data visualization

To address the challenge of visual dashboards failing to adapt to users’ domain expertise, interests, and cognitive load, this paper proposes DrillBoard—a novel adaptive visualization framework supporting dynamic granularity adjustment. Methodologically, it introduces a formal chart semantic model, a cross-chart-type fusion rule engine, and a hierarchical view generation algorithm to enable automatic evolution from baseline dashboards to multi-level abstract views. A web-based visualization authoring tool is developed to support bidirectional customization—by domain experts for modeling and by end users for personalization. Its key innovation lies in the first formal, rule-driven adaptive drill-down mechanism. Experiments on real-world datasets demonstrate feasibility and efficacy: three domain experts successfully instantiated DrillBoard; user studies with non-experts showed significant improvements in information comprehension efficiency and high interaction satisfaction, validating its practicality and effectiveness in personalized adaptation.

This study addresses the cognitive bias in scatterplots where “data-induced grouping”—arising from the interplay between data values and visual encoding—leads users to misinterpret spatial arrangements as meaningful patterns. Through two user studies, the authors systematically demonstrate the prevalence of this phenomenon, develop the first perceptual model capable of predicting whether users perceive a given set of points as a coherent group, and propose a visualization intervention strategy that integrates user perception with data reordering. Notably, the model effectively captures users’ tendency to group points based on trends even in nominal data contexts. Applied to visualization diagnosis and optimization, this approach significantly enhances the accuracy and reliability of graphical representations.

Data visualization instruction often inadequately cultivates students’ critical thinking skills. Method: This study proposes a context-based case teaching approach, designing authentic, multidimensional data narrative cases that systematically guide students to deconstruct the complexity of data representation and interpretation—focusing on chart selection rationale, temporal/categorical comparison logic, identification of visual biases, and reflection on narrative framing. Contribution/Results: The study innovatively articulates reusable principles and a methodology for contextual design, and develops a suite of ready-to-use visualization teaching exemplars. Empirical implementation demonstrates significant improvement in students’ critical analytical capacity regarding data insights, presentation logic, and interpretive stance. This approach offers a transferable, scalable pedagogical intervention for data literacy education.

Contemporary dashboards suffer from complex interactions and tightly coupled views, necessitating labor-intensive authoring and maintenance of guided tutorials—resulting in high costs and poor synchronization with dashboard updates. To address this, we propose DIANA, the first multimodal dashboard assistant integrating speech, text, and mouse gaze inputs. Built upon large language models (LLMs), DIANA implements a context-aware, real-time interactive system that enables users to issue queries via any combination of modalities and receive immediate visual feedback—including interface element highlighting—and semantically grounded explanations. Its key innovation lies in transcending the conventional unimodal text-based LLM paradigm by enabling synergistic, tri-modal-driven dynamic guidance. DIANA establishes, for the first time in visualization analytics, a closed-loop pipeline spanning multimodal input, interface response, and semantic output. A user study demonstrates that DIANA significantly improves users’ comprehension efficiency of dashboard structure and functionality while markedly reducing reliance on manual guidance.

This study investigates whether bar charts and pie charts differentially influence high-level real-world decisions—specifically, students’ selection of academic advisors—and whether low-level visual perception advantages transfer to higher-order decision efficacy. Through a crowdsourced controlled experiment integrating visualization task performance metrics with actual decision outcomes, we find no statistically significant difference in final advisor choices between the two chart types. Critically, this work provides the first empirical evidence that perceptual accessibility—the ease of extracting visual information—does not automatically entail decision accessibility—the ease of leveraging that information for consequential choices. We thus propose a novel theoretical framework distinguishing these two constructs. These findings challenge the assumed universality of classical visualization design principles in high-stakes decision contexts, establishing crucial boundary conditions for decision-centric visualization theory and practice. (149 words)

This work addresses the limitations of linear conversation logs generated by existing conversational data analysis systems, which hinder data workers’ ability to retrospect and communicate about nonlinear, iterative analytical processes. To overcome this, the paper proposes a structured dialogue presentation method that introduces probes enabling multi-level navigation, on-demand detail expansion, and context-enhanced summarization—going beyond conventional scrolling and keyword search. By integrating visual recall with sequential and abstraction-based navigation strategies, the approach effectively supports users in recalling, reorienting within, and prioritizing past analytical exchanges. A user study with ten participants demonstrates that the method significantly enhances traceability of analytical reasoning and improves collaborative efficiency, validating its effectiveness in real-world data analysis workflows.

This study investigates the discrepancies between large language models (LLMs) and human cognition in high-level chart understanding, with a focus on interpreting designer intent and extracting complex data patterns. Through qualitative user studies, it systematically compares the higher-order interpretation strategies employed by humans and LLMs on line charts, bar charts, and scatter plots, while analyzing LLM outputs and reasoning pathways under three distinct prompting conditions. The work reveals, for the first time, that LLMs consistently adopt a structured enumeration strategy rather than constructing coherent trend-based narratives, and their explanatory patterns remain remarkably stable across different prompts. In contrast, humans demonstrate a superior ability to synthesize holistic, narrative-driven interpretations. These findings highlight fundamental mechanistic limitations of LLMs in visual reasoning and offer critical insights for future model design.

This study addresses a critical gap in current research on visualization literacy, which has largely overlooked the central role of interaction in data visualization and the associated cognitive and operational challenges it introduces. To bridge this gap, the work proposes a novel two-dimensional multi-literacy theoretical model that systematically incorporates an interaction dimension into the visualization literacy framework for the first time. The model integrates four established literacy types and introduces five newly defined interaction-related literacies. Its validity is substantiated through theoretical derivation, structured analysis of existing systems, and exploratory user observations. By expanding the boundaries of traditional visualization literacy, this research establishes a clear pathway for future efforts in measuring and assessing interactive visualization literacy, as well as informing the design of interactive visualization systems and pedagogical practices.

Magic: The Gathering—Commander players lack effective tools for match data analysis. Method: This study employs user task analysis, iterative visualization design (including heatmaps and line charts), and structured usability testing to derive dashboard design principles centered on contextual relevance, outcome orientation, and progressive disclosure. It prioritizes adaptability, customizability, and accuracy equally—departing from conventional generic dashboard paradigms. Contribution/Results: Empirical evaluation demonstrates that heatmaps and line charts significantly improve players’ comprehension efficiency of key metrics such as win rate and play tempo. Players strongly prefer localized views, context-driven metrics, and personalized configurations. The study culminates in a domain-specific visualization design guideline for trading card games (TCGs), offering both methodological foundations and empirical validation for context-aware, domain-adapted game analytics tool design.

Existing approaches struggle to reconstruct interactive data dashboards that support functionalities such as clicking and filtering. This work introduces Dashboard2Code, a novel task requiring models to actively explore interactive dashboards and integrate user interaction feedback to generate code that faithfully reproduces the target dashboard. To facilitate research in this direction, we present DashboardMimic, the first benchmark dataset built on Plotly+Dash, along with an automated evaluation framework that combines semantic analysis and dynamic interaction testing. Experimental results on 180 high-quality dashboard–code pairs demonstrate that current models exhibit limited performance on highly complex dashboards, with closed-source models significantly outperforming their open-source counterparts.