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.
Highlights
As digital economy and artificial intelligence are developing rapidly, our world has rushed into the information age
Based on the knowledge graph constructed in previous text, the resources of online platforms need to be integrated with knowledge points as the main content of the characterization technique, the related knowledge points are used to normalize the specific content of the network resources and process them into characteristics that can be understood by the machine and the algorithm
Combining with the characteristics of English knowledge, this paper analyzed the problem that students often get lost in the massive online learning materials and built an efficient and accurate personalized English learning material recommendation system
Summary
As digital economy and artificial intelligence are developing rapidly, our world has rushed into the information age. Regarding the first issue mentioned above, with English course of a certain grade as an example, this paper analyzed the characteristics of English as a linguistic tool and its knowledge attributes, sorted out the knowledge system structure, and built knowledge graph of the course to describe the knowledge points In such case, students would be able to learn the knowledge structure of the course according to the knowledge graph without the professional guidance of teachers, avoiding them from getting lost in the massive learning resources. As for the second issue, this paper analyzed the user portrait of students in the grade, the learning behavior paths of these students, as well as the specific situations of the online teaching platforms; with the help of knowledge graph, it labeled the related knowledge points of learning data on online learning platforms, constructed the personalized English learning material recommendation system, and researched a few key points such as the system recommendation flow, user portrait, and recommendation algorithm, etc
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