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A Multidimensional Mining and Pattern Recognition Approach for Piano Teaching Behavior Data in Music Education

Cheng Lyu

Cheng Lyu

Xi’an Shiyou UniversityXi’an, China
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Lyu, Cheng
  
24 mar 2025
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Pubblicato online: 24 mar 2025
Ricevuto: 01 nov 2024
Accettato: 14 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0705
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© 2025 Cheng Lyu, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

The Basic Steps of Data Mining
The Basic Steps of Data Mining

Figure 2.

The architecture of the 3DBP-TBR
The architecture of the 3DBP-TBR

Figure 3.

The dynamic change of learning efficiency of different teaching behavior mode
The dynamic change of learning efficiency of different teaching behavior mode

Figure 4.

Learn the evolution of the harvest
Learn the evolution of the harvest

Figure 5.

Random data set
Random data set

Identification of the pattern of teaching behavior

Serial number Behavior pattern category Recall rate Accuracy rate F1
S1 Stagnation type 0.9735 0.9895 0.9866
S2 Focus type 0.9984 0.9387 0.9682
S3 Drive type 0.9085 0.9748 0.9435
S4 Rhythm type 0.9978 0.9893 0.9954
Mean 0.9696 0.9731 0.9734
Lingua:
Inglese
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