An Effective Student Grouping and Course Recommendation Strategy Based on Big Data in Education

Autor: Yu Guo, Yue Chen, Yuanyan Xie, Xiaojuan Ban
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Information, Vol 13, Iss 4, p 197 (2022)
Druh dokumentu: article
ISSN: 13040197
2078-2489
DOI: 10.3390/info13040197
Popis: Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students’ cooperation ability and lifelong learning ability. Based on students’ interests, this paper proposes an effective student grouping strategy and group-oriented course recommendation method, comprehensively considering characteristics of students and courses both from a statistical dimension and a semantic dimension. First, this paper combines term frequency–inverse document frequency and Word2Vec to preferably extract student characteristics. Then, an improved K-means algorithm is used to divide students into different interest-based study groups. Finally, the group-oriented course recommendation method recommends appropriate and quality courses according to the similarity and expert score. Based on real data provided by junior high school students, a series of experiments are conducted to recommend proper social practical courses, which verified the feasibility and effectiveness of the proposed strategy.
Databáze: Directory of Open Access Journals
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