Joseph MacInnes

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Humans use intuition and experience to classify everything they perceive, but only if the distinguishing patterns are visible. Machine-learning algorithms can learn class information from data sets, but the created classes' meaning isn't always clear. A proposed mixed-initiative approach combines intuitive visualizations with machine learning to tap into(More)
Numerous investigations have revealed that eye movements and fixation locations differ as a function of how an individual is processing a scene (e.g., Castelhano et al., 2009; Dodd et al., 2009; Land & Hayhoe, 2001; Mills et al., 2011, Yarbus, 1967). As a consequence, a common question of interest is whether a participant's task can be predicted from their(More)
Scaffolding techniques allow human instructors to support novice learners in critical early stages, and to remove that support as expertise grows. This paper describes nAble, an adaptive scaffolding agent designed to guide new users through the use of an analytic software tool in the ‘nSpace Sandbox’ for visual sense-making. nAble adapts the(More)
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