Skip to Main content Skip to Navigation
Book sections

Towards Open Learner Models including the Flow state

Sergio Ramirez 1, 2 Nour El Mawas 1, 2 Jean Heutte 1, 2
2 Trigone-CIREL
CIREL - Centre Interuniversitaire de Recherche en Education de Lille - ULR 4354 : EA1038
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.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-02561368
Contributor : Jean Heutte <>
Submitted on : Sunday, May 3, 2020 - 7:28:39 PM
Last modification on : Friday, March 5, 2021 - 3:28:41 AM

Identifiers

Collections

Citation

Sergio Ramirez, Nour El Mawas, Jean Heutte. Towards Open Learner Models including the Flow state. In T. Kuflik & I. Torre (dir.). UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery, New York, NY United States, pp.305-310, 2020, 978-1-4503-7950-2. ⟨10.1145/3386392.3399295⟩. ⟨hal-02561368⟩

Share

Metrics

Record views

118