arXiv subjects

Nicole Sultanum

Publications and source records attributed to Nicole Sultanum.

9 recordsLinked to original sources

arXiv2026

Not Always Top-Left: Untangling the Signals that Guide Dashboard Reading Order

Dashboards are widely used interfaces for data analysis, combining multiple visualizations, text, and interactive controls within a single view. While dashboard authors often structure layouts to suggest a logical consumption flow, users may interpret and navigate dashboards differently depending on the interplay between design features, analytical goals, and personal preferences. In this work, we investigate how people make sense of dashboards by examining their reading orders, i.e., the sequences in which users engage with dashboard components. We conduct a mixed-methods study with 18 dashboard authors and 16 end-users, capturing how participants design for and reason through these component transitions. Through qualitative and quantitative analyses of participant-generated flows, we outline a set of factors that influence dashboard reading order, including layout, visual saliency, semantics, functional roles, interaction, and user context. We also identify emergent reading patterns and analyze them through aggregate and variability measures, revealing where users converge and diverge in their interpretations. Finally, we discuss implications and opportunities for computational approaches that aim to automatically model, guide, or serialize dashboard consumption.

cs.HC↗

arXiv2026

Once Again, with Style: Understanding and Supporting Partial Reuse in Dashboard Authoring

Presentation-oriented tasks including formatting and layout design are critical but often neglected aspects of dashboard authoring given their labor intensive nature. In this work, we follow a user-centered design approach to explore ways that partial reuse of pre-existing dashboards may support the dashboard design process. Based on collective feedback from 10 professional dashboard creators, we contribute: (a) findings from a formative study characterizing dashboard reuse needs and challenges; and (b) reflections and opportunities from a concept validation study with ReDash, a design probe for partial reuse of dashboard presentation features (style and layout) from multiple sources.

cs.HC↗

arXiv2025

DashGuide: Authoring Interactive Dashboard Tours for Guiding Dashboard Users

Dashboard guidance helps dashboard users better navigate interactive features, understand the underlying data, and assess insights they can potentially extract from dashboards. However, authoring dashboard guidance is a time consuming task, and embedding guidance into dashboards for effective delivery is difficult to realize. In this work, we contribute DashGuide, a framework and system to support the creation of interactive dashboard guidance with minimal authoring input. Given a dashboard and a communication goal, DashGuide captures a sequence of author-performed interactions to generate guidance materials delivered as playable step-by-step overlays, a.k.a., dashboard tours. Authors can further edit and refine individual tour steps while receiving generative assistance. We also contribute findings from a formative assessment with 9 dashboard creators, which helped inform the design of DashGuide; and findings from an evaluation of DashGuide with 12 dashboard creators, suggesting it provides an improved authoring experience that balances efficiency, expressiveness, and creative freedom.

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arXiv2025

Plume: Scaffolding Text Composition in Dashboards

Text in dashboards plays multiple critical roles, including providing context, offering insights, guiding interactions, and summarizing key information. Despite its importance, most dashboarding tools focus on visualizations and offer limited support for text authoring. To address this gap, we developed Plume, a system to help authors craft effective dashboard text. Through a formative review of exemplar dashboards, we created a typology of text parameters and articulated the relationship between visual placement and semantic connections, which informed Plume's design. Plume employs large language models (LLMs) to generate contextually appropriate content and provides guidelines for writing clear, readable text. A preliminary evaluation with 12 dashboard authors explored how assisted text authoring integrates into workflows, revealing strengths and limitations of LLM-generated text and the value of our human-in-the-loop approach. Our findings suggest opportunities to improve dashboard authoring tools by better supporting the diverse roles that text plays in conveying insights.

