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<p>The existing teaching model has the problems of a single learning mode and students’ interest in learning, so it is necessary to build a blended learning model scenario to promote students’ interest in learning. This paper proposes an educational data mining algorithm for the blended learning model of music teaching skills in the context of big data and proposes a blended learning model of teaching skills based on the PSO algorithm. Since the PSO particle swarm algorithm has the problem of low accuracy and overfitting, the XGBoost algorithm model is introduced based on the PSO particle swarm algorithm, and the Iris dataset is clustered. For the evaluation analysis of the blended learning model of music skills, 13 indicators were observed for three randomly selected students in this study. The accuracy of the optimized PSO-XGBoost algorithm was 0.95, which was 10% more accurate than the pre-optimized algorithm, and the overall accuracy was significantly improved. The three students scored 18.96, 18.97, and 19.61 in the music skills blended learning model evaluation learning assessment system evaluation, with the highest score reaching 19.61. The study showed that the music-teaching blended learning model is comprehensive and easy to implement in accordance with the existing teaching environment.</p>
</abstract>

The existing teaching model has the problems of a single learning mode and students’ interest in learning, so it is necessary to build a blended learning model scenario to promote students’ interest in learning. This paper proposes an educational data mining algorithm for the blended learning model of music teaching skills in the context of big data and proposes a blended learning model of teaching skills based on the PSO algorithm. Since the PSO particle swarm algorithm has the problem of low accuracy and overfitting, the XGBoost algorithm model is introduced based on the PSO particle swarm algorithm, and the Iris dataset is clustered. For the evaluation analysis of the blended learning model of music skills, 13 indicators were observed for three randomly selected students in this study. The accuracy of the optimized PSO-XGBoost algorithm was 0.95, which was 10% more accurate than the pre-optimized algorithm, and the overall accuracy was significantly improved. The three students scored 18.96, 18.97, and 19.61 in the music skills blended learning model evaluation learning assessment system evaluation, with the highest score reaching 19.61. The study showed that the music-teaching blended learning model is comprehensive and easy to implement in accordance with the existing teaching environment.

Exploring the blended learning model of music teaching skills for teacher trainees based on teacher training professional certification in the context of big data

Applied Mathematics and Nonlinear Sciences

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

The existing teaching model has the problems of a single learning mode and students’ interest in learning, so it is necessary to build a blended learning model...

Exploring the blended learning model of music teaching skills for teacher trainees based on teacher training professional certification in the context of big data

Data publikacji: 09 paź 2023

Zakres stron: -

Otrzymano: 15 lis 2022

Przyjęty: 19 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00574

Słowa kluczowe
Educational data mining, PSO particle swarm, PSO-XGBoost algorithm, Music teaching, Blended learning model

© 2023 Ke Liu, published by Sciendo

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

Exploring the blended learning model of music teaching skills for teacher trainees based on teacher training professional certification in the context of big data

Data publikacji: 09 paź 2023

Zakres stron: -

Otrzymano: 15 lis 2022

Przyjęty: 19 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00574

Słowa kluczoweEducational data mining, PSO particle swarm, PSO-XGBoost algorithm, Music teaching, Blended learning model

© 2023 Ke Liu, published by Sciendo

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

Słowa kluczowe
Educational data mining, PSO particle swarm, PSO-XGBoost algorithm, Music teaching, Blended learning model