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<p>In the development process of education informatization, realizing personalized learning is the urgent demand of learners. In this paper, we construct an online teaching resources recommendation algorithm (VGNN) based on a graph neural network (GNN). The project-based collaborative filtering algorithm is used to find highly similar teaching resource materials using Pearson’s correlation coefficient in order to personalize the recommendation of online teaching resources. The personalized recommendation algorithm for online teaching resources was tested and analyzed using the online teaching course Fundamentals of Vocal Music Theory as a case study. The results show that the course “Fundamentals of Vocal Music Theory” is divided into 4 clusters with clear boundary contours under the graph neural network algorithm, and the number of knowledge points in the 4 clusters is 2308, 655, 513, and 97, respectively. After learning the personalized recommended content from the 4 clusters of knowledge points, the average rate of loss of points for each of them decreases by about 0.3. The algorithm effectively solves the accurate classification of vocal knowledge and implements personalized recommendations for educational resources.</p>
</abstract>

In the development process of education informatization, realizing personalized learning is the urgent demand of learners. In this paper, we construct an online teaching resources recommendation algorithm (VGNN) based on a graph neural network (GNN). The project-based collaborative filtering algorithm is used to find highly similar teaching resource materials using Pearson’s correlation coefficient in order to personalize the recommendation of online teaching resources. The personalized recommendation algorithm for online teaching resources was tested and analyzed using the online teaching course Fundamentals of Vocal Music Theory as a case study. The results show that the course “Fundamentals of Vocal Music Theory” is divided into 4 clusters with clear boundary contours under the graph neural network algorithm, and the number of knowledge points in the 4 clusters is 2308, 655, 513, and 97, respectively. After learning the personalized recommended content from the 4 clusters of knowledge points, the average rate of loss of points for each of them decreases by about 0.3. The algorithm effectively solves the accurate classification of vocal knowledge and implements personalized recommendations for educational resources.

A personalized recommendation algorithm for online teaching resources of vocal music based on graph neural network

Applied Mathematics and Nonlinear Sciences

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

In the development process of education informatization, realizing personalized learning is the urgent demand of learners. In this paper, we construct an...

A personalized recommendation algorithm for online teaching resources of vocal music based on graph neural network

Published Online: Dec 18, 2023

Page range: -

Received: Jan 16, 2023

Accepted: Jun 28, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01526

Keywords
Graph neural networks, Collaborative filtering algorithms, Pearson’s correlation coefficient, Knowledge points, Personalized recommendations

© 2023 Lina Wang, published by Sciendo

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

A personalized recommendation algorithm for online teaching resources of vocal music based on graph neural network

Published Online: Dec 18, 2023

Page range: -

Received: Jan 16, 2023

Accepted: Jun 28, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01526

KeywordsGraph neural networks, Collaborative filtering algorithms, Pearson’s correlation coefficient, Knowledge points, Personalized recommendations

© 2023 Lina Wang, published by Sciendo

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

Keywords
Graph neural networks, Collaborative filtering algorithms, Pearson’s correlation coefficient, Knowledge points, Personalized recommendations