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

<p>Based on big data technology, this paper firstly collects and pre-processes educational resources and builds a dual teaching system based on two basic concepts of “creation” and “analysis”, combining technology and theory. Secondly, the FCM clustering algorithm is used to cluster the student learning data and get the optimal clustering center, and the Lagrange multiplier method is used to optimize the objective function, identify the student learning characteristics, and conduct a dynamic pre-testing assessment. Finally, the quantum optimization algorithm dynamically assigns the assessment data to eliminate redundant data and construct a feature set library for individual student learning. The practical analysis and application results show that the effect feedback in the two courses of composition and orchestration reached 88% and 93%. In the music analysis and music composition assessments, the percentage of the number of students with performance range values of 100-90 averaged 43% and 42.75%, respectively. In addition, 72.97% of the students found the system helpful in independent learning ability. It indicates that the teaching system constructed in this paper can better serve the teaching of composition technique and further improve the teaching model and process.</p>
</abstract>

Based on big data technology, this paper firstly collects and pre-processes educational resources and builds a dual teaching system based on two basic concepts of “creation” and “analysis”, combining technology and theory. Secondly, the FCM clustering algorithm is used to cluster the student learning data and get the optimal clustering center, and the Lagrange multiplier method is used to optimize the objective function, identify the student learning characteristics, and conduct a dynamic pre-testing assessment. Finally, the quantum optimization algorithm dynamically assigns the assessment data to eliminate redundant data and construct a feature set library for individual student learning. The practical analysis and application results show that the effect feedback in the two courses of composition and orchestration reached 88% and 93%. In the music analysis and music composition assessments, the percentage of the number of students with performance range values of 100-90 averaged 43% and 42.75%, respectively. In addition, 72.97% of the students found the system helpful in independent learning ability. It indicates that the teaching system constructed in this paper can better serve the teaching of composition technique and further improve the teaching model and process.

Dual Construction of “Composition” and “Analysis” in Composition and Technical Theory in the Context of Big Data

Applied Mathematics and Nonlinear Sciences

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

Based on big data technology, this paper firstly collects and pre-processes educational resources and builds a dual teaching system based on two basic concepts...

Dual Construction of “Composition” and “Analysis” in Composition and Technical Theory in the Context of Big Data

Online veröffentlicht: 07. Okt. 2023

Seitenbereich: -

Eingereicht: 11. Dez. 2022

Akzeptiert: 17. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00520

Schlüsselwörter
Big data technology, Dual teaching system, FCM clustering, Quantum optimization, Feature set

© 2023 Fei Huang et al., published by Sciendo

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

Dual Construction of “Composition” and “Analysis” in Composition and Technical Theory in the Context of Big Data

Online veröffentlicht: 07. Okt. 2023

Seitenbereich: -

Eingereicht: 11. Dez. 2022

Akzeptiert: 17. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00520

SchlüsselwörterBig data technology, Dual teaching system, FCM clustering, Quantum optimization, Feature set

© 2023 Fei Huang et al., published by Sciendo

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

Schlüsselwörter
Big data technology, Dual teaching system, FCM clustering, Quantum optimization, Feature set