Automation of the Generation of Accessible Digital Educational Material for Students with Visual Disabilities

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Abstract

Online learning has grown in the last years, where educational institutions are offering courses or study programs in different knowledge areas. Online learning allows students to participate actively in cooperative learning activities, interacting without the preconceived notions of disability that other participants could have, which affects the relationship. However, these advantages have often not become a reality for most people with disabilities, especially in the educational context. For instance, courses’ web pages are not accessible for people with visual disabilities, which makes the experience of taking a course frustrating and tedious. This article proposes a Model-Driven Development approach for supporting the design and generation of accessible educational material, for example, accessible Learning Objects (LO). This approach provides a software infrastructure that includes: (i) A Domain-Specific Language and its corresponding graphic editor that supports accessible LO’s design. Helping teachers during the instructional design and allowing them to describe accessibility requirements for students with visual disabilities. (ii) A LO’s generation engine that takes as input design artifacts and generates the source code that implements accessible LO. Abstracting teachers from technological aspects (e.g., programming language instructions) necessary to construct LO with accessibility features. The applicability of this approach is illustrated by using the DSL and the generation engine to design and automatically implement an accessible LO according to the accessibility profile specified during design. Finally, the generated LO was published in the Learning Management System Moodle.

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APA

Cabrera Medina, B., & Zuñiga-Prieto, M. (2020). Automation of the Generation of Accessible Digital Educational Material for Students with Visual Disabilities. In Communications in Computer and Information Science (Vol. 1195 CCIS, pp. 426–439). Springer. https://doi.org/10.1007/978-3-030-42531-9_34

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