A Voice-Enabled Framework for Recommender and Adaptation Systems in E-Learning

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dc.creatorAzeta, A. A., Ayo, C. K., Omoregbe, N. A.
dc.date2013
dc.date.accessioned2025-03-06T12:57:23Z
dc.descriptionWith the proliferation of learning resources on the Web, finding suitable content (using telephone) has become a rigorous task for voice-based online learners to achieve better performance. The problem with Finding Content Suitability (FCS) with voice E-Learning applications is more complex when the sight-impaired learner is involved. Existing voice-enabled applications in the domain of E-Learning lack the attributes of adaptive and reusable learning objects to be able to address the FCS problem. This study provides a Voice-enabled Framework for Recommender and Adaptation (VeFRA) Systems in E-learning and an implementation of a system based on the framework with dual user interfaces – voice and Web. A usability study was carried out in a visually impaired and non-visually impaired school using the International Standard Organization’s (ISO) 9241-11 specification to determine the level of effectiveness, efficiency and user satisfaction. The result of the usability evaluation reveals that the prototype application developed for the school has “Good Usability” rating of 4.13 out of 5 scale. This shows that the application will not only complement existing mobile and Web-based learning systems, but will be of immense benefit to users, based on the system’s capacity for taking autonomous decisions that are capable of adapting to the needs of both visually impaired and non-visually impaired learners.
dc.formatapplication/pdf
dc.identifierhttp://eprints.covenantuniversity.edu.ng/1269/
dc.identifier.urihttp://itsupport.cu.edu.ng:4000/handle/123456789/29631
dc.languageen
dc.publisherIGI-Global
dc.subjectQA75 Electronic computers. Computer science
dc.titleA Voice-Enabled Framework for Recommender and Adaptation Systems in E-Learning
dc.typeBook Section

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