Applications of Machine Learning for at‐Risk Student Prediction in Online Education: A 10‐Year Systematic Review of Literature.
Background: The growth of online education has provided flexibility and access to a wide range of courses. However, the self‐paced and often isolated nature of these courses has been associated with increased dropout and failure rates. Researchers employed machine learning approaches to identify at‐...
| Published in: | Journal of Computer Assisted Learning Vol. 41; no. 4; pp. 1 - 22 |
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| Main Authors: | , , , |
| Format: | questionnaire/scale research systematic review tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Aug2025
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| Online Access: | View this record in EBSCOhost |