Intelligent recommendation system for College English courses based on graph convolutional networks

With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes...

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Veröffentlicht in:Heliyon Jg. 10; H. 8; S. e29052
Hauptverfasser: Lilan, Chen, Zhong, Jianqi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Elsevier Ltd 30.04.2024
Elsevier
Schlagworte:
algorithms
artificial intelligence
data analysis
Data science applications in education
Distance education and online learning
exhibitions
graph paper
graphs
Human-computer interface
learning
Learning communities
neural networks
problem solving
students
Teaching/learning strategies
universities
Learning communities
Data science applications in education
Teaching/learning strategies
Distance education and online learning
Human-computer interface
ISSN:2405-8440, 2405-8440
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Zusammenfassung:With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students’ major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed. Then, the College English texts are taken as feature input, and multi-layer graph convolutional neural network is used to process the above graph neural network structure. Attention mechanism is introduced to enhance the representation of graph features in College English skills. Finally, multi-layer attention model is used to process the courses that users have learned, and intelligent course recommendation is made by combining the multi-layer attention modeling of College English skills. The experimental data show that the proposed method achieves the best performance compared with the commonly used College English course recommendation method.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e29052