Data-driven Behavioural and Affective Nudging of Online Learners: System Architecture and Design | SpringerLink
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Data-driven Behavioural and Affective Nudging of Online Learners: System Architecture and Design

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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (AIED 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13356))

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Abstract

In this work-in-progress paper, we describe the architecture of a system that can automatically sense an online learner’s situation and context (affective-cognitive state, fatigue, cognitive load, and physical environment), analyse the needs for intervention, and react through an intelligent agent to shape the learner’s self-regulated learning strategies. The paper describes the system concept and its software architecture and design: what sensory data are captured and how they are processed, analysed, and integrated; what intervention decision will follow and what behavioural and affective nudges will be given.

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Author information

Authors and Affiliations

  1. Queen Mary University of London, London, UK

    Marie-Luce Bourguet

  2. Tokyo Institute of Technology, Tokyo, Japan

    Jacqueline Urakami

  3. Tokyo University of Agriculture and Technology, Fuchu, Japan

    Gentiane Venture

Authors
  1. Marie-Luce Bourguet
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  2. Jacqueline Urakami
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  3. Gentiane Venture
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Corresponding author

Correspondence to Marie-Luce Bourguet .

Editor information

Editors and Affiliations

  1. Ateneo De Manila University, Quezon, Philippines

    Maria Mercedes Rodrigo

  2. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

    Noburu Matsuda

  3. Durham University, Durham, UK

    Alexandra I. Cristea

  4. University of Leeds, Leeds, UK

    Vania Dimitrova

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Bourguet, ML., Urakami, J., Venture, G. (2022). Data-driven Behavioural and Affective Nudging of Online Learners: System Architecture and Design. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_117

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  • DOI: https://doi.org/10.1007/978-3-031-11647-6_117

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11646-9

  • Online ISBN: 978-3-031-11647-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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