Detecting At-Risk Students with Early Interventions Using Machine Learning Techniques
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 957
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2019.2943351
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 149464
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In contrast to previous studies that had identified students at risk of withdrawal from or failing courses using parametric and statistical approaches, this is one of the first works to provide preventative rather than reactive approaches to identifying at-risk students in MOOCs (Massive Open Online Courses). The work is an output of an international collaboration between Computer Science Department at LJMU and Engineering Department at Khalifa University, UAE. It was presented as a keynote talk at the Sixth International Conference on Digital Information, Networking, and Wireless Communications (DINWC2018), University of Lebanon, Lebanon.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
