Analyzing contextualized attention metadata with rough set methodologies to support self-regulated learning

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. © 2010 IEEE.