Adaptive learning objects in the context of eco-connectivist communities using learning analytics

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dc.citation.journalTitleHeliyoneng
dc.contributor.authorDiego, Mosquera
dc.contributor.authorCarlos, Guevara
dc.contributor.authorJose, Aguilar
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería de Sistemasspa
dc.contributor.researchgroupI+D+I en Tecnologías de la Información y las Comunicacionesspa
dc.creatorDiego, Mosquera
dc.creatorCarlos, Guevara
dc.creatorJose, Aguilar
dc.date.accessioned2021-04-12T20:55:47Z
dc.date.available2021-04-12T20:55:47Z
dc.date.issued2019-11-01
dc.description.abstractEco-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.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=9832
dc.identifier.doi10.1016/j.heliyon.2019.e02722
dc.identifier.issn24058440
dc.identifier.otherWOS;000500530100024
dc.identifier.otherPUBMED;31763467
dc.identifier.otherSCOPUS;2-s2.0-85074755662
dc.identifier.urihttp://hdl.handle.net/10784/28626
dc.language.isoengeng
dc.publisherElsevier BV
dc.relationWOS;000500530100024
dc.relationPUBMED;31763467
dc.relationSCOPUS;2-s2.0-85074755662
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85074755662&doi=10.1016%2fj.heliyon.2019.e02722&partnerID=40&md5=fbf1e6a112d6e3cf13b465884584ed93
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/2405-8440
dc.sourceHeliyon
dc.subjectEducationeng
dc.subjectComputer scienceeng
dc.subjectData analysiseng
dc.subjectConnectivismeng
dc.subjectPersonal learning environmentseng
dc.subjectLearning communitieseng
dc.subjectAdaptive learning objectseng
dc.titleAdaptive learning objects in the context of eco-connectivist communities using learning analyticseng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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