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The Future of Ubiquitous Learning pp 259–271Cite as

Understanding Cognitive Profiles in Designing Personalized Learning Environments

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Abstract

Understanding the learners’ cognitive characteristics and designing the personalized learning environments accordingly is quite a challenging task. Although various models and frameworks have been proposed when designing adaptive environments, it is less understood how these cognitive characteristics are determined and how different personal characteristics change when exposed to various media and design choices. Therefore, this chapter first aims to introduce neuropsychological tests and their potential uses in determining cognitive profiles. Secondly, existing research will be reviewed to discuss how those individual cognitive characteristics yield different results while interacting with the content. Finally, some recommendations will be made for further research.

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Acknowledgements

I would like to thank anonymous referees and C. Pollack at GSE at Harvard University for their valuable feedback.

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Authors and Affiliations

  1. College of Education, Department of Computer Education and Instructional Technologies Beytepe, Hacettepe University, Ankara, Turkey

    Arif Altun

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  1. Arif Altun
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Correspondence to Arif Altun .

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Editors and Affiliations

  1. Facultat de Pedagogia, University of Barcelona, Barcelona, Spain

    Begoña Gros

  2. Athabasca University, Edmonton, Alberta, Canada

    Kinshuk

  3. Department of Psychology and Education, Universitat Oberta de Catalunya, Barcelona, Spain

    Marcelo Maina

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Altun, A. (2016). Understanding Cognitive Profiles in Designing Personalized Learning Environments. In: Gros, B., Kinshuk, ., Maina, M. (eds) The Future of Ubiquitous Learning. Lecture Notes in Educational Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47724-3_14

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  • DOI: https://doi.org/10.1007/978-3-662-47724-3_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47723-6

  • Online ISBN: 978-3-662-47724-3

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