Submitted by Sara Ribeiro (sara.ribeiro@ucb.br) on 2017-11-25T11:49:15Z No. of bitstreams: 1 WeslleyRodriguesSepulvidaDissertacao2016.pdf: 1633691 bytes, checksum: 6449c77ed21fbc52e7c304fd7364796d (MD5) / Approved for entry into archive by Sara Ribeiro (sara.ribeiro@ucb.br) on 2017-11-25T11:49:41Z (GMT) No. of bitstreams: 1 WeslleyRodriguesSepulvidaDissertacao2016.pdf: 1633691 bytes, checksum: 6449c77ed21fbc52e7c304fd7364796d (MD5) / Made available in DSpace on 2017-11-25T11:49:41Z (GMT). No. of bitstreams: 1 WeslleyRodriguesSepulvidaDissertacao2016.pdf: 1633691 bytes, checksum: 6449c77ed21fbc52e7c304fd7364796d (MD5) Previous issue date: 2016-08-29 / 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. / A educa????o a dist??ncia (EAD) tem crescido nos ??ltimos anos. V??rias institui????es de ensino t??m ofertado cursos que v??o desde aperfei??oamentos e capacita????es internas at?? cursos de extens??o, gradua????o e p??s-gradua????o. Com o crescimento da oferta de cursos e o aumento significativo dos estudantes, as institui????es educacionais se colocam frente a novos desafios, entre eles o combate das altas taxas de evas??o, comum em cursos na modalidade EAD. Nesse sentido, a Minera????o de Dados ?? uma das abordagens que vem sendo explorada para o desenvolvimento de m??todos preditivos de evas??o. O presente trabalho prop??e uma an??lise da evas??o no contexto da EAD de uma tradicional universidade do Centro Oeste. O estudo busca identificar comportamentos dos estudantes que abandonaram cursos de gradua????o nessa modalidade, de maneira a fornecer subs??dios preditivos para os atores envolvidos no processo de ensino-aprendizagem de forma a apoiar a tomada de decis??es preventivas a respeito da evas??o. Parte-se do pressuposto de que interven????es junto aos estudantes propensos a evadir podem acarretar mudan??a no comportamento via Ambiente Virtual de Aprendizagem (AVA) que contribuem para minimizar a evas??o. Este trabalho ?? dividido em quatro partes: (i) uma revis??o da literatura, para embasamento te??rico, sobre evas??o no ??mbito da EAD, ambientes virtuais de aprendizagem (AVA), knowledge Discovery in Database (KDD) e Education Data Mining; (ii) an??lise da base de dados do AVA utilizado pela institui????o aplicando t??cnicas de KDD, para identifica????o do comportamento de abandono do curso; (iii) gera????o e valida????o de um modelo para identifica????o preditiva de estudantes propensos a evadir; (iv) A????es Gerenciais propostas para mitiga????o do problema. Os resultados mostram que ao aplicar o KDD nos dados variantes no tempo, com 30 dias ap??s o in??cio das aulas, ?? poss??vel predizer evas??o com precis??o significativa. A partir dos resultados obtidos, gerou-se um modelo de predi????o de evas??o bem como um modelo de tomada de decis??o e a????es de combate ?? evas??o.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-03-02T22:44:37Z No. of bitstreams: 1 PriscillaSueneDeSantanaNogueira_DISSERT.pdf: 2990260 bytes, checksum: b2f0adece207327dfcf45f7d23b39fd4 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-03-03T22:56:49Z (GMT) No. of bitstreams: 1 PriscillaSueneDeSantanaNogueira_DISSERT.pdf: 2990260 bytes, checksum: b2f0adece207327dfcf45f7d23b39fd4 (MD5) / Made available in DSpace on 2016-03-03T22:56:49Z (GMT). No. of bitstreams: 1 PriscillaSueneDeSantanaNogueira_DISSERT.pdf: 2990260 bytes, checksum: b2f0adece207327dfcf45f7d23b39fd4 (MD5) Previous issue date: 2015-02-06 / A minera??o de dados educacionais (MDE) ? um dom?nio de aplica??o na ?rea de Intelig?ncia artificial que tem sido bastante explorado na atualidade. Os avan?os tecnol?gicos e em especial, a crescente utiliza??o dos ambientes virtuais de aprendizagem t?m permitido a gera??o de consider?veis quantidades de dados a serem investigados. Dentre as atividades a serem tratadas nesse contexto est? a predi??o de rendimento escolar de estudantes, a qual pode ser realizada atrav?s do emprego de t?cnicas de aprendizado de m?quina. Tais t?cnicas podem ser utilizadas para classifica??o dos estudantes em r?tulos previamente definidos. Uma das estrat?gias para aplica??o dessas t?cnicas consiste em combin?-las no projeto de sistemas multiclassificadores, cuja efici?ncia pode ser comprovada por resultados j? alcan?ados em outros trabalhos realizados em diversas ?reas, tais como: medicina, com?rcio e biometria. Os dados utilizados nos experimentos foram obtidos por meio das intera??es entre estudantes em um dos mais utilizados ambientes virtuais de aprendizagem denominado moodle. Diante desse breve panorama, o presente trabalho apresenta resultados de diversos experimentos que incluem o emprego de sistemas multiclassifcadores espec?ficos, denominados comit?s de classificadores, desse modo visando alcan?ar melhores resultados na predi??o de rendimento escolar, ou seja, na busca por maiores percentuais de acur?cia na classifica??o dos estudantes; apresentando uma significativa explora??o de dados educacionais e an?lises relevantes advindas desses experimentos. / Educational Data Mining is an application domain in artificial intelligence area that has been extensively explored nowadays. Technological