Researchers in HCI and teacher education have long recognized the potential of visualization to support teachers' reflective practice. Despite much progress however, teacher educators continue to highlight the need for more dynamic classroom data visualizations to better support teachers' reflective practice, particularly about spatial dimensions of their pedagogy. In response, this article makes three contributions. First, we build on prior work to present the Interaction Geography Slicer (IGS), an open-source tool to dynamically visualize movement, conversation, and video data over space and time in settings such as classrooms. Second, we share findings from a participatory design-based research project involving 11 experienced high school mathematics teachers who used the IGS over one year to support their reflective practice. Finally, we propose new directions for exploratory spatial classroom data visualization.
https://dl.acm.org/doi/10.1145/3706598.3713499
A reader's interpretation of a visualization is informed by both intratextual information (the information directly represented in the visualization) and extratextual information (information not represented in the visualization but known by the reader). Yet, we do not know what kinds of intra- and extratextual information readers use or how they integrate it to form meaning. To explore this area, we conducted semi-structured interviews about four real-world visualizations. We used thematic analysis to understand the types of information that participants used and diffractive reading to reveal how participants blended intra- and extratextual information. Our thematic analysis showed that participants utilized a broad assortment of information from both expected and unexpected sources. Additionally, our diffractive reading exposed three ways that participants incorporated intra- and extratextual information: to decide what to look at, to make (in)accurate assumptions about what the visualization showed, and to discover insights beyond what was directly encoded.
https://dl.acm.org/doi/10.1145/3706598.3713612
Even for well-studied visual reasoning tasks such as those performed on bar charts, little is known about the cognitive strategies users adopt to solve them. Guidance systems that support users in learning visual reasoning require information on successful strategies to help unsuccessful users improve or change their strategies. We introduce the guidance paradigm of sequential visual cues (SVCs), accompanied by a differential pattern mining approach that determines relevant visual attention patterns from gaze data, and exemplified for bar charts. The novel feature of SVCs is to give hints on critical fragments of successful strategies, guiding users where to look in a visualization and in which order, but not what to do with this information. Results from an empirical study (N=30) show how critical patterns of successful and unsuccessful strategies differ for various bar chart tasks. In a qualitative survey (N=5), we explore how to surface relevant gaze patterns as SVCs.
https://dl.acm.org/doi/10.1145/3706598.3713352
In this paper, we introduce the concept of realistic charts, referring to charts in the real world that cannot be digitally altered, such as those printed in newspapers or used in presentations. By enabling interaction with and graphical enhancement of these realistic charts as if they were digital, we transform realistic charts into “digital charts” by adding virtual graphical overlays. To achieve this, we identify 33 overlay strategies (e.g., highlights and trendlines) for five widely-used chart types (e.g., line charts) through systematic exploration and a formative study. To simplify overlay creation, we introduce a new grammar named Vega-Overlay. Leveraging this design space and grammar, we develop a system called HARVis, which allows users to generate virtual overlays through augmented reality devices using speech and optional gestures. A user study involving 33 participants from diverse fields, across 17 tasks, demonstrates the effectiveness and usability of HARVis.
https://dl.acm.org/doi/10.1145/3706598.3714320
The emerging concept of data storytelling (DS) suggests that enhancing visualisations with annotations and narratives can make complex data more insightful than conventional visualisations. Previous works found that DS-enhanced visualisations are more effective than conventional visualisations for simple tasks like identifying key data points or the main message. However, no previous work has explored the extent to which DS enhancements influence task completion across different levels of cognitive complexity. We address this gap by presenting the results of a study where 128 participants completed tasks based on four visualisations (two line charts and two choropleth maps, either with or without DS elements) spanning a range of complexity based on Bloom's taxonomy, which has been applied in data visualisation to categorise tasks hierarchically from lower to higher-order thinking. Results suggest that while DS-enhanced visualisations effectively support lower-order tasks (finding data points and understanding insights), they don't necessarily aid the correct completion of higher-order tasks (application, analysis, evaluation and creation). However, DS enhancements improve how efficiently participants complete complex tasks.
https://dl.acm.org/doi/10.1145/3706598.3714270
The growing popularity of interactive time series exploration platforms has made data visualization more accessible to the public. However, the ease of creating polished charts with preloaded data also enables selective information presentation, often resulting in biased or misleading visualizations. Research shows that these tools have been used to spread misinformation, particularly in areas such as public health and economic policies during the COVID-19 pandemic. Post hoc fact-checking may be ineffective because it typically addresses only a portion of misleading posts and comes too late to curb the spread. In this work, we explore using visualization design to counteract cherry-picking, a common tactic in deceptive visualizations. We propose a design space of guardrails—interventions to expose cherry-picking in time-series explorers. Through three crowd-sourced experiments, we demonstrate that guardrails, particularly those superimposing data, can encourage skepticism, though with some limitations. We provide recommendations for developing more effective visualization guardrails.
https://dl.acm.org/doi/10.1145/3706598.3713385
We present the results of a study comparing the performance of younger adults (YA) and people in late adulthood (PLA) across ten low-level analysis tasks and five basic visualizations, employing Bayesian regression to aggregate and model participant performance. We analyzed performance at the task level and across combinations of tasks and visualizations, reporting measures of performance at aggregate and individual levels. These analyses showed that PLA on average required more time to complete tasks while demonstrating comparable accuracy. Furthermore, at the individual level, PLA exhibited greater heterogeneity in task performance as well as differences in best-performing visualization types for some tasks. We contribute empirical knowledge on how age interacts with analysis task and visualization type and use these results to offer actionable insights and design recommendations for aging-inclusive visualization design. We invite the visualization research community to further investigate aging-aware data visualization. Supplementary materials can be found at https://osf.io/a7xtz/.
https://dl.acm.org/doi/10.1145/3706598.3714229