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arXiv2024

From Instruction to Insight: Exploring the Functional and Semantic Roles of Text in Interactive Dashboards

There is increased interest in the interplay between text and visuals in the field of data visualization. However, this attention has predominantly been on the use of text in standalone visualizations or augmenting text stories supported by a series of independent views. In this paper, we shift from the traditional focus on single-chart annotations to characterize the nuanced but crucial communication role of text in the complex environment of interactive dashboards. Through a survey and analysis of 190 dashboards in the wild, plus 13 expert interview sessions with experienced dashboard authors, we highlight the distinctive nature of text as an integral component of the dashboard experience, while delving into the categories, semantic levels, and functional roles of text, and exploring how these text elements are coalesced by dashboard authors to guide and inform dashboard users. Our contributions are: 1) we distill qualitative and quantitative findings from our studies to characterize current practices of text use in dashboards, including a categorization of text-based components and design patterns; 2) we leverage current practices and existing literature to propose, discuss, and validate recommended practices for text in dashboards, embodied as 12 heuristics that underscore the semantic and functional role of text in offering navigational cues, contextualizing data insights, supporting reading order, etc; 3) we reflect on our findings to identify gaps and propose opportunities for data visualization researchers to push the boundaries on text usage for dashboards, from authoring support and interactivity to text generation and content personalization. Our research underscores the significance of elevating text as a first-class citizen in data visualization, and the need to support the inclusion of textual components and their interactive affordances in dashboard design.

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arXiv2024

Data Guards: Challenges and Solutions for Fostering Trust in Data

From dirty data to intentional deception, there are many threats to the validity of data-driven decisions. Making use of data, especially new or unfamiliar data, therefore requires a degree of trust or verification. How is this trust established? In this paper, we present the results of a series of interviews with both producers and consumers of data artifacts (outputs of data ecosystems like spreadsheets, charts, and dashboards) aimed at understanding strategies and obstacles to building trust in data. We find a recurring need, but lack of existing standards, for data validation and verification, especially among data consumers. We therefore propose a set of data guards: methods and tools for fostering trust in data artifacts.

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arXiv2024

To Search or To Gen? Exploring the Synergy between Generative AI and Web Search in Programming

The convergence of generative AI and web search is reshaping problem-solving for programmers. However, the lack of understanding regarding their interplay in the information-seeking process often leads programmers to perceive them as alternatives rather than complementary tools. To analyze this interaction and explore their synergy, we conducted an interview study with eight experienced programmers. Drawing from the results and literature, we have identified three major challenges and proposed three decision-making stages, each with its own relevant factors. Additionally, we present a comprehensive process model that captures programmers' interaction patterns. This model encompasses decision-making stages, the information-foraging loop, and cognitive activities during system interaction, offering a holistic framework to comprehend and optimize the use of these convergent tools in programming.

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arXiv2023

DataTales: Investigating the use of Large Language Models for Authoring Data-Driven Articles

Authoring data-driven articles is a complex process requiring authors to not only analyze data for insights but also craft a cohesive narrative that effectively communicates the insights. Text generation capabilities of contemporary large language models (LLMs) present an opportunity to assist the authoring of data-driven articles and expedite the writing process. In this work, we investigate the feasibility and perceived value of leveraging LLMs to support authors of data-driven articles. We designed a prototype system, DataTales, that leverages a LLM to generate textual narratives accompanying a given chart. Using DataTales as a design probe, we conducted a qualitative study with 11 professionals to evaluate the concept, from which we distilled affordances and opportunities to further integrate LLMs as valuable data-driven article authoring assistants.

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arXiv2022

An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics: Position Paper

Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languages, for instance, research outcomes are having an increasingly immediate impact on end users. As the gap in adoption by end users decreases, the need increases to ensure that tools and models developed by the research communities and practitioners are reliable, trustworthy, and supportive of the users in their goals. In this position paper, we focus on the issues of evaluating visual text analytics approaches. We take an interdisciplinary perspective from the visualization and natural language processing communities, as we argue that the design and validation of visual text analytics include concerns beyond computational or visual/interactive methods on their own. We identify four key groups of challenges for evaluating visual text analytics approaches (data ambiguity, experimental design, user trust, and "big picture" concerns) and provide suggestions for research opportunities from an interdisciplinary perspective.

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