advances and in particular, the increasing use of virtual learning environments have allowed the generation of considerable amounts of data to be investigated. Among the activities to be treated in this context exists the prediction of school performance of the students, which can be accomplished through the use of machine learning techniques. Such techniques may be used for student?s classification in predefined labels. One of the strategies to apply these techniques consists in their combination to design multi-classifier systems, which efficiency can be proven by results achieved in other studies conducted in several areas, such as medicine, commerce and biometrics. The data used in the experiments were obtained from the interactions between students in one of the most used virtual learning environments called Moodle. In this context, this paper presents the results of several experiments that include the use of specific multi-classifier systems systems, called ensembles, aiming to reach better results in school performance prediction that is, searching for highest accuracy percentage in the student?s classification. Therefore, this paper presents a significant exploration of educational data and it shows analyzes of relevant results about these experiments.

Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2018-05-23T19:00:06Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) warley_leite_fernandes.pdf: 2460576 bytes, checksum: d929e82a2e47dac8f54b1a1d52ed28fb (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2018-06-05T14:49:36Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) warley_leite_fernandes.pdf: 2460576 bytes, checksum: d929e82a2e47dac8f54b1a1d52ed28fb (MD5) / Made available in DSpace on 2018-06-05T14:49:36Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) warley_leite_fernandes.pdf: 2460576 bytes, checksum: d929e82a2e47dac8f54b1a1d52ed28fb (MD5) Previous issue date: 2017 / A Educa??o a Dist?ncia (EAD) tem-se confirmado como importante ferramenta de capacita??o a qualquer tempo e dist?ncia. Por?m, a maioria das Institui??es de Ensino tem encontrado dificuldades relacionadas ao grande n?mero de abandono dos cursos. Avan?os recentes em diversas ?reas da tecnologia possibilitaram o surgimento das Tecnologias da Informa??o e Comunica??o que se tornaram essenciais ? condu??o dos processos educacionais. Assim, imensos volumes de dados s?o gerados pela intera??o de usu?rios em Ambientes Virtuais de Aprendizagem (AVA). Esses dados ?escondem? informa??es ricas. Contudo, manipular tamanha quantidade de dados n?o ? uma tarefa simples. Neste sentido, uma solu??o promissora para extra??o de informa??o ? a Minera??o de Dados, que pode ser entendida como a transforma??o de dados brutos em conhecimento. Essa pesquisa apresenta um estudo para compreender os motivos do baixo desempenho dos alunos em cursos t?cnicos da EAD aplicando, para isto, o algoritmo de Classifica??o Associativa (CBA) em Minera??o de Dados Educacionais (EDM). Com o objetivo de gerar os melhores resultados preditivos de Classifica??o Associativa obtidos pelo CBA, aplicou-se o algoritmo de Regras de Associa??o denominado Predictive Apriori,ainda n?o empregados em trabalhos correlatos. Os resultados experimentais apontam que o CBA aplicado a Bases de Dados Educacionais atinge melhores resultados que os algoritmos de classifica??o tradicionais (alcan?ando uma marca de 85% de acur?cia). Mostrou-se tamb?m que o uso das ferramentas f?rum, quiz e folder t?m uma grande influ?ncia no desempenho dos estudantes. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017. / Distance Education (EAD) has been confirmed as an important training tool at any time and distance. However, most educational institutions have encountered difficulties related to the large number of dropouts. Recent advances in several areas of technology have enabled the emergence of Information and Communication Technologies that have become essential to the conduct of educational processes. Thus, immense data volumes are generated by the interaction of users in Virtual Learning Environments (AVA). These data "hide" rich information. However, handling such a large amount of data is not a simple task. In this sense, a promising solution for information extraction is Data Mining, which can be understood as the transformation of raw data into knowledge. This research presents a study to understand the reasons of the low performance of students in technical courses of the EAD applying, to this, the Association Classification (CBA) algorithm in Educational Data Mining (EDM). In order to further improve the results obtained by the CBA, the Association Rules algorithm called Predictive Apriori, not yet employed in related works, was applied in order to generate the best predictive results of Associative Classification. The experimental results point out that the CBA applied to Educational Databases achieves better results than traditional classification algorithms (reaching a mark of 85% accuracy). It was also shown that the use of the forum, quiz and folder tools have a great influence on student performance.

