Predi????o de evas??o na educa????o a dist??ncia como subs??dio ?? tomada de decis??o

• Ano de defesa: 2016
• Autor(a) principal: Sep??lvida, Weslley Rodrigues
• Orientador(a): Ferneda, Edilson
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Cat??lica de Bras??lia
• Programa de Pós-Graduação: Programa Strictu Sensu em Gest??o do Conhecimento e da Tecnologia da Informa????o
• Departamento: Escola de Educa????o, Tecnologia e Comunica????o
• País: Brasil
• Palavras-chave em Português: Ensino a dist??ncia; Minera????o de dados educacionais; Ambientes virtuais de aprendizagem; Predi????o de evas??o
• Área do conhecimento CNPq: CIENCIA DA INFORMACAO
• Resumo em Inglês: Distance Education has grown along the years. Several educational institutions have been offering courses of internal improvement and qualification, as well as extension, undergraduate and postgraduate courses. The increase in the number of courses and the significant raise in the number of students result in new challenges to the educational institutions. The high dropout rates, common in Distance Education courses, is one of the crucial problems the institutions have to deal with. In this context, Data Mining is one of the main approaches for the development of predictive methods of evasion. The present paper aims an analysis of the Distance Education evasion in a traditional Midwest Brazilian University. The study intends to identify the behavior of the students who have dropped out undergraduate courses, in order to provide subsidies for the subjects involved in the teaching-learning process. As a preventive solution to the evasion issue, it is understood that proper communication with the students who are about to evade can lead to changes that contribute to minimize the problem. This study is presented in four parts: (i) literature review based on theoretical framework on Distance Education evasion, Virtual Learning Environments, Knowledge Discovery in Database (KDD) and Education Data Mining; (ii) an analysis of the Virtual Learning Environment institution database applying KDD techniques to identify the course abandonment behavior; (iii) development and validation of a model for predictive identification of students prone to evade; (iv) management actions to mitigate the problem. The results show that, when applying KDD to the variant data in time, 30 days after the beginning of the classes, it is possible to significantly predict evasion. From the results, an evasion prediction model was developed, as well as an evasion combat model.
• Link de acesso: https://bdtd.ucb.br:8443/jspui/handle/tede/2318

Distance Education has grown along the years. Several educational institutions have been offering courses of internal improvement and qualification, as well as extension, undergraduate and postgraduate courses. The increase in the number of courses and the significant raise in the number of students result in new challenges to the educational institutions. The high dropout rates, common in Distance Education courses, is one of the crucial problems the institutions have to deal with. In this context, Data Mining is one of the main approaches for the development of predictive methods of evasion. The present paper aims an analysis of the Distance Education evasion in a traditional Midwest Brazilian University. The study intends to identify the behavior of the students who have dropped out undergraduate courses, in order to provide subsidies for the subjects involved in the teaching-learning process. As a preventive solution to the evasion issue, it is understood that proper communication with the students who are about to evade can lead to changes that contribute to minimize the problem. This study is presented in four parts: (i) literature review based on theoretical framework on Distance Education evasion, Virtual Learning Environments, Knowledge Discovery in Database (KDD) and Education Data Mining; (ii) an analysis of the Virtual Learning Environment institution database applying KDD techniques to identify the course abandonment behavior; (iii) development and validation of a model for predictive identification of students prone to evade; (iv) management actions to mitigate the problem. The results show that, when applying KDD to the variant data in time, 30 days after the beginning of the classes, it is possible to significantly predict evasion. From the results, an evasion prediction model was developed, as well as an evasion combat model.