Formação de grupos em MOOCs utilizando Particle Swarm Optimization

• Ano de defesa: 2016
• Autor(a) principal: Ullmann, Matheus Rudolfo Diedrich
• Orientador(a): Ferreira, Deller James
• Banca de defesa: Ferreira, Deller James, Camilo Júnior, Celso Gonçalves, Marques, Fátima de Lourdes dos Santos Nunes, Carvalho, Cedric Luiz de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: MOOC; Cursos online; Massivos abertos; CSCL; Aprendizagem colaborativa; Aprendizagem a distância; PSO; Otimização porenxamedepartículas; Análise combinatória; Composição de grupos; Formaçãode grupos
• Palavras-chave em Inglês: MOOC; Massive open online courses; CSCL; Colaborative learning; Distance learning; PSO; Particle swarm optimization; Combinatorial analysis; Group composition; Group formation
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/5609

The MassiveOpenOnlineCourses(MOOCs)areonlinecourseswithopenenrollment that involvingahugeamountofstudentsfromdifferentlocations,withdifferentback- grounds andinterests.Thelargenumberofstudentsimpliesahugeandunmanageable number ofinteractions.Thisfact,alongwiththedifferentinterestsofstudents,resulting in low-qualityinteractions.Duetothelargenumberofstudents,alsobecomesunviable composition manuallylearninggroups.DuetothesecharacteristicspresentinMOOCs, a methodforforminggroupswasdevelopedinthiswork,asanattempttoattendthedi- chotomy existsbetweenthecollective,whichinvolvestheformationofanonlinelearning community onamassivescale,andindividual,withdifferentinterests,priorknowledge and expectationsanddifferentleadershipprofiles.Fortheformationofgroups,anadapta- tion ofParticleSwarmOptimizationalgorithmwasproposedbasedonthreecriteria,kno- wledge level,interestsandleadershipprofiles,formingthengroupswithdifferentlevels of knowledge,similarinterestsanddistributedleadership,providingbetterinteractionand knowledgeconstruction.Werecreatedtwovariationsoftheproblem,withfivestudents and theothersix.Basedoncomputationaltests,thealgorithmdemonstratedthatableto attend thegroupingcriteriainasatisfactorycomputingtimeandismoreefficientthanthe model randomgroupsformation.Thetestsalsodemonstratedthatthealgorithmisrobust taking intoaccountthevariousdatasetsanditerationsvariations.Toevaluatethequality of interactionsandknowledgebuildingingroupsformedbythemethod,Acasestudy wasconducted;andfortheanalysisofthecollecteddiscourses,itwastakenasthebasis twomodelsofdiscourseanalysisfoundintheliterature.Theresultsofthecasestudy demonstrated thatthegroupsformedbytheproposedmethodachievedthebestresultsin the interactionsandknowledgeconstruction,whencomparedwithgroupsthatdonotuse it.