Analyzing Learners Behavior in MOOCs: An Examination of Performance and Motivation Using a Data-Driven Approach
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 941
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2018.2876755
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 73669
- Volume
- 6
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2018
- 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
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work represents an international collaborative output to analyse learner behaviour in Massive Open Online Courses (MOOCs). For the first time, the link between behavioural engagement and motivation as predictors of learning outcomes is explored using statistical analysis and machine learning algorithms. The work was presented as a keynote talk at the Springer technically sponsored 1st Applied Computing Research Innovation and Technology Conference (ACRIT 2019), Ramadi City, Iraq, and ACM technically sponsored 2019 International Conference on Information and Communication, Baghdad, Iraq.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
