VeasyGuide: Personalized Visual Guidance for Low-vision Learners on Instructor Actions in Presentation Videos

Instructors often rely on visual actions such as pointing, marking, and sketching to convey information in educational presentation videos. These subtle visual cues often lack verbal descriptions, forcing low-vision (LV) learners to search for visual indicators or rely solely on audio, which can lead to missed information and increased cognitive load. To address this challenge, we conducted a co-design study with three LV participants and developed VeasyGuide, a tool that uses motion detection to identify instructor actions and dynamically highlight and magnify them. VeasyGuide produces familiar visual highlights that convey spatial context and adapt to diverse learners and content through extensive personalization and real-time visual feedback. VeasyGuide reduces visual search effort by clarifying what to look for and where to look. In an evaluation with 8 LV participants, learners demonstrated a significant improvement in detecting instructor actions, with faster response times and significantly reduced cognitive load. A separate evaluation with 8 sighted participants showed that VeasyGuide also enhanced engagement and attentiveness, suggesting its potential as a universally beneficial tool.

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Publication Details

Published
2025-10-22
DOI
https://doi.org/10.1145/3663547.3746372
Citations
1
Primary Topic
Video Analysis and Summarization
Type
article
Field-Weighted Citation Impact
1.28
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article

VeasyGuide: Personalized Visual Guidance for Low-vision Learners on Instructor Actions in Presentation Videos

Amy Pavel, Ariel Shamir, Yotam Sechayk, Takeo Igarashi
1 citations
Video Analysis and Summarization
1.28
article

VeasyGuide: Personalized Visual Guidance for Low-vision Learners on Instructor Actions in Presentation Videos

Amy Pavel, Ariel Shamir, Yotam Sechayk, Takeo Igarashi
article en
1 citations

Abstract

Instructors often rely on visual actions such as pointing, marking, and sketching to convey information in educational presentation videos. These subtle visual cues often lack verbal descriptions, forcing low-vision (LV) learners to search for visual indicators or rely solely on audio, which can lead to missed information and increased cognitive load. To address this challenge, we conducted a co-design study with three LV participants and developed VeasyGuide, a tool that uses motion detection to identify instructor actions and dynamically highlight and magnify them. VeasyGuide produces familiar visual highlights that convey spatial context and adapt to diverse learners and content through extensive personalization and real-time visual feedback. VeasyGuide reduces visual search effort by clarifying what to look for and where to look. In an evaluation with 8 LV participants, learners demonstrated a significant improvement in detecting instructor actions, with faster response times and significantly reduced cognitive load. A separate evaluation with 8 sighted participants showed that VeasyGuide also enhanced engagement and attentiveness, suggesting its potential as a universally beneficial tool.

Herzliya Medical Center (IL), The University of Tokyo (JP), University of California, Berkeley (US)
Openalex Percentile: Top 15%
Video Analysis and Summarization
1.28
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