Curriculum Content Construction and Updating for Intelligent Educational Systems Using Knowledge Graphs
26 set 2025
INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 26 set 2025
Ricevuto: 21 gen 2025
Accettato: 02 mag 2025
DOI: https://doi.org/10.2478/amns-2025-1075
Parole chiave
© 2025 Lina Fan, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Comparison results
No. | Number of questions | The number of answers returned | The correct number of answers | Accuracy | Precision |
---|---|---|---|---|---|
1 | 120 | 108 | 95 | 79.2% | 87.96% |
2 | 120 | 103 | 89 | 74.2% | 86.41% |
3 | 120 | 91 | 84 | 70.0% | 92.31% |
4 | 120 | 96 | 85 | 70.8% | 88.54% |
5 | 120 | 106 | 91 | 75.8% | 85.85% |
6 | 120 | 110 | 97 | 80.8% | 88.18% |
7 | 120 | 107 | 96 | 80.0% | 89.72% |
8 | 120 | 97 | 90 | 75.0% | 92.78% |
9 | 120 | 105 | 98 | 81.7% | 93.33% |
10 | 120 | 89 | 82 | 68.3% | 92.13% |
Average | 120 | 101.2 | 90.7 | 75.6% | 89.62% |
Test results of different models
Model | Precision (%) | Recall (%) | F1 (%) |
---|---|---|---|
CRF | 73.22 | 66.08 | 69.47 |
Bi_LSTM | 82.28 | 75.23 | 78.60 |
Bi_LSTM+CRF | 87.35 | 87.63 | 87.49 |
BERT | 94.69 | 95.33 | 95.01 |
Ours |
Experimental comparison of various models (%)
Sorting algorithm | Macro accuracy | Macro recall | Macro F1 |
---|---|---|---|
SVM | 87.36 | 85.81 | 86.58 |
KNN | 86.84 | 89.38 | 88.09 |
Ours | 91.54 | 92.22 | 91.88 |
Physical identification of experimental parameters
Experimental parameters | Value |
---|---|
train_batch_size | 48 |
eval_batch_size | 64 |
max_seq_length | 64 |
num_train_epochs | 10 |
drop_out | 0.5 |
embedding_size | 752 |
learning_rate | 5.0×10-5 |
hidden_dropout_prob | 48 |
Classifier experimental effect (%)
Question type | Precision | Recall | F1 |
---|---|---|---|
Factual type | 92.22 | 92.63 | 92.42 |
Statistical type | 92.63 | 93.48 | 93.05 |
Whether or not type | 91.82 | 92.54 | 92.18 |
List type | 91.51 | 91.29 | 91.40 |
Method type | 89.61 | 90.17 | 89.89 |