A systematic review for Mooc dropout prediction from the perspective of machine learning
Bibliographic Data
| ID | 21632550 |
|---|---|
| Authors | Jing Chen (0000-0002-7243-0806, College of Transportation Engineering, Chang'an University, Xi'an, People's Republic of China), Bei Fang (0000-0002-3461-7995, Shaanxi Normal University), Hao Zhang (0000-0003-0232-5565, Graduate School, Shaanxi University of Chinese Medicine, Xianyang, People's Republic of China), Xia Xue (0000-0002-5783-1281, Maths and Information Technology School, Yuncheng University, Yuncheng, People's Republic of China) |
| Year | 2022 |
| Pages | 1-14 |
| Publication date | 2022-09-23 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Interactive Learning Environments (JOURNAL) |
| Journal identifiers | ISSN: 1049-4820 • E-ISSN: 1744-5191 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10494820.2022.2124425 |
| Language | EN |
| Citations received | 5 |
| References cited | 45 |
A systematic review of Mooc platforms, evaluation frameworks and development of integrated model
To what extent has machine learning achieved in predicting online at-risk students? Evidence from quantitative meta-analysis
Unified Mooc utilization model (Umum) for exploring computer-supported learning with educational technology in terms of Mooc utilization in higher education
Predicting students engagement in asynchronous online learning
Applications of Machine Learning for at‐Risk Student Prediction in Online Education
| Citations per year | 5 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |