The personal equation of interaction for interface learning: Predicting the performance of visual analysis through the assessment of individual differences

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Cognitive science
Visual analytics
Data visualization
Interface learning
Individual differences
Laboratory studies

The Personal Equation of Interaction (PEI) for Interface Learning is a short self-report psychometric measure which predicts reasoning outcomes of interface learning such as accurate target identification and insights garnered through and inferred from learning interaction. By predicting outcomes, we consider why some interfaces are more appropriate than others, provide a tool for intuitive interface design, and advance the pursuit and design of interface individuation. Through study designs which use comparative interfaces and simple but imperative tasks to any interface learning, such as target identification and inferential learning, we evaluate the accuracy of analysts and how it is impacted by graphical representation. By using psychometric items culled from normed trait assessment, we have created a measure which predicts accuracy and learning, called the Personal Equation of Interaction. This prediction tool can be used in a variety of ways, including as a function or equation that puts a number on the association between analyst and interface. We also use the PEI to build profiles of analyst expert cohorts and discuss how its use might impact Visual Analytics.

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This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
Brian Fisher
Communication, Art & Technology: School of Interactive Arts and Technology
Thesis type: 
(Dissertation) Ph.D.