Hello everyone, I’m Tingying He. Today, I'll present our work,
Using Tactile Charts to Support Comprehension
and Learning of Complex Visualizations for Blind and Low-Vision Individuals.
Visualization is a powerful tool for communicating complex information. However,
a large population of people who are blind or have low vision often face barriers to directly
accessing visualizations, because interpreting visualization relies heavily on the visual system.
Despite these challenges, BLV individuals need access to the
rich information visualizations convey. Most often, they access this information
through alternative text, which provides textual descriptions of chart content.
Accessibility guidelines commonly recommend including the chart type in the alt text.
Visualizations often convey more than just raw data. For example, a histogram shows the
distribution of a dataset—something that’s hard to grasp from the raw numbers alone.
Research on visualization accessibility also highlights the importance of chart type for
BLV individuals’ understanding of visualization descriptions.
While BLV individuals are generally familiar with basic chart types such as pie,
bar, and line charts, they often struggle with more advanced types.
Yet, complex visualizations are now common in
professional contexts such as scientific reporting.
Therefore, it is valuable for BLV professionals to learn complex chart types, both to better
interpret alt text and to collaborate effectively with sighted colleagues.
Tactile charts are another modality that can make visualizations accessible to BLV individuals.
Previous work has examined tactile charts as data representations and found that they can
help users understand spatial relationships and data patterns. However, tactile charts are slower
and more costly to produce. Static tactile charts cannot be easily updated once created,
and while dynamic tactile devices do exist, they are still expensive and have limited resolution.
Tactile charts have also been used as teaching tools, where they do not
often need to be updated. In practice, tactile graphics are commonly used in BLV education,
but typically only include simple charts. A few research exploring their use as educational
tools for visualizations, but mostly focuses on basic chart types. Teaching
complex chart types through tactile charts remains largely underexplored.
In this work, we aim to support BLV individuals in building mental models of complex chart types
through tactile charts. We hypothesize that developing such mental models can help them
better interpret digital representations, such as alt text, that use these chart types.
To test this hypothesis, we selected four advanced chart types commonly used in scientific contexts:
UpSet plots, clustered heatmaps, faceted area plots, and violin plots. Since our goal
was to use tactile charts as learning tools, we designed them using simple,
familiar datasets so participants could focus on the chart types rather than the data content.
The design of tactile charts greatly influences their readability.
However, there are not many design guidelines specific to the chart types we selected.
To address this gap, we followed a collaborative and iterative design process. We followed existing
guidelines and best practices, applied our expertise in visualization design,
and collaborated closely with two BLV researchers.
To create a tactile chart, we first designed a 2D version, then built 3D
models and produced them with 3D printing, and finally added labels to the backside.
To explore different design options, we created two initial variations of each chart and prepared
exploration instructions. We then asked our BLV collaborators for feedback on both the design
and the instructions. Based on their input, we refined each chart into a third, final design.
We also finalized the exploration instructions alongside them.
We recommend checking our paper for details on our design considerations and recommendations.
All final models and exploration instructions are available on our project website.
Using these finalized tactile charts, we conducted an interview study with 12 BLV
participants to evaluate their effectiveness. We required participants to be students or
in employment status, because the focus of our study is a professional data analysis context.
Our goal was not only to introduce participants to complex chart types but also to test whether
learning through tactile charts improved their comprehension when reading alt text
for new datasets of the same chart type. For each chart, participants first learned it either with
a tactile model and textual instructions or with textual instructions only, then demonstrated their
understanding using an alt text describing a more complex dataset of that chart type.
Given the expected interview length and recruitment challenges, it was infeasible
to test all four chart types. To narrow them down, we consulted our BLV collaborators and
ranked the charts by difficulty. From most to least difficult: UpSet plot, clustered
heatmap, violin plot, and faceted line chart. To balance the need for evaluating complex charts
without overwhelming participants, we selected the clustered heatmap and violin plot for testing.
Participants learned one chart type with tactile charts and the other without. We
compared participants’ learning experiences, outcomes, and preferences between the two
modalities and gathered feedback on the tactile charts themselves.
We conducted a thematic analysis and identified five main themes. Here,
I’ll focus on three of them, but you can find all five in our paper.
First, I’ll discuss how tactile charts support building mental models of chart types.
As one participant P9 said: “The tactile model helps put it
into a picture that you can use, you can create a mental model, to picture things in your head.”
Tactile charts, in particular, supported learning about chart
layout and element shapes, which are important for understanding how the chart type works.
Participants also prefer the hands-on learning approach, for example,P3 said:
“I want to touch everything.” P4 also mentioned: “I’m blind, but I do consider myself a visual
or a tactile learner in the sense that I’m able to feel what I’m being told.”
Second, participants found that the mental models they built were reusable and transferable across
different datasets. These models helped them interpret new data and alt texts.
As P12 said: “It helps me
understand the chart itself, then it allows me to recreate it mentally with a different data set.”
Participants emphasized that tactile charts help them develop a structural understanding
of how each chart type functions and provide a reference point when interpreting new datasets.
The third theme relates to BLV visualization education.
Participants discussed the challenges BLV individuals face when learning subjects
involving data visualizations. As P2 shared:
“They didn’t know how to help me. So we just skipped that part of the curriculum for me.”
They expressed strong interest in learning new visualizations and emphasized the importance of
equal access to information. As P1 said:
“Information, like how to read a map or access data, is so important, especially when you
get a job. You feel left out when you don’t have that information while your peers do.”
Participants also believed tactile charts could
empower their learning and expand future opportunities.
As P2 put it: “It is extremely important
for students to be able to have access to that, because it just changes the game for you…[tactile
charts could enable blind students to be] on the same playing field as their sighted peers.”
In conclusion, we found that thoughtfully designed tactile
charts with clear exploration instructions can be a helpful and preferred educational
tool for BLV individuals to learn complex visualizations.
Thank you for listening.