Physiological Data and Learning Analytics: Opportunities and Challenges for Research and Practice

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Abstract

Wearable technology that monitors physiological data through non-invasive techniques has become popular in mainstream consumer markets. In education, the use of physiological data as one or part of other Learning Analytic (LA) tools is still marginal. Considering the fast path of technological development in the field of wearable technology and physiological data, we consider necessary to foster discussion among the education research community about the opportunities and challenges that physiological data might have for research and practice in learning. The expert panel will highlight topics related to self-monitored body generated data in the context of learning. In the panel, we introduce most popular biomarkers related to learning and identify main approaches regarding the use of this kind of data in education. The panel consists in a group interview of the participating experts from the fields relevant in the emerging transdisciplinary theme. Furthermore the panel will be open for discussion with the audience. In order to foster debate and enable participation, we adopt a conversation format called fishbowl discussion. The topics discussed during the panel will help learning researchers and practitioners to take more informed decisions and identify areas that are interesting for further exploration regarding the inclusion of physiological data in LA.

Details

Original languageEnglish
Publication statusPublished - 2016
EventInternational Learning Analytics and Knowledge Conference - University of Edinburgh, Edinburgh, United Kingdom
Duration: 25 Apr 201629 Apr 2016
Conference number: 6
http://lak16.solaresearch.org/

Conference

ConferenceInternational Learning Analytics and Knowledge Conference
Abbreviated titleLAK
CountryUnited Kingdom
CityEdinburgh
Period25/04/201629/04/2016
Internet address

    Research areas

  • Physiological data, Learning Analytics, Biomarkers, wearable technology, self-monitoring, Quantified-Self

ID: 8624131