Abstract Annotation is a central mechanism in visualization design that enables people to communicate key insights. Prior research has provided essential accounts of the visual forms annotations take, but less attention has been paid to the decisions behind them. This paper examines how annotations are designed in practice and how educators reflect on those practices. We conducted a two‐phase qualitative study: interviews with ten practitioners from diverse backgrounds revealed the heuristics they draw on when creating annotations, and interviews with seven visualization educators offered complementary perspectives situated within broader concerns of clarity, guidance, and viewer agency. These studies provide a systematic account of annotation design knowledge in professional settings, highlighting the considerations, trade‐offs, and contextual judgments that shape the use of annotations. By making this tacit expertise explicit, our work complements prior form‐focused studies, strengthens understanding of annotation as a design activity, and points to opportunities for improved tool and guideline support.

Annotations are widely used in information visualization to guide attention, clarify patterns, and support interpretation. We present a comprehensive survey of 191 research articles describing empirical studies, tools, techniques, and systems that incorporate annotations across various visualization contexts. Based on a structured analysis, we characterize annotations by their types, generation methods, and targets, and examine their use across four primary application domains: user engagement, storytelling, collaboration, and exploratory data analysis. We also discuss key trends, practical challenges, and open research directions. These findings offer a foundation for designing more effective annotation systems and advancing future research on annotation in visualization.

Annotations play a vital role in highlighting critical aspects of visualizations, aiding in data externalization and exploration, collaborative sensemaking, and visual storytelling. However, despite their widespread use, we identified a lack of a design space for common practices for annotations. In this paper, we evaluated over 1,800 static annotated charts to understand how people annotate visualizations in practice. Through qualitative coding of these diverse real-world annotated charts, we explored three primary aspects of annotation usage patterns: analytic purposes for chart annotations (e.g., present, identify, summarize, or compare data features), mechanisms for chart annotations (e.g., types and combinations of annotations used, frequency of different annotation types across chart types, etc.), and the data source used to generate the annotations. We then synthesized our findings into a design space of annotations, highlighting key design choices for chart annotations. We presented three case studies illustrating our design space as a practical framework for chart annotations to enhance the communication of visualization insights. All supplemental materials are available at https://shorturl.at/bAGM1.

In the evolving landscape of educational technology, data visualization plays a pivotal role in higher education. While peer review is an established pedagogical tool that actively engages students, its long-term effectiveness, particularly when integrated with data-driven predictive modeling for analyzing student comments, has not been empirically validated, especially in the context of data visualization courses. This study aims to fill this gap by employing Naïve Bayes modeling to analyze peer review data from an undergraduate visual literacy course over a five-year period (2017–2022). Building on the research of Friedman and Rosen [1], as well as Beasley et al. [2], our study not only reaffirms the utility of Naïve Bayes modeling in analyzing student comments, particularly focusing on parts of speech with nouns as the prominent category but also explores its application in enhancing the peer review process. A key finding is the emphasis on the ‘lie factor’ in students' comments when using the visual peer review rubric, highlighting areas for potential course content adaptation and instructional refinement. Comparing the Naïve Bayes model with Beasley's approach, we find that while both methodologies aid instructors in mapping classroom dynamics, the Naïve Bayes model offers a more detailed framework for predictive analysis. Our findings suggest that predictive modeling, as a tool to assess student comments, can provide novel insights into visual peer review. This could lead to impactful changes in course content, project modifications, and rubric enhancements, ultimately benefiting student learning and engagement in visual literacy courses.