The PERLA Framework: Blending Personalization and Learning Analytics

Personalization is crucial for achieving smart learning environments in different lifelong learning contexts. There is a need to shift from one-size-fits-all systems to personalized learning environments that give control to the learners. Recently, learning analytics (LA) is opening up new opportuni...

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Autores principales: Mohamed Amine Chatti, Arham Muslim
Formato: article
Lenguaje:EN
Publicado: Athabasca University Press 2019
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Acceso en línea:https://doaj.org/article/01b337a207b648618a2540e169a9b56c
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spelling oai:doaj.org-article:01b337a207b648618a2540e169a9b56c2021-12-02T19:25:48ZThe PERLA Framework: Blending Personalization and Learning Analytics10.19173/irrodl.v20i1.39361492-3831https://doaj.org/article/01b337a207b648618a2540e169a9b56c2019-02-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/3936https://doaj.org/toc/1492-3831Personalization is crucial for achieving smart learning environments in different lifelong learning contexts. There is a need to shift from one-size-fits-all systems to personalized learning environments that give control to the learners. Recently, learning analytics (LA) is opening up new opportunities for promoting personalization by providing insights and understanding into how learners learn and supporting customized learning experiences that meet their goals and needs. This paper discusses the Personalization and Learning Analytics (PERLA) framework which represents the convergence of personalization and learning analytics and provides a theoretical foundation for effective analytics-enhanced personalized learning. The main aim of the PERLA framework is to guide the systematic design and development of effective indicators for personalized learning. Mohamed Amine ChattiArham MuslimAthabasca University Pressarticlepersonalizationself-regulated learninguser-centered learning analyticslearning analytics reference modelgoal-oriented learning analyticsSpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 20, Iss 1 (2019)
institution DOAJ
collection DOAJ
language EN
topic personalization
self-regulated learning
user-centered learning analytics
learning analytics reference model
goal-oriented learning analytics
Special aspects of education
LC8-6691
spellingShingle personalization
self-regulated learning
user-centered learning analytics
learning analytics reference model
goal-oriented learning analytics
Special aspects of education
LC8-6691
Mohamed Amine Chatti
Arham Muslim
The PERLA Framework: Blending Personalization and Learning Analytics
description Personalization is crucial for achieving smart learning environments in different lifelong learning contexts. There is a need to shift from one-size-fits-all systems to personalized learning environments that give control to the learners. Recently, learning analytics (LA) is opening up new opportunities for promoting personalization by providing insights and understanding into how learners learn and supporting customized learning experiences that meet their goals and needs. This paper discusses the Personalization and Learning Analytics (PERLA) framework which represents the convergence of personalization and learning analytics and provides a theoretical foundation for effective analytics-enhanced personalized learning. The main aim of the PERLA framework is to guide the systematic design and development of effective indicators for personalized learning.
format article
author Mohamed Amine Chatti
Arham Muslim
author_facet Mohamed Amine Chatti
Arham Muslim
author_sort Mohamed Amine Chatti
title The PERLA Framework: Blending Personalization and Learning Analytics
title_short The PERLA Framework: Blending Personalization and Learning Analytics
title_full The PERLA Framework: Blending Personalization and Learning Analytics
title_fullStr The PERLA Framework: Blending Personalization and Learning Analytics
title_full_unstemmed The PERLA Framework: Blending Personalization and Learning Analytics
title_sort perla framework: blending personalization and learning analytics
publisher Athabasca University Press
publishDate 2019
url https://doaj.org/article/01b337a207b648618a2540e169a9b56c
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