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Autor/inn/enDing, Xinyi; Larson, Eric C.; Doyle, Amanda; Donahoo, Kevin; Rajgopal, Radhika; Bing, Eric
TitelEduAware: Using Tablet-Based Navigation Gestures to Predict Learning Module Performance
QuelleIn: Interactive Learning Environments, 29 (2021) 5, S.720-732 (13 Seiten)Infoseite zur Zeitschrift
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ZusatzinformationORCID (Ding, Xinyi)
Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1049-4820
DOI10.1080/10494820.2019.1609524
SchlagwörterHandheld Devices; Educational Technology; Navigation (Information Systems); Learning Modules; College Students; Cancer; Screening Tests; Learning Analytics; Prediction; Self Evaluation (Individuals); Accuracy; Performance Factors; Adult Education; Responses
AbstractIn this paper, we develop a context-aware, tablet-based learning module for adult education. Specifically, we focus on adult education in healthcare-teaching learners to perform a medical screening procedure. Based upon how learners navigate through the learning module (e.g. swipe-speed and click duration, among others), we use machine learning to predict what comprehensive test questions a user will answer correctly or incorrectly. Compared with other context aware learning applications, this is the first time tablet-based navigation gestures have been used to support learning assessment. We conducted a 21 participant user study, showing the system can predict how users respond to test questions with 87.7% accuracy without user-specific calibration data. This is compared to item response theory methods which achieve about 70% accuracy. We also investigate which attributes are most responsible for making predictions. (As Provided).
AnmerkungenRoutledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2024/1/01
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