Fitbit for learning

Towards capturing the learning experience using wearable sensing

Bibliographic Data

ID21642902
AuthorsMichail N Giannakos (0000-0002-8016-6208, Norwegian University of Science and Technology, corresponding author), Kshitij Sharma (0000-0003-3364-637X, Norwegian University of Science and Technology), Sofia Papavlasopoulou (0000-0002-1974-0522, Norwegian University of Science and Technology), Ilias O Pappas (0000-0001-7528-3488, University of Agder), Vassilis Kostakos (0000-0003-2804-6038, The University of Melbourne)
Year2020
Volume136
Pages102384
Publication date2020-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Human-Computer Studies (JOURNAL)
Journal identifiersISSN: 1071-5819 • E-ISSN: 1095-9300
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.ijhcs.2019.102384
OpenAlexW2996667664
LanguageEN
Citations received10
References cited50

The assessment of learning during class activities mostly relies on standardized questionnaires to evaluate the efficacy of the learning design elements. However, standardized questionnaires pose additional strain on students, do not provide “temporal” information during the learning experience, require considerable effort and language competence, and sometimes are not appropriate. To overcome these challenges, we propose using wearable devices, which allow for continuous and unobtrusive monitoring of physiological parameters during learning. In this paper we set out to quantify how well we can infer students’ learning experience from wrist-worn devices capturing physiological data. We collected data from 31 students in 93 class sessions (3 class sessions per student), and our analysis shows that wrist data can predict the learning experience with 11% error. We also show that 6.25 min (SD = 3.1 min) of data are needed to achieve a reliable estimate (i.e., 13.8% error). Our work highlights the benefits and limitations of utilizing wearable devices to assess learning experiences. Our findings help shape the future of quantified-self technologies in learning by pointing out the substantial benefits of physiological sensing for self-monitoring, evaluation, and metacognitive reflection in learning

Human–computer interaction · Machine learning · Wearable computer · Wearable technology · Computer Science · Innovative Teaching and Learning Methods · Online and Blended Learning · Online Learning and Analytics · Psychology · Artificial Intelligence

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Open DOISci-HubOpen Access