Design and Application of English Language and Literature Smart Classroom Based on Artificial Intelligence Technology
03 lut 2025
O artykule
Data publikacji: 03 lut 2025
Otrzymano: 21 wrz 2024
Przyjęty: 07 sty 2025
DOI: https://doi.org/10.2478/amns-2025-0007
Słowa kluczowe
© 2025 Zhi Zhang, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.

Figure 2.

Figure 3.

Figure 4.

Test results of the English language literature before experiment
Class | N | Mean | S.D. | S.E. | |
---|---|---|---|---|---|
Grade | Class 1 | 47 | 42.56 | 5.712 | 1.052 |
Class 2 | 48 | 43.07 | 5.324 | 1.127 | |
Independent sample t test differences | |||||
The variance of the Levene test | T test of the mean equation | ||||
F | Sig. | T | df | Sig. (Double tail) | |
Equal variance | 1.037 | 0.286 | -0.068 | 94 | 0.853 |
Unequal variance | -0.068 | 93.286 | 0.853 |
The comparison results of the model on QWK in different text characteristics
Prompts | LSTMsem | LSTMisem+simple | LSTMsem+synt | LSTMsem+topic | LSTMAll |
---|---|---|---|---|---|
1 | 0.838 | 0.884 | 0.871 | 0.849 | 0.859 |
2 | 0.794 | 0.865 | 0.843 | 0.802 | 0.807 |
3 | 0.728 | 0.815 | 0.742 | 0.758 | 0.762 |
4 | 0.743 | 0.835 | 0.752 | 0.787 | 0.827 |
5 | 0.755 | 0.823 | 0.762 | 0.784 | 0.808 |
6 | 0.771 | 0.833 | 0.786 | 0.795 | 0.824 |
7 | 0.788 | 0.835 | 0.795 | 0.801 | 0.815 |
8 | 0.673 | 0.793 | 0.696 | 0.775 | 0.781 |
Average | 0.761 | 0.835 | 0.781 | 0.794 | 0.810 |
Test results of the English language literature after experiment
Class | N | Mean | S.D. | S.E. | |||
---|---|---|---|---|---|---|---|
Grade | Class 1 | 47 | 45.82 | 3.862 | 0.725 | ||
Class 2 | 48 | 43.54 | 5.238 | 1.153 | |||
Correlation analysis of class 1 | |||||||
Mean | N | S.D. | S.E. | Correlation | Sig. | ||
Class 1 | Before | 42.56 | 47 | 5.712 | 1.052 | 0.528 | 0.001 |
After | 45.82 | 48 | 3.862 | 0.725 | |||
Independent sample t test of class 1 | |||||||
F | Mean | S.D. | S.E. | T | df | Sig. (Double tail) | |
Class 1 | Before-after | -3.260 | 5.315 | 1.032 | -2.175 | 46 | 0.022 |
Different models of the QWK evaluation index
Prompts | LSTM-MoT | CNN-CNN-MoT | CNN-LSTM-ATT | Improved LSTM |
---|---|---|---|---|
1 | 0.816 | 0.804 | 0.823 | 0.859 |
2 | 0.747 | 0.738 | 0.799 | 0.807 |
3 | 0.672 | 0.683 | 0.693 | 0.762 |
4 | 0.744 | 0.773 | 0.805 | 0.827 |
5 | 0.729 | 0.712 | 0.793 | 0.808 |
6 | 0.815 | 0.811 | 0.815 | 0.824 |
7 | 0.734 | 0.787 | 0.802 | 0.815 |
8 | 0.647 | 0.739 | 0.747 | 0.781 |
Average | 0.738 | 0.756 | 0.785 | 0.810 |
Statistical analysis of various results
Type | N | Mean | S.D. | S.E. | Sig. (Double tail) | |
---|---|---|---|---|---|---|
Inductive keynote | After | 47 | 9.742 | 3.084 | 0.534 | 0.002 |
Before | 47 | 7.869 | 1.094 | 0.233 | ||
Guessing meaning | After | 47 | 7.512 | 3.237 | 0.487 | 0.006 |
Before | 47 | 6.506 | 2.562 | 0.319 | ||
Look for details | After | 47 | 18.791 | 2.863 | 0.381 | 0.073 |
Before | 47 | 18.587 | 1.273 | 0.206 | ||
Reasoning | After | 47 | 9.776 | 2.385 | 0.283 | 0.081 |
Before | 47 | 9.601 | 1.459 | 0.141 |