Fanny Chevalier

HC
h-index33
5papers
93citations
Novelty31%
AI Score35

5 Papers

2.9HCOct 21, 2022
Considerations for Visualizing Uncertainty in Clinical Machine Learning Models

Caitlin F. Harrigan, Gabriela Morgenshtern, Anna Goldenberg et al.

Clinician-facing predictive models are increasingly present in the healthcare setting. Regardless of their success with respect to performance metrics, all models have uncertainty. We investigate how to visually communicate uncertainty in this setting in an actionable, trustworthy way. To this end, we conduct a qualitative study with cardiac critical care clinicians. Our results reveal that clinician trust may be impacted most not by the degree of uncertainty, but rather by how transparent the visualization of what the sources of uncertainty are. Our results show a clear connection between feature interpretability and clinical actionability.

6.6HCApr 11
Characterizing Creativity in Data Visualization: Reflections and Future Directions

Tianwei Ma, Zinat Ara, Safwat Ali Khan et al.

Characterizing creativity in visualization design can lead to the design of more expressive representations and visualization authoring tools that prioritize human creativity. In this paper, we examine how creativity manifests itself in visualization design processes through two complementary studies. First, a systematic review of 63 papers yields a design space spanning three themes: creative design frameworks that focus on developing design processes by incorporating divergent and convergent thinking activities, creative visual representations that focus on developing unorthodox visualizations, and visualization-enabled creativity support tools that focus on supporting a creative task (e.g., writing) with visualization. Second, we conducted qualitative interviews with 11 visualization practitioners and researchers to understand practical challenges and contrast those with current academic framing through our design space. The interview findings indicate that artifacts or final products (unorthodox visualizations) are often disproportionately considered as the primary indicator of creativity, whereas the design process remains undervalued in practical and organizational contexts. We also found that ideation is a universal bottleneck, and organizational constraints are often the primary barrier to creative work. We discuss implications for rethinking the relationship between our design space categories, addressing organizational barriers, and designing future frameworks, tools, and evaluation methods that better support creativity in the age of AI-assisted visualization. The full list of coded papers is available here: https://vizcreativity.notion.site/coded-papers.

1.2CYOct 8, 2020
Computational Skills by Stealth in Secondary School Data Science

Wesley Burr, Fanny Chevalier, Christopher Collins et al.

The unprecedented growth in the availability of data of all types and qualities and the emergence of the field of data science has provided an impetus to finally realizing the implementation of the full breadth of the Nolan and Temple Lang proposed integration of computing concepts into statistics curricula at all levels in statistics and new data science programs and courses. Moreover, data science, implemented carefully, opens accessible pathways to stem for students for whom neither mathematics nor computer science are natural affinities, and who would traditionally be excluded. We discuss a proposal for the stealth development of computational skills in students' first exposure to data science through careful, scaffolded exposure to computation and its power. The intent of this approach is to support students, regardless of interest and self-efficacy in coding, in becoming data-driven learners, who are capable of asking complex questions about the world around them, and then answering those questions through the use of data-driven inquiry. This discussion is presented in the context of the International Data Science in Schools Project which recently published computer science and statistics consensus curriculum frameworks for a two-year secondary school data science program, designed to make data science accessible to all.

7.9HCAug 31, 2020
Designing Narrative-Focused Role-Playing Games for Visualization Literacy in Young Children

Elaine Huynh, Angela Nyhout, Patricia Ganea et al.

Building on game design and education research, this paper introduces narrative-focused role-playing games as a way to promote visualization literacy in young children. Visualization literacy skills are vital in understanding the world around us and constructing meaningful visualizations, yet, how to better develop these skills at an early age remains largely overlooked and understudied. Only recently has the visualization community started to fill this gap, resulting in preliminary studies and development of educational tools for use in early education. We add to these efforts through the exploration of gamification to support learning, and identify an opportunity to apply role-playing game-based designs by leveraging the presence of narratives in data-related problems involving visualizations. We study the effects of including narrative elements on learning through a technology probe, grounded in a set of design considerations stemming from visualization, game design, and education science. We create two versions of a game -- one with narrative elements and one without -- and evaluate our instances on 33 child participants between 11- to 13-years old using a between-subjects study design. Despite participants requiring double the amount of time to complete their game due to additional elements, the inclusion of such elements were found to improve engagement without sacrificing learning; our results indicate no significant differences in development of graph-reading skills, but significant differences in engagement and overall enjoyment of the game. We report observations and qualitative feedback collected, and note areas for improvement and room for future wook.

9.3HCAug 1, 2019
Common Fate for Animated Transitions in Visualization

Amira Chalbi, Jacob Ritchie, Deokgun Park et al.

The Law of Common Fate from Gestalt psychology states that visual objects moving with the same velocity along parallel trajectories will be perceived by a human observer as grouped. However, the concept of common fate is much broader than mere velocity; in this paper we explore how common fate results from coordinated changes in luminance and size. We present results from a crowdsourced graphical perception study where we asked workers to make perceptual judgments on a series of trials involving four graphical objects under the influence of conflicting static and dynamic visual factors (position, size and luminance) used in conjunction. Our results yield the following rankings for visual grouping: motion > (dynamic luminance, size, luminance); dynamic size > (dynamic luminance, position); and dynamic luminance > size. We also conducted a follow-up experiment to evaluate the three dynamic visual factors in a more ecologically valid setting, using both a Gapminder-like animated scatterplot and a thematic map of election data. The results indicate that in practice the relative grouping strengths of these factors may depend on various parameters including the visualization characteristics and the underlying data. We discuss design implications for animated transitions in data visualization.