A supervised learning framework: using assessment to identify students at risk of dropping out of a MOOC
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Analysing the tools available would be beneficial for monitoring student retention rates, identifying underachieving students, predicting students' trends and attitudes, and accreditation purposes.
“Analysing the tools available would be beneficial for monitoring student retention rates (Monllaó Olivé et al., 2020), identifying underachieving students (Saqr et al., 2017), predicting students' trends and attitudes, and accreditation purposes (El Tantawi et al., 2015; Saleh & Salama, 2018; Strang, 2016).”
From A systematic review on trends in using Moodle for teaching and learning (2022) · cites this paper as Monllaó Olivé et al., 2020
Ten percent of the articles reviewed discussed the in-built statistical tools such as the facility index and discrimination index along with the databases available in LMSs for the use of educational and research purposes.
“Ten percent of the articles reviewed discussed the in-built statistical tools such as the facility index and discrimination index along with the databases available in LMSs for the use of educational and research purposes (Fenu et al., 2017; Gamage et al., 2019; Monllaó Olivé et al., 2020).”
From A systematic review on trends in using Moodle for teaching and learning (2022) · cites this paper as Monllaó Olivé et al., 2020
Cites
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
Matthews, B. W. (1975) · Biochimica et Biophysica Acta (BBA) - Protein Structure · doi:10.1016/0005-2795(75)90109-9 · Cited by 5,399 · cited 1× · see citing works
“The Machine learning algorithm included in the Machine Learning backend is trained with the training dataset and the test dataset is used to calculate the Mat-thews’ correlation coefficient (Matthews 1975) of the prediction model.”
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A systematic review on trends in using Moodle for teaching and learning
Gamage, S. H. P. W., Ayres, J. R., & Behrend, M. B. (2022) · International Journal of STEM Education · doi:10.1186/s40594-021-00323-x · Cited by 360 · see citing works
Recent trends in computational intelligence for educational big data analysis
Ikegwu, A. C., Nweke, H. F., & Anikwe, C. V. (2024) · Iran Journal of Computer Science · doi:10.1007/s42044-023-00158-5 · Cited by 53 · see citing works
- Authors
- Monllaó Olivé, D., Huynh, D. Q., Reynolds, M., Dougiamas, M., & Wiese, D.
- Affiliations
- The University of Western Australia (AU)
- Venue
- Journal of Computing in Higher Education
- Published
- 2019-05-24
- Volume
- 32
- Issue
- 1
- Pages
- 9-26
- ISSN
- 1042-1726 (ISSN-L)
- 1867-1233
- OpenAlex
- W2944926045
- Cited by
- 64
- References
- 1
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 11 · 2023: 7 · 2022: 11 · 2021: 15 · 2020: 4
- FWCI
- 7.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computing in Higher Education (publishedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- Funders
- Research Training Program
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- student dropout prediction, MOOCs, Moodle, learning analytics, assessment data, artificial neural networks, analytical framework, virtual learning environments, at-risk student identification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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