A Personalized English Learning Material Recommendation System Based on Knowledge Graph

Yiqin Huang, Jiang Zhu

Abstract


The world has rushed into the information age. As the lingua franca, English shapes the global landscape of information transmission and exchange. Mastering English is equivalent to possessing an important tool for acquiring precious information. Therefore, it is very necessary to improve English teaching. This paper analyzes the problems in traditional classroom teaching and online learning of English, and discusses how to keep students unbaffled in online learning, improve their English learning efficiency, and satisfy their personalized demands. Specifically, the relevant data were characterized by knowledge points in English teaching, and used to formulate a knowledge graph. Then, the related knowledge points were labeled in online learning data on learning platforms. After that, user portrait was created by analyzing the data on daily learning behaviors. Finally, collaborative filtering was coupled with content-based recommendation to push English learning resources to students, which meet their personalized demands.

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Copyright (c) 2021 Yiqin Huang, Jiang Zhu


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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