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

<p>In this paper, texture mapping technology is utilized to enhance the virtual reality piano teaching situation, and the piano classroom teaching data is integrated through big data statistical analysis methods. A deep neural network based on BERT+BiLSTM+CRF is used to train a named entity recognition model and complete the construction of a knowledge graph for piano subject data. Then, deep learning methods are used to extract the piano subject data and construct a visualized knowledge graph for the piano subject. Analyze the use of the Piano Smart Classroom teaching platform and the development of a blended teaching mode. The design requirements of the piano intelligent teaching platform are planned from two aspects: platform user roles and platform use cases, and the overall design of the piano intelligent teaching platform is carried out. The use of piano teaching video samples is a tool for calculating the teaching language ratio, analyzing the teaching interactions between teachers and students, and comparing the number of interactions between traditional teaching and smart classroom teaching. Analyze the effectiveness of piano-smart classroom teaching in three dimensions: cognition, emotion, and skill. The mean value of the specific content score of piano intelligent classroom teaching is 3.82, which is the single item with the highest mean value, and the piano teaching classroom supported by smart technology can show the piano teaching content more comprehensively.</p>
</abstract>

In this paper, texture mapping technology is utilized to enhance the virtual reality piano teaching situation, and the piano classroom teaching data is integrated through big data statistical analysis methods. A deep neural network based on BERT+BiLSTM+CRF is used to train a named entity recognition model and complete the construction of a knowledge graph for piano subject data. Then, deep learning methods are used to extract the piano subject data and construct a visualized knowledge graph for the piano subject. Analyze the use of the Piano Smart Classroom teaching platform and the development of a blended teaching mode. The design requirements of the piano intelligent teaching platform are planned from two aspects: platform user roles and platform use cases, and the overall design of the piano intelligent teaching platform is carried out. The use of piano teaching video samples is a tool for calculating the teaching language ratio, analyzing the teaching interactions between teachers and students, and comparing the number of interactions between traditional teaching and smart classroom teaching. Analyze the effectiveness of piano-smart classroom teaching in three dimensions: cognition, emotion, and skill. The mean value of the specific content score of piano intelligent classroom teaching is 3.82, which is the single item with the highest mean value, and the piano teaching classroom supported by smart technology can show the piano teaching content more comprehensively.

Practical Exploration of Smart Classroom in Piano Teaching

Sichuan University of Science & Engineering

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Practical Exploration of Smart Classroom in Piano Teaching"}

In this paper, texture mapping technology is utilized to enhance the virtual reality piano teaching situation, and the piano classroom teaching data is...

Practical Exploration of Smart Classroom in Piano Teaching

Online veröffentlicht: 10. Juni 2024

Seitenbereich: -

Eingereicht: 24. Feb. 2024

Akzeptiert: 09. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1462

Schlüsselwörter
Texture mapping technology, Deep neural network, Subject knowledge mapping, Entity recognition, Piano teaching

© 2024 Xiaodong Huang, published by Sciendo

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

Practical Exploration of Smart Classroom in Piano Teaching

Online veröffentlicht: 10. Juni 2024

Seitenbereich: -

Eingereicht: 24. Feb. 2024

Akzeptiert: 09. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1462

SchlüsselwörterTexture mapping technology, Deep neural network, Subject knowledge mapping, Entity recognition, Piano teaching

© 2024 Xiaodong Huang, published by Sciendo

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

Schlüsselwörter
Texture mapping technology, Deep neural network, Subject knowledge mapping, Entity recognition, Piano teaching