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A Generalized Approach for Context-Aware Adaptation in Mobile E-Learning Settings

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Mobile, Ubiquitous, and Pervasive Learning

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 406))

Abstract

Most existing adaptive tools and applications have been developed specifically for one or few selected scenarios. What is missing is a framework that enables the methodical development of adaptive mobile applications that support a wide variety of educational scenarios. However, challenges are to identify relevant contextual information, to enable the design of adaptive learning scenarios, even by non-technophile teachers, to create a platform-independent way of collecting contextual information, and to find a way to satisfy the different demands from universities and industry. To meet some of these challenges, this chapter introduces a systematic approach for developing mobile adaptive learning applications.

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Notes

  1. 1.

    https://cordova.apache.org/.

  2. 2.

    The work in this part of the project was implemented as part of a master’s thesis. Stefanie Lemcke, who worked on the master’s thesis, has been supported in her work by the Stiftung Industrieforschung with a scholarship.

  3. 3.

    http://www.apple.com/ios/.

  4. 4.

    http://www.android.com/.

  5. 5.

    https://www.windowsphone.com/.

  6. 6.

    https://code.google.com/syssu/.

  7. 7.

    http://requirejs.org/.

  8. 8.

    https://www.gnu.org/software/easejs/.

  9. 9.

    http://www.broofa.com/Tools/Math.uuid.js.

  10. 10.

    http://www.google.com/landing/now/.

  11. 11.

    http://www.microsoft.com/en-us/mobile/campaign-cortana/.

  12. 12.

    https://www.apple.com/ios/siri/.

  13. 13.

    Funding reference: KF3155601MS3.

Abbreviations

API:

Application programming interface

GUI:

Graphical user inferface

ITS:

Intelligent Tutoring System

MOTIVATE:

Mobile Training Via Adaptive Technologies

OWL:

Web Ontology Language

RDF:

Resource description framework

RDFS:

Resource description framework schema

REST:

Representational state transfer

SPARQL:

SPARQL protocol and RDF query language

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Acknowledgments

We would like to thank Helena Jank, Stefanie Lemcke, Martin Biermann, and Julius Höfler for the invaluable work that they have contributed to the project. We would also like to thank the BMWi for the funding of the MOTIVATE projectFootnote 12 and the Stiftung Industrieforschung for supporting Stefanie Lemcke’s master thesis with a scholarship.

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Authors and Affiliations

  1. Universität Potsdam, Institut für Informatik, Haus 4, August-Bebel-Straße 89, 14482, Potsdam, Germany

    Tobias Moebert, Raphael Zender & Ulrike Lucke

Authors
  1. Tobias Moebert
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  2. Raphael Zender
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  3. Ulrike Lucke
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Corresponding author

Correspondence to Tobias Moebert .

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Editors and Affiliations

  1. Artificial Intelligence on Education Lab, WOLNM:, Ciudad de México, Mexico

    Alejandro Peña-Ayala

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Moebert, T., Zender, R., Lucke, U. (2016). A Generalized Approach for Context-Aware Adaptation in Mobile E-Learning Settings. In: Peña-Ayala, A. (eds) Mobile, Ubiquitous, and Pervasive Learning. Advances in Intelligent Systems and Computing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-319-26518-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-26518-6_2

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  • Online ISBN: 978-3-319-26518-6

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