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The “flat peer learning” agent-based model

This paper deals with peer learning and, in particular, with the phenomena of exclusion; it proposes to model a group of learners where everyone has his own behaviour that expresses his way of following a curriculum. The focus is on individual motivations that avoid disadvantage certain individuals...

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Detalles Bibliográficos
Autor principal: Collard, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8164895/
https://www.ncbi.nlm.nih.gov/pubmed/34095600
http://dx.doi.org/10.1007/s42001-021-00120-0
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author Collard, Philippe
author_facet Collard, Philippe
author_sort Collard, Philippe
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description This paper deals with peer learning and, in particular, with the phenomena of exclusion; it proposes to model a group of learners where everyone has his own behaviour that expresses his way of following a curriculum. The focus is on individual motivations that avoid disadvantage certain individuals while optimising behaviour at the community level; in this context, the approach is based on the belief that the induced learning dynamics can be clarified by the contribution of agent-based modelling and its entry into the field of peer learning simulation. Flat learning means here that every learner features the same initial skill level, along with the same opportunities to learn both independently and with the help of peers. To address this topic the paper proposes the Flat Peer Learning agent-based computational model inspired by the Vygotsky’s social and learning theory. The paper shows that even if strict equity could be guaranteed, educators would still be faced with the dilemma of having to choose between optimising the learning process for the group or preventing exclusion for some.
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spelling pubmed-81648952021-06-01 The “flat peer learning” agent-based model Collard, Philippe J Comput Soc Sci Research Article This paper deals with peer learning and, in particular, with the phenomena of exclusion; it proposes to model a group of learners where everyone has his own behaviour that expresses his way of following a curriculum. The focus is on individual motivations that avoid disadvantage certain individuals while optimising behaviour at the community level; in this context, the approach is based on the belief that the induced learning dynamics can be clarified by the contribution of agent-based modelling and its entry into the field of peer learning simulation. Flat learning means here that every learner features the same initial skill level, along with the same opportunities to learn both independently and with the help of peers. To address this topic the paper proposes the Flat Peer Learning agent-based computational model inspired by the Vygotsky’s social and learning theory. The paper shows that even if strict equity could be guaranteed, educators would still be faced with the dilemma of having to choose between optimising the learning process for the group or preventing exclusion for some. Springer Nature Singapore 2021-05-30 2022 /pmc/articles/PMC8164895/ /pubmed/34095600 http://dx.doi.org/10.1007/s42001-021-00120-0 Text en © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Research Article
Collard, Philippe
The “flat peer learning” agent-based model
title The “flat peer learning” agent-based model
title_full The “flat peer learning” agent-based model
title_fullStr The “flat peer learning” agent-based model
title_full_unstemmed The “flat peer learning” agent-based model
title_short The “flat peer learning” agent-based model
title_sort “flat peer learning” agent-based model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8164895/
https://www.ncbi.nlm.nih.gov/pubmed/34095600
http://dx.doi.org/10.1007/s42001-021-00120-0
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