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Analysing contextualized attention metadata for self-regulated learning

A supporting framework for self-monitoring and self-reflection
: Scheffel, M.; Beer, F.; Wolpers, M.

Postprint urn:nbn:de:0011-n-1510262 (343 KByte PDF)
MD5 Fingerprint: 21558a84105934214a2f895f8f00f525
Erstellt am: 20.1.2011

Cordeiro, J. ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
CSEDU 2010, 2nd International Conference on Computer Supported Education. Proceedings. Vol.1 : Valencia, Spain, April 7-10, 2010
Setúbal: INSTICC, 2010
ISBN: 978-989-674023-8
International Conference on Computer Supported Education (CSEDU) <2, 2010, Valencia>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FIT ()
usage metadata; attention analysis; self-regulated learning; self-reflection; self-monitoring; software framework; personalised learning software; personal learning environment

In order to successfully learn in a self-regulated way, self-monitoring of the learner and reflection of learning behaviour is required. We therefore introduce a framework that collects usage metadata from application programs and stores them as Contextualized Attention Metadata (CAM). We also present three approaches on how we exploit the collected CAM for further analysis such as object recommendation or learning activity classification in order to help the learner become aware of her learning behaviour, to self-reflect and to support her during her learning processes.