosine similarity and pearson similarity, and predicting the target user’s rating of an element based on the rating value of an item given by a nearby user. Then the personalized teaching system is designed by the actual situation of the online teacher teaching courses on the platform and the course demands of the platform students, and the path of high-quality improvement of teacher training and teaching is studied, mainly focusing on three aspects: clear value orientation, good personalized system teaching planning, and implementation of multiple evaluations. When analyzing the student-teacher ratio of teacher education institutions, the student-teacher ratio of Central China Normal University has the largest value, 24.79%, while the values of East China Normal University and Nanjing Normal University are relatively small, 15.97% and 15.39%, respectively. Either based on theory or based on practice, any institution of higher education with an excessive student-teacher ratio. This study helps teachers grow professionally and provides a good foundation for eventual individualized student development.

Exploring the path of high-quality development of teacher education and teaching based on collaborative filtering algorithm

Applied Mathematics and Nonlinear Sciences

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

In this paper, firstly, a collaborative filtering algorithm based on users is used to mine the past behaviors of the target users. Items are glanced and...

Exploring the path of high-quality development of teacher education and teaching based on collaborative filtering algorithm

Publié en ligne: 30 sept. 2023

Pages: -

Reçu: 14 oct. 2022

Accepté: 10 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00398

Mots clés
Internet, Collaborative filtering algorithm, Cosine similarity, Personalized teaching system, Pearson similarity.

© 2023 Haiyan Liu et al., published by Sciendo

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

Exploring the path of high-quality development of teacher education and teaching based on collaborative filtering algorithm

Publié en ligne: 30 sept. 2023

Pages: -

Reçu: 14 oct. 2022

Accepté: 10 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00398

Mots clésInternet, Collaborative filtering algorithm, Cosine similarity, Personalized teaching system, Pearson similarity.

© 2023 Haiyan Liu et al., published by Sciendo

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

Mots clés
Internet, Collaborative filtering algorithm, Cosine similarity, Personalized teaching system, Pearson similarity.