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Adaptive game-based learning in multi-agent educational settings

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Abstract

The traditional educational paradigm has been nowadays transformed to tech-aided personalised learning, tailored to individual learning styles and needs, applicable in any environment. Such an educational framework should provide capabilities for adaptive, affective and interactive learning, taking advantage of technological means to recognize the learners’ performance, behaviour and progress over the learning process. A novel methodology is proposed to model an educational framework able to represent and optimally foster these needs, along with a methodology for non-linearly adapting networked learning objectives. In addition, the framework is supported with an ontology that enables personalised and contextualised decision-making over learning activities on autonomous devices, enabling their dynamic modularisation during the learning process.

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Notes

  1. https://github.com/learningactionsontology/lao.

  2. Assertion Box, i.e., facts that instantiate entities of a TBox (an ontology).

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Acknowledgements

This work has been supported by the European Commission through the European Union’s Horizon 2020 Programme (H2020-ICT-2015), under Grant Agreement No. 687772 MaTHiSiS.

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Authors and Affiliations

  1. Information Technologies Institute, Centre for Research & Technology Hellas, Thessaloníki, Greece

    Dorothea Tsatsou, Nicholas Vretos & Petros Daras

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  1. Dorothea Tsatsou
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  2. Nicholas Vretos
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  3. Petros Daras
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Correspondence to Dorothea Tsatsou.

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Tsatsou, D., Vretos, N. & Daras, P. Adaptive game-based learning in multi-agent educational settings. J. Comput. Educ. 6, 215–239 (2019). https://doi.org/10.1007/s40692-018-0118-9

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  • DOI: https://doi.org/10.1007/s40692-018-0118-9

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