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    Using Keystroke Analytics to Improve Pass–Fail Classifiers


    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:

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