Construction of Multi-Channel Teaching Effect Evaluation System Based on Deep Learning in the Era of Education Informatization
y
26 sept 2025
Acerca de este artículo
Publicado en línea: 26 sept 2025
Recibido: 29 ene 2025
Aceptado: 30 abr 2025
DOI: https://doi.org/10.2478/amns-2025-1085
Palabras clave
© 2025 Jing Ma et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.
Figure 2.
Figure 3.
Figure 4.
Figure 5.
Figure 6.
Figure 7.
Figure 8.
Channel teaching evaluation index system
| Primary indicator | Secondary indicator |
|---|---|
| Background evaluation | Target setting |
| Faculty | |
| Student capacity | |
| Input evaluation | Facility resources |
| Teacher reserve | |
| Course preparation | |
| Process evaluation | Teacher performance |
| Student performance | |
| Programme implementation | |
| Result evaluation | Teacher development |
| Student growth | |
| Overall effect |
The number of questionnaires and the results of experts’ evaluation
| Course name | Questionnaire distribution | Expert evaluation results |
|---|---|---|
| 3D animation design and production | 100 | Excellence |
| China modern history | 100 | Good |
| College students mental health education | 100 | Medium |
| Gem appreciation | 100 | Qualify |
| National music appreciation | 100 | Out of line |
| College students’ artistic appreciation | 100 | Good |
| Tot | 600 |
According to the questionnaire obtained
| Courses | Serial number | Evaluation index | Evaluation grade | Quantitative result | ||||
|---|---|---|---|---|---|---|---|---|
| X1 | X2 | X3 | …… | X12 | ||||
| 3D animation design and production | 1 | 3 | 4 | 3 | …… | 3 | Excellence | 0.95 |
| 2 | 4 | 4 | 3 | …… | 3 | Excellence | 0.95 | |
| 3 | 4 | 3 | 3 | …… | 4 | Excellence | 0.95 | |
| …… | …… | …… | …… | …… | …… | …… | …… | |
| 100 | 3 | 3 | 4 | …… | 3 | Excellence | 0.95 | |
| China modern history | 1 | 4 | 3 | 3 | …… | 3 | Good | 0.85 |
| 2 | 4 | 4 | 3 | …… | 3 | Good | 0.85 | |
| 3 | 3 | 4 | 4 | …… | 3 | Good | 0.85 | |
| …… | …… | …… | …… | …… | …… | …… | …… | |
| 100 | 3 | 4 | 3 | …… | 4 | Good | 0.85 | |
| College students mental health education | 1 | 2 | 2 | 3 | …… | 3 | Medium | 0.75 |
| 2 | 3 | 3 | 2 | …… | 2 | Medium | 0.75 | |
| 3 | 3 | 3 | 3 | …… | 2 | Medium | 0.75 | |
| …… | …… | …… | …… | …… | …… | …… | …… | |
| 100 | 2 | 3 | 2 | …… | 2 | Medium | 0.75 | |
| Gem appreciation | 1 | 1 | 1 | 2 | …… | 1 | Qualify | 0.65 |
| 2 | 1 | 1 | 2 | …… | 2 | Qualify | 0.65 | |
| 3 | 1 | 1 | 1 | …… | 2 | Qualify | 0.65 | |
| …… | …… | …… | …… | …… | …… | …… | …… | |
| 100 | 2 | 2 | 2 | …… | 1 | Qualify | 0.65 | |
| National music appreciation | 1 | 1 | 0 | 1 | …… | 1 | Out of line | 0.3 |
| 2 | 1 | 1 | 0 | …… | 1 | Out of line | 0.3 | |
| 3 | 1 | 0 | 0 | …… | 0 | Out of line | 0.3 | |
| …… | …… | …… | …… | …… | …… | …… | …… | |
| 100 | 0 | 0 | 1 | …… | 0 | Out of line | 0.3 | |
| College students’ artistic appreciation | 1 | 4 | 3 | 2 | …… | 3 | Good | 0.85 |
| 2 | 4 | 2 | 2 | …… | 4 | Good | 0.85 | |
| 3 | 4 | 3 | 3 | …… | 3 | Good | 0.85 | |
| …… | …… | …… | …… | …… | …… | …… | …… | |
| 100 | 4 | 3 | 2 | …… | 2 | Good | 0.85 | |
Comparison of performance indicators of different algorithms
| Algorithm | Training error (RMSE) | Test error (RMSE) | The number of hidden layers of neurons | Training time/s | Test time/s |
|---|---|---|---|---|---|
| GA-RBF | 10.7415 | 10.9745 | 12 | 955.4 | 0.0033 |
| APSO-RBF | 8.1544 | 8.1145 | 11 | 914.2 | 0.0032 |
| Improved PSO-RBF | 7.0025 | 7.0128 | 9 | 905.1 | 0.0028 |
Sample training results
| Courses | Serial number | Actual output | Expected output | Training results | Expert outcome |
|---|---|---|---|---|---|
| College students’ artistic appreciation | 1 | 0.8541 | 0.85 | Good | Good |
| 2 | 0.8564 | 0.85 | Good | Good | |
| 3 | 0.8451 | 0.85 | Good | Good | |
| …… | …… | …… | …… | …… | |
| 100 | 0.8459 | 0.85 | Good | Good |