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Interactive data learning tools provide explorable ways for students to build intuitions about data, data representations, and statistical parameters. However, these tools rely on visual consumption and are not accessible to blind and low vision (BLV) students. In this work, we investigate opportunities to leverage active exploration, enriched with multimodal feedback and embodied interaction, to foster an understanding of the relationships among individual data values, data representations, and statistical measures. We explore these opportunities in the form of an accessible learning platform that allows students to hear and feel how statistical measures are changing in real time as they construct and manipulate physicalized data representations. We introduced the platform to four teachers of students with visual impairments (TVIs) through a two-hour-long focus group. TVIs embraced the platform’s exploratory nature and universality and recommended the consideration of additional auditory and texture-based interactions to enhance engagement.
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Designing Interactive 3D Printed Models with Teachers of the Visually Impaired
Students with visual impairments struggle to learn various concepts in the academic curriculum because diagrams, images, and other visual are not accessible to them. To address this, researchers have design interactive 3D printed models (I3Ms) that provide audio descriptions when a user touches components of a model. In prior work, I3Ms were designed on an ad hoc basis, and it is currently unknown what general guidelines produce effective I3M designs. To address this gap, we conducted two studies with Teachers of the Visually Impaired (TVIs). First, we led two design workshops with 35 TVIs, who modified sample models and added interactive elements to them. Second, we worked with three TVIs to design three I3Ms in an iterative instructional design process. At the end of this process, the TVIs used the I3Ms we designed to teach their students. We conclude that I3Ms should (1) have effective tactile features (e.g., distinctive patterns between components), (2) contain both auditory and visual content (e.g., explanatory animations), and (3) consider pedagogical methods (e.g., overview before details).
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- Award ID(s):
- 1746123
- PAR ID:
- 10125914
- Date Published:
- Journal Name:
- 2019 ACM Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3290605.3300427
