Evaluating Collaborative Cues for Remote Affinity Diagramming Tasks in Augmented Reality
Llorens, Nathaniel Roman
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This thesis documents the design and implementation of an augmented reality (AR) application that could be extended to support group brainstorming tasks remotely. Additionally, it chronicles our investigation into the helpfulness of traditional collaborative cues in this novel application of augmented reality. We implemented IdeaSpace, an interactive application that emulates an affinity diagramming environment on an AR headset. In our application, users can organize and manipulate virtual sticky notes around a central virtual board. We performed a user study, with each session requiring users to perform an affinity diagramming clustering task with and without common collaborative cues. Our results indicate that the presence or absence of cues has little effect on this task, or that other factors played a larger role than cue condition, such as learning effects. Our results also show that our application's usability could be improved. We conclude this document with a discussion of our results and the design implications that may arise from them.
General Audience Abstract
Our project was aimed at creating an app for modern augmented reality headsets that could help people perform group brainstorming sessions remotely from each other. We were also interested in finding out the benefits or downsides of some of the design decisions that recent research in remote augmented reality recommends, such as lines showing where a user is focusing and visualizations for a user's head and hands. In our app, which we dubbed IdeaSpace, users were faced with a virtual corkboard and a number of virtual sticky notes, similar to what they might expect in a traditional brainstorming session. We ran three-person study sessions comparing design techniques recommended by literature to an absence of such techniques and did not find they helped much in our task. We also found that our application was not as usable as we had hoped and could be improved in future iterations. We conclude our paper discussing what our results might mean and what can be learned for the future.
- Masters Theses