{"id":{"repo_id":"brazil-ufrn","oai_identifier":"oai:repositorio.ufrn.br:123456789/25609"},"canonical_url":"https://search.dev.ndltd.org/etd/brazil-ufrn/oai:repositorio.ufrn.br:123456789/25609","repository":{"repo_id":"brazil-ufrn","name":"Brazil UFRN","base_url":"https://repositorio.ufrn.br/server/oai/request"},"display":{"title":"Magister - Metodologia de análise de programas de educação à distância baseada em Learning Analytics","abstract":"The increasing of the data registered in courses offered in the distance modality boost the use of computational methods adapted to the research and the grouping of educational data, aiming to discover learning behaviors patterns. This research area allows the development of automated monitoring, prediction and intervention tools aiming at improving the educational indexes. As a result, this work proposes a methodology for analyzing distance learning programs based on the Learning Analytics technology, using the students’ access data to the Learning Management System (LMS), identifying the most frequent sequential patterns of use and classifying them as according to the self-regulated learning categories. For a sequential mining of sequential data the SPAM and VGEN algorithms were applied to the databases of two educational institutions. In addition to the development of the methodology, as a result of processing, a high incidence of behavior not predicted in the self-regulated learning theory was identified, and to classify it was created a pattern called low participation.","abstract_html":"The increasing of the data registered in courses offered in the distance modality boost the use of computational methods adapted to the research and the grouping of educational data, aiming to discover learning behaviors patterns. This research area allows the development of automated monitoring, prediction and intervention tools aiming at improving the educational indexes. As a result, this work proposes a methodology for analyzing distance learning programs based on the Learning Analytics technology, using the students’ access data to the Learning Management System (LMS), identifying the most frequent sequential patterns of use and classifying them as according to the self-regulated learning categories. For a sequential mining of sequential data the SPAM and VGEN algorithms were applied to the databases of two educational institutions. In addition to the development of the methodology, as a result of processing, a high incidence of behavior not predicted in the self-regulated learning theory was identified, and to classify it was created a pattern called low participation.","abstract_has_math":false,"creators":["Lacerda, Ivan Max Freire de"],"institution":"Brasil","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Valentim, Ricardo Alexsandro de Medeiros"],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-02","date_published":"2018-03-02","updated_at":"2026-07-24T01:20:51Z","subjects":["Aprendizagem autorregulada","Ensino a distância","Ambiente virtual de aprendizagem","Mineração de dados educacionais","Learning analytics"],"languages":["por"],"rights":["Acesso Aberto"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repositorio.ufrn.br/jspui/handle/123456789/25609","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Valentim, Ricardo Alexsandro de Medeiros"]},{"key":"dc:creator","label":"Author","values":["Lacerda, Ivan Max Freire de"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-07-26T19:49:52Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-07-26T19:49:52Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-03-02"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Aprendizagem autorregulada","Ensino a distância","Ambiente virtual de aprendizagem","Mineração de dados educacionais","Learning analytics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["por"]},{"key":"dc:rights","label":"Dc Rights","values":["Acesso Aberto"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://repositorio.ufrn.br/jspui/handle/123456789/25609"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The increasing of the data registered in courses offered in the distance modality boost the use of computational methods adapted to the research and the grouping of educational data, aiming to discover learning behaviors patterns. This research area allows the development of automated monitoring, prediction and intervention tools aiming at improving the educational indexes. As a result, this work proposes a methodology for analyzing distance learning programs based on the Learning Analytics technology, using the students’ access data to the Learning Management System (LMS), identifying the most frequent sequential patterns of use and classifying them as according to the self-regulated learning categories. For a sequential mining of sequential data the SPAM and VGEN algorithms were applied to the databases of two educational institutions. 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