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Design of Learning Progress Tracking and Feedback Mechanism Based on Data Visualisation Technology in Music Teaching

Peihan Lin

Peihan Lin

Department Music and Dance, Jiaying UniversityMeizhou, China
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Lin, Peihan
  
Feb 05, 2025
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Published Online: Feb 05, 2025
Received: Sep 19, 2024
Accepted: Jan 01, 2025
DOI: https://doi.org/10.2478/amns-2025-0047
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© 2025 Peihan Lin, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Common RNN network based on sigmoid
Common RNN network based on sigmoid

Figure 2.

The experimental class learns the behavior path transformation
The experimental class learns the behavior path transformation

Figure 3.

The comparison class learns the behavior path transformation
The comparison class learns the behavior path transformation

Figure 4.

Control group students’ study of attention change
Control group students’ study of attention change

Figure 5.

The students’ attention changes
The students’ attention changes

Figure 6.

Invalid learning behavior time curve
Invalid learning behavior time curve

Figure 7.

The third grade of teaching is a difference
The third grade of teaching is a difference

Descriptive statistics on usage and academic performance

/ N Minimum Maximum Mean Std Skewness Kurtosis
Final test results 232 19 95.4536 63.4875 15.2089 -0.4862 -0.5136
Musical learning 232 14.4868 29.3157 26.1069 1.7636 -3.4064 15.0546
Total score 232 54.4856 93.4845 76.8615 8.2654 -0.2456 -0.4555
Review number 232 1 35 7.1898 4.7652 1.5682 3.5486
Chapter view length (minute) 232 1 115.8596 15.6524 14.2625 2.7264 12.0855
Access to various chapters 232 1 118.7814 15.4983 14.0625 2.8669 12.6558
Usual test results 232 32.4588 95.4525 85.4886 7.6285 -3.4569 16.5878
Learning hours (minutes) 232 678.1958 19728.4852 4266.0899 2348.5496 1.7855 6.5747
Study effort value 232 1498.9876 8312.4886 4765.0865 1186.4848 0.3758 0.4258

Independent sample t test for learning strategy dimensions

/ Group N Mean Std S.E. of mean
The total score of learning strategies Experimental class 232 26.4136 2.8565 0.1985
Control class 243 19.229 2.9496 0.0356
General method Experimental class 232 4.4986 0.5632 0.0485
Control class 243 3.1659 0.5496 0.0485
Learning help Experimental class 232 4.5263 0.6297 0.0496
Control class 243 3.0896 0.6655 0.0486
Learning plan Experimental class 232 4.2656 0.6868 0.0422
Control class 243 3.1269 0.6148 0.0493
Learning summary Experimental class 232 4.5359 0.7586 0.0489
Control class 243 3.2675 0.6418 0.0385
Learning evaluation Experimental class 232 4.4886 0.7998 0.0364
Control class 243 3.4896 0.6145 0.0487
Learning management Experimental class 232 4.0986 0.5987 0.0386
Control class 243 3.0895 0.6493 0.0485
/ Levene’s test
F P T df
The total score of learning strategies Assumed equal variance 0.2655 0.0154 0.0348 472
Equivariance is not assumed 0.0348 471.5846
General method Assumed equal variance 0.4866 0.0486 0.7856 472
Equivariance is not assumed 0.7855 471.9896
Learning help Assumed equal variance 0.1985 0.0165 0.4652 472
Equivariance is not assumed 0.4652 471.5966
Learning plan Assumed equal variance 0.8462 0.0346 0.3482 472
Equivariance is not assumed 0.3482 471.9645
Learning summary Assumed equal variance 0.2486 0.0165 -0.5278 472
Equivariance is not assumed -0.5278 471.2975
Learning evaluation Assumed equal variance 0.4098 0.0284 -0.5297 472
Equivariance is not assumed -0.5297 471.9663
Learning management Assumed equal variance 1.9486 0.0387 0.3785 472
Equivariance is not assumed 0.3785 471.6869
/ t test for mean identify
P(2-tail) MD SE /
The total score of learning strategies Assumed equal variance 0.0156 0.0145 0.2855
Equivariance is not assumed 0.0156 0.0145 0.2855
General method Assumed equal variance 0.0248 0.0498 0.0563
Equivariance is not assumed 0.0248 0.0498 0.0563
Learning help Assumed equal variance 0.0396 0.0263 0.0587
Equivariance is not assumed 0.0396 0.0263 0.0587
Learning plan Assumed equal variance 0.0189 0.0187 0.0486
Equivariance is not assumed 0.0189 0.0187 0.0486
Learning summary Assumed equal variance 0.0265 -0.0385 0.0636
Equivariance is not assumed 0.0265 -0.0385 0.0636
Learning evaluation Assumed equal variance 0.0478 -0.0348 0.0572
Equivariance is not assumed 0.0478 -0.0348 0.0572
Learning management Assumed equal variance 0.0158 0.0289 0.0628
Equivariance is not assumed 0.0158 0.0289 0.0628
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