Use of performance predictors in visual analytics

Visual Analytics is a multi-disciplinary field that uses interactive visualisations to promote and assist the analytic reasoning and generate insights. Understanding the perceptual and cognitive factors is key to the progress in this field. This research focuses on understanding the benefits of interaction in terms of insight generation Moreover, this investigation explores the compounding effects individual differences have with interaction when analysing data to generate insights. This study investigated the individual differences in two sets; psychometric set measures, and a sensorial preferences multimodal learning style. Interaction was analysed from an information visualisation perspective, exploring the Visual Mapping and View Transformation interaction, by isolating interaction as an independent variable. Moreover, the View Transformation experiment used two different visual representations 2D and 3D. Additionally, the individual differences were analysed using the aptitude-by-treatment interaction (ATI) methodology. The ATI approach enabled the assessment of the performance gains in terms of insight generation according to pre-defined set levels of individual differences measures. This thesis confirms the benefits of interaction in generating more insights and increasing their accuracy, whilst facilitating the generation of insights requiring lower mental effort. Further, the results show significant conjoint effects between interaction and individual differences. Furthermore this research revealed a performance difference between 2D and 3D visual representation in the serious game problem solving context. Overall, this thesis provides tangible proof that both visual mapping and view transformation interaction are beneficial to visual analytics in generating insights. Strengthening the view that interaction with the problem-set improves understanding, and the number of insights gleaned into the problem and that more research into the use of individual differences, as a performance predictor in Visual Analytics is beneficial.