Early warning system as a predictor for student performance in higher education blended courses
Bibliographic Data
| ID | 4097624 |
|---|---|
| Authors | Anjeela Jokhan (Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji), Anjeela D Jokhan (University of the South Pacific), Bibhya Sharma (0000-0002-7519-7712, Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji, corresponding author), Shaveen Singh (0000-0002-7862-8047, Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji) |
| Year | 2019 |
| Volume | 44 |
| Issue | 11 |
| Pages | 1900-1911 |
| Publication date | 2019-11-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Studies in Higher Education (JOURNAL) |
| Journal identifiers | ISSN: 0307-5079 • E-ISSN: 1470-174X |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/03075079.2018.1466872 |
| OpenAlex | W2804257937 |
| Language | EN |
| Citations received | 16 |
| References cited | 8 |
Early warning systems are being used to assist students in their studies as well as understanding student behaviour and performance better. A home-grown EWS plug-in for Moodle was used to predict the student performance in a first year IT literacy course at University of the South Pacific. The alert tool was designed to capture student logins, completion of online activities and online engagement. Data were captured from Moodle and statistical modelling using the regression model was used to determine any correlation between student’s online behaviour and their performance. Student performance in this higher education course could be predicted based on their average logins per week and the average completion rates of activities. The accuracy of the model was 60.8%. Hence the EWS can be a very useful tool to measure student progression in a course as well as identifying underperforming students early in their course of allowing for early intervention
Blended Learning · Data science · Educational technology · Higher education · Learning analytics · Machine learning · Mathematics education · Medical education · Performance indicator · Regression analysis · Computer Science · Medicine · Online and Blended Learning · Online Learning and Analytics · Psychology · Software System Performance and Reliability
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| Unique citing works | 16 |
|---|---|
| Citations per year | 2,67 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 12 |