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ERIC Number: EJ1149276
Record Type: Journal
Publication Date: 2017
Pages: 23
Abstractor: As Provided
ISSN: EISSN-1929-7750
Using Keystroke Analytics to Improve Pass-Fail Classifiers
Casey, Kevin
Journal of Learning Analytics, v4 n2 p189-211 2017
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.
Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Descriptive
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Ireland (Dublin)
Grant or Contract Numbers: N/A