research-article

Towards Open Learner Models Including the Flow State

Authors:

Sergio Iván Ramírez Luelmo,

Jean HeutteAuthors Info & Claims

UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Pages 305 - 310

https://doi.org/10.1145/3386392.3399295

Published: 13 July 2020 Publication History

Abstract

Lifelong Learning encompasses vast learning opportunities and MOOCs are a learning environment that can be up to the challenge if current modeling challenges are addressed. Studies have shown the importance of modeling the learner for a more personal and tailored learning experience in MOOC. Furthermore, Open Learner Models have proven their added value in facilitating learner's follow-up and course content personalization. However, while modeling the learner's knowledge is a common practice, modeling the learner's psychological state is a relegated concern within the community. This is despite the myriad of scientific evidence backing up the importance and repercussion of the learner's psychological state during and on the learning process.

Flow is a psychological state characterized by total immersion in a task and a state of optimal performance. Programmers often refer to it as "being in the zone". It reliably correlates favorable learning metrics, such as motivation and engagement, among others. The aim of this paper is to propose a functional and technical architecture (comprising a Domain Model, a Flow Model, and an Open Learner Model for MOOC in a Lifelong Learning context) accounting for the learner's Flow state. This work is dedicated to MOOC designers/providers, pedagogical engineers, psychology, and education researchers who meet difficulties to incorporate and account for the Flow psychological state in a MOOC.

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Cited By

Ramírez Luelmo SEl Mawas NBachelet RHeutte J(2024)Detection and Asynchronous Flow Prediction in a MOOCSN Computer Science10.1007/s42979-024-02838-w5:5Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1007/s42979-024-02838-w
Ramírez Luelmo SEl Mawas NHeutte J(2022)Existing Machine Learning Techniques for Knowledge Tracing: A Review Using the PRISMA GuidelinesComputer Supported Education10.1007/978-3-031-14756-2_5(73-94)Online publication date: 21-Aug-2022
https://doi.org/10.1007/978-3-031-14756-2_5

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Towards Open Learner Models Including the Flow State

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cover image ACM Conferences

UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

July 2020

395 pages

ISBN:9781450379502

DOI:10.1145/3386392

Editors:
Tsvi Kuflik
University of Haifa, Israel
,
Ilaria Torre
University of Genoa, Italy
,
Robin Burke
University of Colorado, Boulder, USA
,
Cristina Gena
University of Turin, Italy

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 13 July 2020

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July 14 - 17, 2020

Genoa, Italy

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Cited By

Ramírez Luelmo SEl Mawas NBachelet RHeutte J(2024)Detection and Asynchronous Flow Prediction in a MOOCSN Computer Science10.1007/s42979-024-02838-w5:5Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1007/s42979-024-02838-w
Ramírez Luelmo SEl Mawas NHeutte J(2022)Existing Machine Learning Techniques for Knowledge Tracing: A Review Using the PRISMA GuidelinesComputer Supported Education10.1007/978-3-031-14756-2_5(73-94)Online publication date: 21-Aug-2022
https://doi.org/10.1007/978-3-031-14756-2_5

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