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Quantifying user performance with metrics such as time and accuracy does not show the whole picture when researchers evaluate complex, interactive visualization tools. In such systems, performance is often influenced by different analysis strategies that statistical analysis methods cannot account for. To remedy this lack of nuance, we propose a novel analysis methodology for evaluating complex interactive visualizations at scale. We implement our analysis methods in reVISit, which enables analysts to explore participant interaction performance metrics and responses in the context of users' analysis strategies. Replays of participant sessions can aid in identifying usability problems during pilot studies and make individual analysis processes salient. To demonstrate the applicability of reVISit to visualization studies, we analyze participant data from two published crowdsourced studies. Our findings show that reVISit can be used to reveal and describe novel interaction patterns, to analyze performance differences between different analysis strategies, and to validate or challenge design decisions.
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Topicalizer: reframing core concepts in machine learning visualization by co-designing for interpretivist scholarship
Computational algorithms can provide novel, compelling functionality for interactive systems. However, designing such systems for users whose expertise lies outside computer science poses novel and complex challenges. This paper focuses specifically on the domain of designing interactive topic modeling visualizations to support interpretivist scholars. It describes a co-design process that involved working directly with two such scholars across two different corpora. The resultant visualization has both several similarities and key differences with other topic modeling visualizations, illustrated here using both the final design and discarded prototypes. The paper’s core contribution is an attention to how our emphasis on interpretation played out, both in the design process and in the final visualization design. The paper concludes by discussing the kinds of issues and tensions that emerged in the course of this work, as well as the ways that these issues and tensions can apply to much broader contexts of designing interactive algorithmic systems.
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- Award ID(s):
- 1844901
- PAR ID:
- 10160137
- Date Published:
- Journal Name:
- Human–Computer Interaction
- ISSN:
- 0737-0024
- Page Range / eLocation ID:
- 1 to 29
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/07370024.2020.1734460
