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Using Keystroke Analytics to Improve Pass–Fail Classifiers
Casey, Kevin
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.
Keyword(s): Learning analytics; keystroke analytics; data mining; virtual learning environments; student behaviour; early intervention
Publication Date:
2017
Type: Journal article
Peer-Reviewed: Yes
Institution: Maynooth University
Citation(s): Casey, Kevin (2017) Using Keystroke Analytics to Improve Pass–Fail Classifiers. Journal of Learning Analytics, 4 (2). pp. 189-211. ISSN 1929-7750
Publisher(s): UTS Press
File Format(s): other
Related Link(s): http://mural.maynoothuniversity.ie/10183/1/KC-Keystroke-2017.pdf
First Indexed: 2020-04-02 06:17:26 Last Updated: 2020-04-02 06:17:26