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Personal and Ubiquitous Computing

, Volume 20, Issue 2, pp 167–184 | Cite as

A model for learning objects adaptation in light of mobile and context-aware computing

  • Márcia Abech
  • Cristiano André da Costa
  • Jorge Luis Victória Barbosa
  • Sandro José Rigo
  • Rodrigo da Rosa Righi
Original Article

Abstract

The growth usage of mobile technologies and devices such as smartphones and tablets, and the almost ubiquitous wireless communication set the stage for the development of novel kinds of applications. One possibility is exploiting this scenario in the field of education, so creating more intelligent, flexible and customizable systems. Mobile devices can be used to help students to learn, considering their learning styles, surroundings, devices and profiles. In this way, the main goal of this article is to propose EduAdapt, an architectural model for the adaptation of learning objects considering device characteristics, learning style and other student’s context information. To make this adaptation we used inferences and rules in a proposed ontology, named OntoAdapt. We believe that such ontology can help recommending learning objects to students or adapt these objects according to the context (context-aware computing). We evaluate this proposal in two ways. Firstly, we used scenarios and metrics to assess the ontology. Secondly, we developed a prototype of EduAdapt model and submitted to a class of 20 students with the intention of evaluating the usability and adherence to adapted objects, resulting in a 78 % of acceptance. In brief, the evaluation presented encouraging results, indicating that the proposed model would be useful in the learning process.

Keywords

Learning style Dynamic adaptation e-learning Mobile computing Context awareness Ubiquitous learning 

Notes

Acknowledgments

The authors would like to thank National Council for Scientific and Technological Development - CNPQ and GVDASA Sistemas (http://www.gvdasa.com.br) for financial support which made possible the development of this work.

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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Márcia Abech
    • 1
  • Cristiano André da Costa
    • 1
  • Jorge Luis Victória Barbosa
    • 1
  • Sandro José Rigo
    • 1
  • Rodrigo da Rosa Righi
    • 1
  1. 1.Applied Computing Graduate ProgramUniversidade do Vale do Rio dos SinosSão LeopoldoBrazil

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