Exploiting IoT Technologies for Personalized Learning

Evangelos Spyrou*, Nikos Vretos, Andrew Pomazanskyi, Stylianos Asteriadis, Helen Leligou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

6 Citations (Web of Science)


This paper presents the IoT ready platform of the MaTHiSiS H2020 EU project. Sensing devices are used to capture the affect of learners during their interaction with learning material, which comes in the form of serious games although other forms are also considered. This interaction may use mobile devices such as smart mobile phones and tablets, but also robots. Within the context of MaTHiSiS, a learning process is broken down into "learning atoms", i.e., pieces of knowledge that may not be further divided. A set of learning atoms leads to a "learning goal", which is set by the tutor. The process of learning is non-linear, i.e., the order of learning activities that are presented to a user and are connected with a learning atom may be different per user. This personalization process may also have an influence in the difficulty of the learning actions and is modeled using the concept of the "learning graph". The overall system architecture complies to the IoT paradigm. A set of representative serious games developed for different use cases that exploit the available IoT infrastructure to personalize the learning experience is also presented.
Original languageEnglish
Title of host publicationIEEE Conference on Computational Intelligence and Games (CIG 2018)
Number of pages8
ISBN (Print)9781538643594
Publication statusPublished - 2018
Event14th IEEE Conference on Computational Intelligence and Games (CIG) - Department of Data Science and Knowledge Engineering, Maastricht, Netherlands
Duration: 14 Aug 201817 Aug 2018

Publication series

SeriesIEEE Conference on Computational Intelligence and Games


Conference14th IEEE Conference on Computational Intelligence and Games (CIG)
Internet address


  • IoT
  • learning
  • affect recognition
  • serious games

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