An Intelligent Guide Application for English Online Education Based on Deep Learning

With the development of online education technology, the status of English online education system is also increasing. However, the current intelligent learning guide design lacks self-adaptation and cannot get the learning state of different students. In addition, it is not possible to provide pers...

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Vydáno v:Journal of advanced computational intelligence and intelligent informatics Ročník 29; číslo 3; s. 489 - 499
Hlavní autor: Zhu, Ting
Médium: Journal Article
Jazyk:angličtina
Vydáno: Tokyo Fuji Technology Press Co. Ltd 20.05.2025
Témata:
Algorithms
Customization
Deep learning
Education
Knowledge
Knowledge representation
Students
Tracking
ISSN:1343-0130, 1883-8014
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Shrnutí:With the development of online education technology, the status of English online education system is also increasing. However, the current intelligent learning guide design lacks self-adaptation and cannot get the learning state of different students. In addition, it is not possible to provide personalized learning and topic push for students with different learning states. Therefore, we propose an intelligent guide design for English online education based on deep learning, which combines knowledge tracking algorithm and exercise recommendation algorithm. Our model can extract students’ knowledge state and ability level and recommend appropriate exercises. In addition, we also introduce knowledge graph technology and use knowledge graph embedding technology to preprocess the knowledge points of exercises to enhance the state representation in the model and explore the implicit relationship. Experiments are designed to prove the effectiveness and superiority of our proposed method. Experimental results of DACK model: ACC = 93.1%, AUC = 98.8%, significantly superior to traditional knowledge tracking methods. Experimental results of DRSS model: Hit-Ratio@10 = 41.6%, NDCG@10 = 70.2%, performed excellently in personalized practice recommendations.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2025.p0489