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Adaptive Learning Based on Affect Sensing

  • Dorothea Tsatsou
  • Andrew Pomazanskyi
  • Enrique Hortal
  • Evaggelos Spyrou
  • Helen C. Leligou
  • Stylianos Asteriadis
  • Nicholas Vretos
  • Petros Daras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10948)

Abstract

This paper introduces an end-to-end solution for dynamic adaptation of the learning experience for learners of different personal needs, based on their behavioural and affective reaction to the learning activities. Personal needs refer to what learner already know, what they need to learn, their intellectual and physical capacities and their learning styles.

Notes

Acknowledgments

This work has been supported by the European Commission under Grant Agreement No. 687772 MaTHiSiS.

References

  1. 1.
    Antonaras, D., Pavlidis, C., Vretos, N., Daras, P.: Affect state recognition for adaptive human robot interaction in learning environments. In: 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 71–75 (2017)Google Scholar
  2. 2.
    Athanasiadis, C., Hortal, E., Koutsoukos, D., Lens, C.Z., Asteriadis, S.: Personalized, affect and performance-driven computer-based learning. In: Proceedings of the 9th International Conference on Computer Supported Education, CSEDU, vol. 1 (2017)Google Scholar
  3. 3.
    D’Mello, S.K., Graesser, A.: Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Model. User Adapt. Interact. 20(2), 147–187 (2010)CrossRefGoogle Scholar
  4. 4.
    Hamari, J., Shernoff, D.J., Rowe, E., Coller, B., Asbell-Clarke, J., Edwards, T.: Challenging games help students learn: an empirical study on engagement, flow and immersion in game-based learning. Comput. Hum. Behav. 54, 170–179 (2016)CrossRefGoogle Scholar
  5. 5.
    Kevan, J.M., Ryan, P.R.: Experience API: flexible, decentralized and activity-centric data collection. Technol. Knowl. Learn. 21(1), 143–149 (2016)CrossRefGoogle Scholar
  6. 6.
    Mazziotti, C., Holmes, W., Wiedmann, M., Loibl, K., Rummel, N., Mavrikis, M., Hansen, A., Grawemeyer, B.: Robust student knowledge: adapting to individual student needs as they explore the concepts and practice the procedures of fractions. In: Workshop on Intelligent Support in Exploratory and Open-Ended Learning Environments Learning Analytics for Project Based and Experiential Learning Scenarios at the 17th International Conference on Artificial Intelligence in Education (AIED 2015), pp. 32–40 (2015)Google Scholar
  7. 7.
    Papakostas, M., Spyrou, E., Giannakopoulos, T., Siantikos, G., Sgouropoulos, D., Mylonas, P., Makedon, F.: Deep visual attributes vs. hand-crafted audio features on multidomain speech emotion recognition. Computation 5(2), 26 (2017)CrossRefGoogle Scholar
  8. 8.
    Santos, O.C., Saneiro, M., Salmeron-Majadas, S., Boticario, J.G.: A methodological approach to eliciting affective educational recommendations. In: IEEE 14th International Conference on Advanced Learning Technologies (ICALT), pp. 529–533. IEEE (2014)Google Scholar
  9. 9.
    Tsatsou, D., Vretos, N., Daras, P.: Modelling learning experiences in adaptive multi-agent learning environments. In: 9th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games), pp. 193–200 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Dorothea Tsatsou
    • 1
  • Andrew Pomazanskyi
    • 2
  • Enrique Hortal
    • 3
  • Evaggelos Spyrou
    • 4
  • Helen C. Leligou
    • 5
  • Stylianos Asteriadis
    • 3
  • Nicholas Vretos
    • 1
  • Petros Daras
    • 1
  1. 1.Information Technologies Institute, Centre for Research & Technology HellasThessalonikiGreece
  2. 2.NurogamesCologneGermany
  3. 3.University of MaastrichtMaastrichtThe Netherlands
  4. 4.Institute of Informatics & Telecommunications, National Centre for Scientific Research “Demokritos”AthensGreece
  5. 5.OTE AcademyAthensGreece

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