Research on the Innovative Model of English Teaching by Integrating Traditional Culture and Artificial Intelligence
Sep 29, 2025
About this article
Published Online: Sep 29, 2025
Received: Dec 24, 2024
Accepted: Apr 21, 2025
DOI: https://doi.org/10.2478/amns-2025-1090
Keywords
© 2025 Weiwei Sheng, published by Sciendo.
This work is licensed under the Creative Commons Attribution 4.0 International License.
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AUC values of DKVMN-F variants on different datasets
Model | ASSISTments2009 | Statics2011 | ASSISTments2012 | Synthetic-5 |
---|---|---|---|---|
DKVMN-F (basic) | 0.8288 | 0.8386 | 0.7412 | 0.8406 |
DKVMN-F (without forget) | 0.8481 | 0.8586 | 0.7560 | 0.8610 |
DKVMN-F (without learning) | 0.8463 | 0.8587 | 0.7519 | 0.8567 |
DKVMN-F | 0.8669 | 0.8670 | 0.7757 | 0.8701 |
Comparison of model performance on ASSISTments2009
Metric | MAP | Precision@1 | Precision@5 | Precision@10 | Recall@1 | Recall@5 | Recall@10 |
---|---|---|---|---|---|---|---|
Model | |||||||
Caser | 0.0303 | 0.0490 | 0.0451 | 0.0432 | 0.0028 | 0.0135 | 0.0249 |
RCNN | 0.0327 | 0.0489 | 0.0481 | 0.0491 | 0.0022 | 0.0131 | 0.0269 |
CosRec | 0.0375 | 0.0591 | 0.0544 | 0.0515 | 0.0038 | 0.0177 | 0.0354 |
SCosRec | 0.0394 | 0.0604 | 0.0572 | 0.0537 | 0.0036 | 0.0211 | 0.0367 |
HANN | 0.0431 | 0.0661 | 0.0614 | 0.0565 | 0.0041 | 0.0218 | 0.0389 |
Comparison of AUC on different data sets
Model | ASSISTments2009 | Statics2011 | ASSISTments2012 | Synthetic-5 |
---|---|---|---|---|
DKT | 0.7437 | 0.8142 | 0.7197 | 0.7598 |
DKT+forget | 0.7528 | 0.7540 | 0.7354 | 0.7621 |
DKVMN | 0.8254 | 0.8395 | 0.7362 | 0.8370 |
LFKT | 0.8569 | 0.8619 | 0.7605 | 0.8614 |
Bi-CLKT | 0.8549 | 0.8628 | 0.7617 | 0.8646 |
DKVMN-F | 0.8669 | 0.8670 | 0.7757 | 0.8701 |
Comparison of model performance on ML-1M
Metric | MAP | Precision@1 | Precision@5 | Precision@10 | Recall@1 | Recall@5 | Recall@10 |
---|---|---|---|---|---|---|---|
Model | |||||||
Pop | 0.0694 | 0.1279 | 0.1122 | 0.1009 | 0.0055 | 0.0225 | 0.0369 |
BPR | 0.0914 | 0.1472 | 0.1292 | 0.1183 | 0.0067 | 0.0301 | 0.0564 |
FPMC | 0.1033 | 0.2004 | 0.1674 | 0.1448 | 0.0132 | 0.0454 | 0.0772 |
GRU4rec | 0.1435 | 0.2512 | 0.2135 | 0.1914 | 0.0158 | 0.0618 | 0.1094 |
Caser | 0.1512 | 0.2501 | 0.2189 | 0.1994 | 0.0146 | 0.0634 | 0.1118 |
RCNN | 0.1681 | 0.2830 | 0.2491 | 0.2225 | 0.0191 | 0.0728 | 0.1267 |
CosRec | 0.1895 | 0.3297 | 0.2831 | 0.2493 | 0.0211 | 0.0831 | 0.1441 |
SCosRec | 0.1969 | 0.3447 | 0.2930 | 0.2585 | 0.0228 | 0.0886 | 0.1526 |
HANN | 0.1971 | 0.3349 | 0.2971 | 0.2641 | 0.0238 | 0.0924 | 0.1613 |
Comparison of model performance on Gowalla
Metric | MAP | Precision@1 | Precision@5 | Precision@10 | Recall@1 | Recall@5 | Recall@10 |
---|---|---|---|---|---|---|---|
Model | |||||||
Pop | 0.0226 | 0.0519 | 0.0359 | 0.0282 | 0.0051 | 0.0272 | 0.0407 |
BPR | 0.0756 | 0.1637 | 0.0983 | 0.0736 | 0.0236 | 0.0747 | 0.1083 |
FPMC | 0.0762 | 0.1556 | 0.0934 | 0.0694 | 0.0248 | 0.0726 | 0.1065 |
GRU4rec | 0.0582 | 0.1049 | 0.0733 | 0.0781 | 0.0151 | 0.0514 | 0.0831 |
Caser | 0.0922 | 0.1955 | 0.1145 | 0.0575 | 0.0315 | 0.0858 | 0.1228 |
RCNN | 0.0768 | 0.1767 | 0.0973 | 0.0737 | 0.0269 | 0.0708 | 0.1030 |
CosRec | 0.0982 | 0.2139 | 0.1187 | 0.0872 | 0.0335 | 0.0878 | 0.1305 |
SCosRec | 0.1011 | 0.2192 | 0.1192 | 0.0892 | 0.0343 | 0.0918 | 0.1317 |
HANN | 0.1027 | 0.2199 | 0.1246 | 0.0918 | 0.0367 | 0.0948 | 0.1347 |