Dropout early warning systems for high school students using machine learning
Bibliographic Data
| ID | 11337107 |
|---|---|
| Authors | Jae Young Chung (0000-0003-0073-4314, Ewha Womans University, corresponding author), Sunbok Lee (0000-0002-0924-7056, University of Houston) |
| Year | 2019 |
| Volume | 96 |
| Pages | 346-353 |
| Publication date | 2019-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Children and Youth Services Review (JOURNAL) |
| Journal identifiers | ISSN: 0190-7409 • E-ISSN: 1873-7765 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.childyouth.2018.11.030 |
| OpenAlex | W2901400593 |
| Language | EN |
| Citations received | 22 |
| References cited | 10 |
Big data · Binary classification · Christian ministry · Data mining · Dropout (neural networks · Early warning system · Machine learning · Mathematics education · National education · Pedagogy · Political science · Random forest · School dropout · Sociology · Support vector machine · The Internet · Warning system · World Wide Web · Computer Science · Online Learning and Analytics · Psychology · Artificial Intelligence
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| Citations per year | 3.67 |
|---|---|
| Citation span | 2020 - 2026 (7) |
| Highly cited | No |