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Browsing by Author "Moraes, Marcia, committee member"

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    Interaction and navigation in cross-reality analytics
    (Colorado State University. Libraries, 2024) Zhou, Xiaoyan, author; Ortega, Francisco, advisor; Ray, Indrakshi, committee member; Moraes, Marcia, committee member; Batmaz, Anil Ufuk, committee member; Malinin, Laura, committee member
    Along with immersive display technology's fast evolution, augmented reality (AR) and virtual reality (VR) are increasingly being researched to facilitate data analytics, known as Immersive Analytics. The ability to interact with data visualization in the space around users not only builds the foundation of ubiquitous analytics but also assists users in the sensemaking of the data. However, interaction and navigation while making sense of 3D data visualization in different realities still need to be better understood and explored. For example, what are the differences between users interacting in augmented and virtual reality, and how can we utilize them in the best way during analysis tasks? Moreover, based on the existing work and our preliminary studies, improving the interaction efficiency with immersive displays still needs to be solved. Therefore, this thesis focuses on understanding interaction and navigation in augmented reality and virtual reality for immersive analytics. First, we explored how users interact with multiple objects in augmented reality by using the "Wizard of Oz" study approach. We elicited multimodal interactions involving hand gestures and speech, with text prompts shown on the head-mounted display. Then, we compared the results with previous work in a single-object scenario, which helped us better understand how users prefer to interact in a more complex AR environment. Second, we built an immersive analytics platform in both AR and VR environments to simulate a realistic scenario and conducted a controlled study to evaluate user performance with designed analysis tools and 3D data visualization. Based on the results, interaction and navigation patterns were observed and analyzed for a better understanding of user preferences during the sensemaking process. ii Lastly, by considering the findings and insights from prior studies, we developed a hybrid user interface in simulated cross-reality for situated analytics. An exploratory study was conducted with a smart home setting to understand user interaction and navigation in a more familiar scenario with practical tasks. With the results, we did a thorough qualitative analysis of feedback and video recording to disclose user preferences with interaction and visualization in situated analytics in the everyday decision-making scenario. In conclusion, this thesis uncovered user-designed multimodal interaction including mid-air hand gestures and speech for AR, users' interaction and navigation strategies in immersive analytics in both AR and VR, and hybrid user interface usage in situated analytics for assisting decision-making. Our findings and insights in this thesis provide guidelines and inspiration for future research in interaction and navigation design and improving user experience with analytics in mixed-reality environments.
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    Optimizing designer cognition relative to generative design methods
    (Colorado State University. Libraries, 2023) Botyarov, Michael, author; Miller, Erika, advisor; Bradley, Thomas, committee member; Forrest, Jeffrey, committee member; Moraes, Marcia, committee member; Simske, Steve, committee member; Radford, Donald, committee member
    Generative design is a powerful tool for design creation, particularly for complex engineering problems where a plethora potential design solutions exist. Generative design systems explore the entire solution envelope and present the designer with multiple design alternatives that satisfy specified requirements. Although generative design systems present design solutions to an engineering problem, these systems lack consideration for the human element of the design system. Human cognition, particularly cognitive workload, can be hindered when presented with unparsed generative design system output, thereby reducing the efficiency of the systems engineering life cycle. Therefore, the objective of this dissertation was to develop a structured approach to produce an optimized parsing of spatially different generative design solutions, derived from generative design systems, such that human cognitive performance during the design process is improved. Generative design usability foundation work was conducted to further elaborate on gaps found in the literature in the context of the human component of generative design systems. A generative design application was then created for the purpose of evaluating the research objective. A novel generative design solution space parsing method that leverages the Gower distance matrix and partitioning around medoids (PAM) clustering method was developed and implemented in the generative design application to structurally parse the generative design solution space for the study. The application and associated parsing method were then used by 49 study participants to evaluate performance, workload, and experience during a generative design selection process, given manipulation of both the quantity of designs in the generative design solution space and filtering of parsed subsets of design alternatives. Study data suggests that cognitive workload is lowest when 10 to 100 generative design alternatives are presented for evaluation in the subset of the overall design solution space. However, subjective data indicates a caution when limiting the subset of designs presented, since design selection confidence and satisfaction may be decreased the more limited the design alternative selection becomes. Given these subjective considerations, it is recommended that a generative design solution space consists of 50 to 100 design alternatives, with the proposed clustering parsing method that considers all design alternative variables.