Immersive Analytics Interaction: User Preferences and Agreements by Task Type
Date
2018-05-10
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Abstract
For immersive computing environments, multiple interaction modes (e.g. voice, gestures, handheld controller) have been proposed. In this thesis, I present the results of an elicitation study examining user preferences and measuring interaction agreements, based on two task types from an existing task taxonomy, in the context of data interaction in augmented reality (AR). The results indicate a non-statistically-significant association between a user’s input mode preference and the type of the performed task in most cases. However, agreements on interactions were found to be higher in one type of task. I reflect on the resulting implications and offer one practical guideline for UX designers creating AR-based analytics applications. This thesis also details an alternative way of quantifying user agreements in an elicitation study on interactions.
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Keywords
speech input, gesture input, elicitation, immersive analytics, augmented reality
Citation
Chen, Q. (2018). Immersive Analytics Interaction: User Preferences and Agreements by Task Type (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/31914