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Casey, Kevin (2017) Using Keystroke Analytics to Improve Pass–Fail Classifiers. Journal of Learning Analytics, 4 (2). pp. 189-211. ISSN 1929-7750
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Abstract
Learning analytics offers insights into student behaviour and the potential to detect
poor performers before they fail exams. If the activity is primarily online (for example computer
programming), a wealth of low-level data can be made available that allows unprecedented
accuracy in predicting which students will pass or fail. In this paper, we present a classification
system for early detection of poor performers based on student effort data, such as the
complexity of the programs they write, and show how it can be improved by the use of low-level
keystroke analytics.
| Item Type: | Article |
|---|---|
| Additional Information: | The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0). Please refer to the Journal's Copyright notice for more details: https://learning-analytics.info/journals/index.php/JLA/about/submissions#copyrightNotice |
| Keywords: | Learning analytics; keystroke analytics; data mining; virtual learning environments; student behaviour; early intervention; |
| Academic Unit: | Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 10183 |
| Identification Number: | 10.18608/jla.2017.42.14 |
| Depositing User: | Hamilton Editor |
| Date Deposited: | 07 Nov 2018 15:24 |
| Journal or Publication Title: | Journal of Learning Analytics |
| Publisher: | UTS Press |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/10183 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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