Name
Abstract | ||
|---|---|---|
Identifying at-risk students is one of the most important issues in online education. During different stages of a semester, students display various online learning behaviors. Therefore, we propose a phased prediction model to predict at-risk students at different stages of a semester. We analyze students’ individual characteristics and online learning behaviors, extract features that are closely related to their learning performance, and propose combined feature sets based on a time window constraint strategy and a learning time threshold constraint strategy. The results of our experiments show that the precision of the proposed model in different phases is from 90.4 to 93.6%. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10115-019-01374-x | Knowledge and Information Systems |
Keywords | Field | DocType |
Online education, Student performance, Feature extraction, Prediction model, Educational big data mining | Online learning,Educational technology,Computer science,Feature extraction,At-risk students,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
62 | 3 | 0219-1377 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Chen | 1 | 3 | 1.39 |
| Qinghua Zheng | 2 | 1261 | 160.88 |
| Shuguang Ji | 3 | 2 | 0.37 |
| Feng Tian | 4 | 372 | 40.28 |
| Haiping Zhu | 5 | 8 | 1.65 |
| Min Liu | 6 | 335 | 40.49 |