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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/7525
Title: Learning Pulse: Using Wearable Biosensors and Learning Analytics to Investigate and Predict Learning Success in Self-regulated Learning
Authors: Di Mitri, Daniele
Scheffel, Maren
Drachsler, Hendrik
Börner, Dirk
Ternier, Stefaan
Specht, Marcus
Keywords: learning analytics
biosensors
affective computing
wearable enhanced learning
Issue Date: 2016
Publisher: CEUR
Citation: Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S., & Specht, M. (2016). Learning Pulse: Using Wearable Biosensors and Learning Analytics to Investigate and Predict Learning Success in Self-regulated Learning. In R. Martinez-Maldonado & D. Hernandez-Leo (Eds.), Proceedings of the First International Workshop on Learning Analytics Across Physical and Digital Spaces, Vol. 1601 (pp. 34-39): CEUR Proceedings
Abstract: The Learning Pulse study aims to explore whether physiological data such as heart rate and step count correlate with learning activity data and whether they are good predictors for learning success during self-regulated learning. To verify this hypothesis an experiment was set up involving eight doctoral students at the Open University of the Netherlands. Through wearable sensors, heart rate and step count were constantly monitored and learning activity data were collected. All data were stored in a Learning Record Store in xAPI format. Additionally, with an Activity Rating Tool, the participants rated their learning and working experience by indicating the perceived levels of productivity, stress, challenge and abilities along with the type of activity. These human annotated labels can be used for supervising machine learning algorithms to discriminate the successful learning moments from the unsuccessful ones and eventually discover the attributes that most influence the learning process.
URI: http://hdl.handle.net/1820/7525
Appears in Collections:1. TELI Publications, books and conference papers

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