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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zimmerman, B.J.: From cognitive modeling to self-regulation: a social cognitive career path. Educ. Psychol. 48(3), 135–147 (2013). https://doi.org/10.1080/00461520.2013.794676
Harley, J.M., Taub, M., Bouchet, F., et al.: A framework to understand the nature of co-regulated learning in human-pedagogical agent interactions. In: 11th International Conference on Intelligent Tutoring Systems, Crete (2012)
Kuppens, P.: It’s about time: a special section on affect dynamics. Emot. Rev. 7(4), 297–300 (2015)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement, arxiv:1804.02767 Tech report (2018)
Baker, R.S., d’Mello, S.K., Rodrigo, W.T., et al.: Better to be frustrated than bored: the incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. Int. J. Hum. Comput. Stud. 68, 223–241 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-11647-6_117
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11646-9
Online ISBN: 978-3-031-11647-6
eBook Packages: Computer ScienceComputer Science (R0)