Analyzing Contextualized Attention Metadata with Rough Set Methodologies to Support Self-regulated Learning

Authors:
Maren Scheffel;Martin Wolpers;Frank Beer
Affiliations:
-;-;-
Venue:
ICALT '10 Proceedings of the 2010 10th IEEE International Conference on Advanced Learning Technologies
Year:
2010

Citing 0
Cited 4

Demands of modern PLEs and the ROLE approach

EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
Fault detection in embedded system using rough and fuzzy rough sets

Proceedings of the 15th WSEAS international conference on Computers
A dashboard to regulate project-based learning

EC-TEL'12 Proceedings of the 7th European conference on Technology Enhanced Learning
Towards a script-aware monitoring process of computer-supported collaborative learning scenarios

International Journal of Technology Enhanced Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

A learner’s interaction with her computer can be recorded and stored in the format of Contextualized Attention Metadata. The collected data can then be analyzed to support the learner in her self-reflection processes. We present two ways to discover patterns in the collected attention metadata by applying methodologies based on the Rough Set Theory and explain how these results can support a learner when learning in a self-regulated way.