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:	https://doi.org/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
URI:	https://learning-analytics.info/journals...
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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