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TopInformation visualisation (I.V.) uses visuals to enhance the user’s interaction with information. It is seen as a particular type of visual which aims to amplify cognition. As Card et al. (1999) suggest, it is the visualization through interactive and visual representation of abstract data that provides clarity, that aids meaning making, and one of its big challenges is to create representations of complex information structures in comprehensible form (Judelman, 2004). In terms of learning, information visualisation strives to overcome the complexity of data by increasing resources to the human in the form of memory and processing resources. As Card says “visualisation helps the user by making the world outside the mind a resource for thought in fairly specific ways” (cited in Jacko & Sears, 2003, p.551). Information visualisation is about being enticed to engage with visual content, for instance information; it is about understanding and interpreting, about creating something new and then possibly acting upon it. The important outcome is that the user has visually interacted with the content and has formed a convincing interpretation and understanding of it. However, there still is “a big gap in visualisation discourse between science, art, technology and design” (Judelman, 2004, p.1). As Card emphasises, to be a good information visualisation, the mappings are cognitive and must encode all of the data relations intended and no other data relations. The outcomes of information visualisation are defined: they depend on a certain amount of control to address the problem of how to use the visualisation to create an efficient and useful tool for information exploration. But as research has shown, it has become more than just that, as Judelman (2004, p.6) agrees “the challenges facing information visualisation researchers often involve finding innovative graphic and interactive techniques to represent the complexity of information structures.”