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

Author

Scheffel, Maren ; Wolpers, Martin ; Beer, Frank

Author_Institution

Fraunhofer Inst. for Appl. Inf. Technol. FIT, St. Augustin, Germany

fYear

2010

fDate

5-7 July 2010

Firstpage

125

Lastpage

129

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.

Keywords

computer aided instruction; data analysis; human computer interaction; meta data; psychology; rough set theory; contextualized attention metadata analysis; learner computer interaction; rough set methodology; self reflection process; self regulated learning; Approximation methods; Computer aided manufacturing; Computers; Context; Electronic mail; Fires; Set theory; Rough Set Theory; attention metadata; behavioral similarities; classification; concept approximation; object-relational database system; self-reflection; self-regulated learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Learning Technologies (ICALT), 2010 IEEE 10th International Conference on

Conference_Location

Sousse

Print_ISBN

978-1-4244-7144-7

Type

conf

DOI

10.1109/ICALT.2010.43

Filename

5571192