Seeing beyond reading: a survey on visual text analytics

We review recent visualization techniques aimed at supporting tasks that require the analysis of text documents, from approaches targeted at visually summarizing the relevant content of a single document to those aimed at assisting exploratory investigation of whole collections of documents.Techniques are organized considering their target input materialeither single texts or collections of textsand their focus, which may be at displaying content, emphasizing relevant relationships, highlighting the temporal evolution of a document or collection, or helping users to handle results from a query posed to a search engine.We describe the approaches adopted by distinct techniques and briefly review the strategies they employ to obtain meaningful text models, discuss how they extract the information required to produce representative visualizations, the tasks they intend to support and the interaction issues involved, and strengths and limitations. Finally, we show a summary of techniques, highlighting their goals and distinguishing characteristics. We also briefly discuss some open problems and research directions in the fields of visual text mining and text analytics.

Representations as tools of appropriation, creating meaning through activities.

Based on some constructs of the Activity Theory (Leontiev, 1978), we point to the need to develop activities that reveal the meaning of representations. We examine use of representations in teaching and propose some suggestions. Shaaron Ainsworth (1999) asserted that, in order to learn from engaging with multiple representations of scientific concepts, students need to be able to (a) understand the codes and signifiers in a representation, (b) understand the links between the representation and the target concept or process, (c) translate key features of the concept across representations and (d) know which features to emphasize in designing their own representations.