Design and Application of English Language and Literature Smart Classroom Based on Artificial Intelligence Technology
Feb 03, 2025
About this article
Published Online: Feb 03, 2025
Received: Sep 21, 2024
Accepted: Jan 07, 2025
DOI: https://doi.org/10.2478/amns-2025-0007
Keywords
© 2025 Zhi Zhang, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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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 |