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How Can We Study Learning with Geovisual Analytics Applied to Statistics?
AbstractIt is vital to understand what kind of processes for learning that Geovisual Analytics creates, as certain activities and conditions are produced when employing Geovisual Anlytic tools in education. To understand learning processes created by Geovisual Analytics, first requires an understanding of the interactions between the technology, the workplace where the learning takes place, and learners’ specific knowledge formation. When studying these types of interaction it demands a most critical consideration from theoretical perspectives on research design and methods. This paper first discusses common, and then a more uncommon, theoretical approach used within the fields of learning with multimedia environments and Geovisual Analytics, the socio-cultural theoretical perspective. The paper next advocates this constructivist theoretical and empirical perspective when studying learning with multiple representational Geovisual Analytic tools. To illustrate, an outline of a study made within this theoretical tradition is offered. The study is conducted in an educational setting where the Open Statistics eXplorer platform is used. Discussion of our study results shows that the socio-cultural perspective has much to offer in terms of what kind of understanding can be reached in conducting this kind of studies. Therefore, we argue that empirical research to analyze how specific communities use various Geovisual Analytics to evaluate information is best positioned in a socio-cultural theoretical perspective.
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Stenliden, L.; Jern, M. How Can We Study Learning with Geovisual Analytics Applied to Statistics? Future Internet 2012, 4, 22-41.View more citation formats
Stenliden L, Jern M. How Can We Study Learning with Geovisual Analytics Applied to Statistics? Future Internet. 2012; 4(1):22-41.Chicago/Turabian Style
Stenliden, Linnea; Jern, Mikael. 2012. "How Can We Study Learning with Geovisual Analytics Applied to Statistics?" Future Internet 4, no. 1: 22-41.