Image and text analytic systems for accessible online learning
Li, Jiaxi
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https://hdl.handle.net/2142/120301
Description
Title
Image and text analytic systems for accessible online learning
Author(s)
Li, Jiaxi
Issue Date
2023-04-21
Director of Research (if dissertation) or Advisor (if thesis)
Nahrstedt, Klara
Angrave, Lawrence
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Online Learning
Accessibility
Image Analysis
Text Analysis
Language
eng
Abstract
Online learning has been widely used for college-level education in recent years. Although many course platforms offer accessible features such as closed captioning and embedded forums, they are not sufficient to satisfy the demands of students who are deaf and hard of hearing, or are blind or have low vision, students who have difficulty attending in-person lectures, and students who have insufficient prerequisite knowledge. To provide an interactive, accessible, and inclusive learning experience for all students, image and text analytic systems were deployed on ClassTranscribe, a web-based learning platform, to extract useful image and text content from lecture videos in an accurate and efficient manner. Reusing, remixing and transforming on the extracted items enables the generation of multimodal accessibility features including 1) Visual-based lecture delivery, 2) Audio-based lecture delivery, and 3) A glossary application. Preliminary user study results indicated a general positive opinion among students who have utilized the accessibility features for lecture learning.
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