RALO: Accessible Learning Objects Assessment Ecosystem Based on Metadata Analysis, Inter-Rater Agreement, and Borda Voting Schemes

Paola Ingavelez-Guerra, Vladimir E. Robles-Bykbaev, Angel Perez-Munoz, Jose Hilera-Gonzalez, Salvador Oton-Tortosa, Elena Campo-Montalvo

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The increasing number of people are living with disability in the World and their access to formal education is considered a challenge for the development of the online education and educational resources. This problem is considered one of the 17 sustainable development goals that are focused on inclusive and equitable quality education. Nevertheless, the existing proposals for mainstream accessibility in virtual education are still complex to apply. However, the models, standards, and good practices to contribute to the virtual educational process and the design of learning for all are identified. For these reasons, in this paper, we describe an accessibility evaluation proposal based on 4 interaction domains: user analysis and interaction, intelligent systems, knowledge databases, and evaluation. In the same way, we describe a set of tools that constitute a Repository of Accessible Learning Objects (RALO) from the perspective of accessibility and adaptability metadata. In this line, the knowledge database follows the regulation and educational models focused on the students with disabilities needs and preferences from the conception of universal design. The validation of the proposal is based on the interaction study and analysis of regular and disabled students and teachers who developed the Learning Objects (LO). To determine whether there was consensus among the teacher's scores, we used Kendall's Coefficient of Concordance W.

Original languageEnglish
Pages (from-to)8223-8239
Number of pages17
JournalIEEE Access
Volume11
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • accessibility
  • distance learning
  • evaluation
  • Learning object
  • metadata

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