Name
Title | ||
|---|---|---|
An Effective Student Grouping and Course Recommendation Strategy Based on Big Data in Education |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/info13040197 | INFORMATION |
Keywords | DocType | Volume |
study group division, course recommendation, feature vectors, semantic analysis, clustering algorithm | Journal | 13 |
Issue | ISSN | Citations |
4 | 2078-2489 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Guo | 1 | 6 | 5.89 |
| Yue Chen | 2 | 0 | 0.34 |
| Yuanyan Xie | 3 | 0 | 0.34 |
| Xiaojuan Ban | 4 | 71 | 31.34 |