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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

A Study on Distance Personalized English Teaching Based on Deep Directed Graph Knowledge Tracking Model

Author 1: Lianmei Deng

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2023.0140288

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 2, 2023.

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Abstract: Despite the continuous development of online education models, the effectiveness of online distance education has never been able to meet people’s expectations due to individual difference of learners. How to personalize teaching in a targeted manner and stimulate learners’ independent learning ability has become a key issue. In this study, the multidimensional features of the learning process are mined with the help of the BORUTA feature selection model, and the DKVMN-BORUTA model incorporating multidimensional features is established. This optimized deep knowledge tracking method is combined with graph structure rules. Then, an intelligent knowledge recommendation algorithm based on reinforcement learning is used to construct a fusion approach-based model for distanced personalized teaching and learning of English. The results show that the research proposed fused deep-directed graph knowledge tracking with graph structure rules for remote personalized English teaching model has the lowest AUC value of 0.893 and the highest AUC value of 0.921 on each dataset. The prediction accuracy of the research model is 94.3% and the F1 score is 0.92, which is the highest among the studied models, indicating that the proposed model has a strong performance. The fusion model proposed in the study has a higher accuracy rate of knowledge personalization recommendation than the traditional deep knowledge tracking model, and it can help learners save revision time effectively and improve their overall English performance.

Keywords: Distanced personalized English teaching model; knowledge tracking; deep learning; graph structure rules; DKVMN

Lianmei Deng, “A Study on Distance Personalized English Teaching Based on Deep Directed Graph Knowledge Tracking Model” International Journal of Advanced Computer Science and Applications(IJACSA), 14(2), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140288

@article{Deng2023,
title = {A Study on Distance Personalized English Teaching Based on Deep Directed Graph Knowledge Tracking Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140288},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140288},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Lianmei Deng}
}


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