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Model for Profiling Users With Disabilities on e-Learning Platforms
- Source :
- IEEE Access, Vol 9, Pp 74258-74274 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- There are millions of people worldwide using e-Learning platforms. However, people with disabilities still have problems using these platforms due to a lack of accessibility. In the present study, a model for the generation of the profiles of users with disabilities is proposed. This model enables e-Learning platforms to have information on users’ accessibility needs as input for automated adaptation of its interfaces. This would enable equal access for all learners with and without disabilities. In this context, different studies related to the profiling of users on e-Learning platforms were identified and analyzed. The model presented considers the Web Content Accessibility Guidelines (WCAG) that can be implemented in the interfaces of the e-Learning platforms and the metadata that represent the accessibility needs of users based on Schema.org. The researchers used Unified Modelling Language diagrams to design the model and define a description of the interaction between users, user interfaces, WCAG, Schema.org., and the eXtensible Markup Language (XML) profile generated. The testing of the prototype was carried out using WAVE and ARC Toolkit. The validation with the support of forty-four users was conducted using the System Usability Scale (SUS). The most outstanding results of this study are the identification of WCAG success criteria that are automatically implementable, the inclusion of two special categories for combined accessibility needs related to the elderly and linguistics, and the automatic generation of the profile as an XML file containing the metadata needed to enable the adaptation of e-Learning platform interfaces.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.5f21449b58014657a27eb44ca8671f54
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2021.3081061