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Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem

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Recommender Systems for Technology Enhanced Learning

Abstract

A Technology Enhanced Learning (TEL) ecosystem is a kind of Digital Ecosystem formed by independent platforms combined and used by learners to support their learning. Related work shows that recommendations in TEL can support learners and that in TEL ecosystems, learners do use different platforms. We therefore pursue the goal to enable recommendations across different platforms by exploiting the synergies between them to benefit learners. However, building such cross-platform recommender systems poses new and unique technological challenges for developers. In this paper, we discuss the challenges faced and present a framework, with a running example, for the development of cross-platform recommender systems for TEL ecosystems. The framework decouples the development of the recommender system from the evolution of the specific platforms and allows the integration of different recommendation algorithms by combining graph-based algorithms. As proof of concept, the framework was effectively applied and evaluated to develop a cross-platform recommender system in a TEL ecosystem comprising Moodle as the Learning Management System, and MediaWiki customized as Learning Object Repository. For future work, the integration of different recommendation algorithms and a user study on the benefits of recommendations from different sources in a learning scenario is planned.

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Notes

  1. 1.

    http://facebook.com (last accessed 25/2/2013).

  2. 2.

    http://twitter.com (last accessed 25/2/2013).

  3. 3.

    http://ocw.mit.edu/index.htm (last accessed 25/2/2013).

  4. 4.

    http://www.ariadne-eu.org/ (last accessed 25/2/2013).

  5. 5.

    http://www.moodle.org (last accessed 25/2/2013).

  6. 6.

    http://www.coursera.org/(last retrieved 25/2/2013).

  7. 7.

    http://www.mendeley.com/(last accessed 25/2/2013).

  8. 8.

    http://www.amazon.com (last accessed 25/2/2013).

  9. 9.

    http://www.netflix.com (last accessed 25/2/2013).

  10. 10.

    http://www.mahara.org (last accessed on 25/2/2013).

  11. 11.

    http://www.mongodb.org (last accessed 25/2/2013).

  12. 12.

    The Mediawiki was just available when the course was about to end (as it was being prepared for the 2013 edition of the course) therefore usage data for the Wiki does not reflect the activity of the scenario and we did not take it into account.

  13. 13.

    http://www.demo.crokodil.de (last accessed 25/2/2013).

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Acknowledgements

 This work was partially funded by the Argentinean Ministry of Science and Technology and the German Academic Exchange Service as part of the bi-national cooperation project “Semantic support for collaborative learning and knowledge management”.

Author information

Authors and Affiliations

  1. LIFIA, CIC/Facultad de Informática. UNLP, La Plata, Argentina

    Alejandro Fernández

  2. UCALP, La Plata, Argentina

    Alejandro Fernández

  3. Multimedia Communications Lab, Technische Universität Darmstadt, Darmstadt, Germany

    Mojisola Erdt & Christoph Rensing

  4. LIFIA, Facultad de Informática. UNLP, La Plata, Argentina

    Ivan Dackiewicz

Authors
  1. Alejandro Fernández
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  2. Mojisola Erdt
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  3. Ivan Dackiewicz
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  4. Christoph Rensing
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Corresponding author

Correspondence to Alejandro Fernández .

Editor information

Editors and Affiliations

  1. Agro-Know, Athens, Greece

    Nikos Manouselis

  2. Faculty of Psychology and Educational Sciences, Welten Institute – Research Centre for Learning, Teaching and Technology, Open University of the Netherlands, Heerlen, The Netherlands

    Hendrik Drachsler

  3. Department of Computer Science, VUB & KU Leuven, Leuven, Belgium

    Katrien Verbert

  4. aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, Madrid, Spain

    Olga C. Santos

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Fernández, A., Erdt, M., Dackiewicz, I., Rensing, C. (2014). Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem. In: Manouselis, N., Drachsler, H., Verbert, K., Santos, O. (eds) Recommender Systems for Technology Enhanced Learning. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0530-0_12

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  • DOI: https://doi.org/10.1007/978-1-4939-0530-0_12

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