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Accepted author manuscript, 342 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 10/08/2018 |
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Host publication | Proceedings - IEEE 18th International Conference on Advanced Learning Technologies, ICALT 2018 |
Editors | Nian-Shing Chen, Maiga Chang, Ronghuai Huang, K. Kinshuk, Kannan Moudgalya, Sahana Murthy, Demetrios G Sampson |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 191-195 |
Number of pages | 5 |
ISBN (print) | 9781538660492 |
<mark>Original language</mark> | English |
Event | 18th IEEE International Conference on Advanced Learning Technologies, ICALT 2018 - Bombay, India Duration: 9/07/2018 → 13/07/2018 |
Conference | 18th IEEE International Conference on Advanced Learning Technologies, ICALT 2018 |
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Country/Territory | India |
City | Bombay |
Period | 9/07/18 → 13/07/18 |
Conference | 18th IEEE International Conference on Advanced Learning Technologies, ICALT 2018 |
---|---|
Country/Territory | India |
City | Bombay |
Period | 9/07/18 → 13/07/18 |
Students' dropout rate is a key metric in online and open distance learning courses. We propose a time-series classification method to construct data based on students' behaviour and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.