Processing and visualizing data in complex learning environments

Article

Thompson, Kate, Ashe, David, Carvalho, Lucila, Goodyear, Peter, Kelly, Nick and Parisio, Martin. 2013. "Processing and visualizing data in complex learning environments." American Behavioral Scientist. 57 (10), pp. 1401-1420. https://doi.org/10.1177/0002764213479368
Article Title

Processing and visualizing data in complex learning environments

ERA Journal ID6073
Article CategoryArticle
AuthorsThompson, Kate (Author), Ashe, David (Author), Carvalho, Lucila (Author), Goodyear, Peter (Author), Kelly, Nick (Author) and Parisio, Martin (Author)
Journal TitleAmerican Behavioral Scientist
Journal Citation57 (10), pp. 1401-1420
Number of Pages20
Year2013
Place of PublicationThousand Oaks, CA. United States
ISSN0002-7642
1552-3381
Digital Object Identifier (DOI)https://doi.org/10.1177/0002764213479368
Web Address (URL)http://abs.sagepub.com/content/57/10/1401
Abstract

The ability to capture large amounts of data that describe the interactions of learners becomes useful when one has a framework in which to make sense of the processes of learning in complex learning environments. Through the analysis of such data, one is able to understand what is happening in these networks; however, deciding which elements will be of most interest in a specific learning context and how to process, visualize, and analyze large amounts of data requires the use of analytical tools that adequately support the phases of the research process. In this article, we discuss the selection, processing, visualization, and analysis of multiple elements of learning and learning environments and the links between them. We discuss, using the cases of two learning environments, how structure affects the behavior of learners and, in turn, how that behavior has the potential to affect learning. This approach will allow us to suggest possible ways of improving future designs of learning environments.

Keywordsdesign for learning; learning analytics; learning environments; online learning; CSCL
ANZSRC Field of Research 2020490501. Applied statistics
461002. Human information behaviour
390409. Learning sciences
Public Notes

© 2013 SAGE Publications. Published version deposited in accordance with the copyright policy of the publisher.

Byline AffiliationsUniversity of Sydney
Institution of OriginUniversity of Southern Queensland
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