Using Keystroke Analytics to Improve Pass-Fail Classifiers

Autor/in	Casey, Kevin
Titel	Using Keystroke Analytics to Improve Pass-Fail Classifiers
Quelle	In: Journal of Learning Analytics, 4 (2017) 2, S.189-211 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Keyboarding (Data Entry); Educational Research; Data Collection; Data Analysis; Pass Fail Grading; Computer Assisted Testing; Programming; At Risk Students; Classification; Accuracy; Prediction; Identification; Low Achievement; Early Intervention; Undergraduate Students; Computer Science Education; Correlation; Foreign Countries; Ireland (Dublin) + Suchen Sie Ihr Suchwort? Texterfassung; Bildungsforschung; Pädagogische Forschung; Data capture; Datensammlung; Auswertung; Programmierung; Classification system; Klassifikation; Klassifikationssystem; Vorhersage; Identifikation; Identifizierung; Unterdurchschnittliche Leistung; Computer science lessons; Informatikunterricht; Korrelation; Ausland
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. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01