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Adaptive learning objects in the context of eco-connectivist communities using learning analytics
Eco-connectivist communities are groups of individuals with similar characteristics, which emerge in a connectivist learning process within a knowledge ecology. ARMAGAeco-c is a reflexive and autonomic middleware for the management and optimization of eco-connectivist knowledge ecologies using descr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6859233/ https://www.ncbi.nlm.nih.gov/pubmed/31763467 http://dx.doi.org/10.1016/j.heliyon.2019.e02722 |
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author | Diego, Mosquera Carlos, Guevara Jose, Aguilar |
author_facet | Diego, Mosquera Carlos, Guevara Jose, Aguilar |
author_sort | Diego, Mosquera |
collection | PubMed |
description | Eco-connectivist communities are groups of individuals with similar characteristics, which emerge in a connectivist learning process within a knowledge ecology. ARMAGAeco-c is a reflexive and autonomic middleware for the management and optimization of eco-connectivist knowledge ecologies using description, prediction and prescription models. Adaptive Learning Objects are autonomic components that seek to personalize Learning Objects according to certain contextual information, such as learning styles of the learner's, technological restrictions, among other aspects. MALO is a system that allows the management of Adaptive Learning Objects. One of the main challenges of the connectivist learning process is the adaptation of the educational context to the student needs. One of them is the learning objects. For this reason, this work has two objectives, specifying a data analytics task to determine the learning style of a student in an eco-connectivist community and, adapting instances of Adaptive Learning Objects using the learning styles of the students in the communities. We use graph theory to identify the referential member of each eco-connectivist community, and a learning paradigm detection algorithm to identify the set of activities, strategies, and tools that Adaptive Learning Objects instances should have, according to the learning style of the referential member. To test our approach, a case study is presented, which demonstrates the validity of our approach. |
format | Online Article Text |
id | pubmed-6859233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-68592332019-11-22 Adaptive learning objects in the context of eco-connectivist communities using learning analytics Diego, Mosquera Carlos, Guevara Jose, Aguilar Heliyon Article Eco-connectivist communities are groups of individuals with similar characteristics, which emerge in a connectivist learning process within a knowledge ecology. ARMAGAeco-c is a reflexive and autonomic middleware for the management and optimization of eco-connectivist knowledge ecologies using description, prediction and prescription models. Adaptive Learning Objects are autonomic components that seek to personalize Learning Objects according to certain contextual information, such as learning styles of the learner's, technological restrictions, among other aspects. MALO is a system that allows the management of Adaptive Learning Objects. One of the main challenges of the connectivist learning process is the adaptation of the educational context to the student needs. One of them is the learning objects. For this reason, this work has two objectives, specifying a data analytics task to determine the learning style of a student in an eco-connectivist community and, adapting instances of Adaptive Learning Objects using the learning styles of the students in the communities. We use graph theory to identify the referential member of each eco-connectivist community, and a learning paradigm detection algorithm to identify the set of activities, strategies, and tools that Adaptive Learning Objects instances should have, according to the learning style of the referential member. To test our approach, a case study is presented, which demonstrates the validity of our approach. Elsevier 2019-11-14 /pmc/articles/PMC6859233/ /pubmed/31763467 http://dx.doi.org/10.1016/j.heliyon.2019.e02722 Text en © 2019 Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Diego, Mosquera Carlos, Guevara Jose, Aguilar Adaptive learning objects in the context of eco-connectivist communities using learning analytics |
title | Adaptive learning objects in the context of eco-connectivist communities using learning analytics |
title_full | Adaptive learning objects in the context of eco-connectivist communities using learning analytics |
title_fullStr | Adaptive learning objects in the context of eco-connectivist communities using learning analytics |
title_full_unstemmed | Adaptive learning objects in the context of eco-connectivist communities using learning analytics |
title_short | Adaptive learning objects in the context of eco-connectivist communities using learning analytics |
title_sort | adaptive learning objects in the context of eco-connectivist communities using learning analytics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6859233/ https://www.ncbi.nlm.nih.gov/pubmed/31763467 http://dx.doi.org/10.1016/j.heliyon.2019.e02722 |
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