Comparing Natural Language and Vibro-Audio Modalities for Inclusive STEM Learning with Blind and Low Vision Users

aforbes — Fri, 27 Oct 2023 20:35:39 +0000

Data representations continue to be produced in predominantly visual forms within STEM disciplines. The disparity in access to these graphical representations between students who are blind or have low vision (BLV) and their sighted peers is exacerbated as the adoption of digital screens become more prevalent in educational settings. Standard accessibility solutions rely heavily on natural language processing, e.g., screen readers, for non-visual information access. But can other non-visual modalities, like touch, be effective for learning graphical content rendered on touchscreens? To investigate this question, we conducted a user study with a multimodal touchscreen learning system to assess the effectiveness of two non-visual graphical presentation modalities: 1) a vibro-audio condition, which used the device’s embedded vibration motor plus an auditory content overview (a spatial and multimodal technique), and 2) a natural language condition that provided a complete description of the content (a cognitively mediated technique). BLV participants (N = 19) were presented with the learning system and asked to answer multiple-choice questions about three different graph types using both presentation modalities. Findings showed that the two presentation modalities were functionally equivalent for learning the graphical information presented, suggesting that for these stimuli, the presentation modality did not have a significant effect on participant graph learning accuracy. However, the type of graph being learned did have a reliable effect. Moreover, a majority of the participants stated a preference towards the natural language condition and, on average, learned graphs faster than with the vibro-audio condition. The similarity found between the two learning modalities is interpreted as supporting user learning preferences while providing redundancy in the information being communicated. This approach layers the various types of information found in graphical representations (text, numerical, spatial) for individuals with accessible learning needs, providing more control, independence, and responsive tools to optimize their own educational materials.

Keywords: Applied Computing, Education, Interactive Learning Environments, Human-Centered Computing, Accessibility, Accessibility Systems and Tools, Human Computer Interaction, HCI Design and Evaluation Methods, Usability Testing

Citation: Brown et al., “Comparing Natural Language and Vibro-Audio Modalities for Inclusive STEM Learning with Blind and Low Vision Users.”.
(https://www.sciencedirect.com/science/article/pii/S1369847823001870)

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The Autonomous Vehicle Assistant (AVA): Emerging technology design supporting blind and visually impaired travelers in autonomous transportation

aforbes — Thu, 10 Aug 2023 19:12:09 +0000

The U.S. Department of Transportation’s Inclusive Design Challenge spurred innovative research promoting accessible technology for people with disabilities in the future of autonomous transportation. This paper presents the user-driven design of the Autonomous Vehicle Assistant (AVA), a winning project of the challenge focused on solutions for people who are blind and visually impaired. Results from an initial survey (n = 90) and series of user interviews (n = 12) informed AVA’s novel feature set, which was evaluated through a formal navigation study (n = 10) and participatory design evaluations (n = 6). Aggregate findings suggest that AVA’s sensor fusion approach combining computer vision, last-meter assistance, and multisensory alerts provide critical solutions for users poised to benefit most from this emerging transportation technology.

Keywords: Autonomous vehicles, People with visual impairment, Accessibility

Citation: Paul D.S. Fink, Stacy A. Doore, Xue Lin, Matthew Maring, Pu Zhao, Aubree Nygaard, Grant Beals, Richard R. Corey, Raymond J. Perry, Katherine Freund, Velin Dimitrov, Nicholas A. Giudice, The Autonomous Vehicle Assistant (AVA): Emerging technology design supporting blind and visually impaired travelers in autonomous transportation,
International Journal of Human-Computer Studies, Volume 179, 2023, 103125, ISSN 1071-5819, https://doi.org/10.1016/j.ijhcs.2023.103125

Stacy A. Doore – VEMI Lab

Comparing Natural Language and Vibro-Audio Modalities for Inclusive STEM Learning with Blind and Low Vision Users

The Autonomous Vehicle Assistant (AVA): Emerging technology design supporting blind and visually impaired travelers in autonomous transportation