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Learning Analytics in Distance and Mobile Learning for Designing Personalised Software

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Machine Learning Paradigms

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 158))

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Abstract

Distance Learning, in its synchronous and asynchronous form, has gained an increasing interest over the last decades, both because of the realization of the “digital era” and also due to the reachability and accessibility it offered to human education. Lately, mobile learning has also been gaining a lot of interest due to the widespread popularity of Smartphones. In order to improve human educational interaction with Personal Computers and Smartphones, collecting learning analytics data and utilizing them is considered as a valuable requirement. Distance and mobile learning analytics may improve and assist the entire learning process by providing personalized software solutions. This paper focuses on the collection and the combination of the learning analytics data offered by different modalities in Personal Computers and modern Smartphones. For this combination two different Multi-Criteria Decision making theories are used, namely the Analytical Hierarchy Process and the Simple Additive Weighting model.

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Author information

Authors and Affiliations

  1. Ionian University, Zakynthos, Greece

    Katerina Kabassi

  2. University of Piraeus, Piraeus, Greece

    Efthimios Alepis

Authors
  1. Katerina Kabassi
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  2. Efthimios Alepis
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Correspondence to Katerina Kabassi .

Editor information

Editors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Efthimios Alepis

  3. University of Piraeus, Pireas, Greece

    George A. Tsihrintzis

  4. Faculty of Science, Technology and Mathematics, University of Canberra, Canberra, SA, Australia

    Lakhmi C. Jain

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Kabassi, K., Alepis, E. (2020). Learning Analytics in Distance and Mobile Learning for Designing Personalised Software. In: Virvou, M., Alepis, E., Tsihrintzis, G., Jain, L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-13743-4_10

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