Data de aprova??o ausente. / Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2017-05-11T18:00:35Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) rodrigo_magalhaes_mota_santos.pdf: 605428 bytes, checksum: a0c6c2c74fb7252604442e7b79b71d5d (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2017-05-16T17:13:39Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) rodrigo_magalhaes_mota_santos.pdf: 605428 bytes, checksum: a0c6c2c74fb7252604442e7b79b71d5d (MD5) / Made available in DSpace on 2017-05-16T17:13:39Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) rodrigo_magalhaes_mota_santos.pdf: 605428 bytes, checksum: a0c6c2c74fb7252604442e7b79b71d5d (MD5) Previous issue date: 2016 / A tecnologia, presente cada vez mais no ambiente educacional, tem contribu?do para o aumento da oferta de cursos ? dist?ncia. Grande parte dos cursos ofertados nesta modalidade utilizam os Ambientes Virtuais de Aprendizagem (AVA). Estes ambientes ganham espa?o no cotidiano dos educadores devido ao f?cil manuseio e a grande diversidade de ferramentas disponibilizadas. Tais ferramentas permitem, de forma geral, a administra??o de cursos totalmente ? dist?ncia com oferta de m?ltiplas m?dias e recursos (f?runs de discuss?o, chats, dentre outros) para intera??es entre professores e alunos. Tais intera??es criam enormes volumes de dados que podem ser analisados atrav?s da aplica??o de t?cnicas de Minera??o de Dados Educacionais. Com a aplica??o destas t?cnicas pode-se realizar a previs?o de desempenho acad?mico que pode ter grande utilidade para Institui??es de Ensino no sentido de auxili?-las a tomar, de forma antecipada, decis?es pedag?gicas que possam ajudar os estudantes. Este trabalho apresenta um estudo de m?todos como Sele??o de Atributos utilizando a abordagem Wrapper e Classificador em Cascata, ainda n?o empregados em trabalhos correlatos pesquisados, que visam melhorar os resultados obtidos pelas t?cnicas de Minera??o de Dados Educacionais utilizadas na previs?o de desempenho acad?mico de estudantes. Os resultados experimentais indicam uma melhora no desempenho dos algoritmos classificadores utilizados (alguns alcan?ando a not?vel marca de 90,2% de acur?cia), bem como apontam quais os recursos utilizados no AVA possuem maior influ?ncia no desempenho dos estudantes. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2016. / The technology, which is being increasingly used in the educational environment, has contributed for the popularity of distance courses. Much of the courses offered in this mode uses the so-called Virtual Learning Environments (VLE). These environments are gaining ground in the daily lives of educators due to its easy handling and the wide variety of available tools. These tools allow, in general, the administration of fully distance courses with multiple media and resources (forums, chats, among others) for interactions between teachers and students. These interactions create huge volumes of data that can be analyzed through the application of Educational Data Mining techniques. Such techniques can be used to academic performance prediction that can be very useful for education institutions in order to help them to take, in advance, pedagogical decisions that can help students. This work presents a study of methods as Feature Selection using the Wrapper approach and Classifier Cascade that were not employed in other works, with the aim to improve the results obtained by Educational Data Mining techniques used in the academic performance prediction. Results showed an improvement in the performance of classifiers (some obtaining the remarkable mark of 90.2% in accuracy results), as well as pointed out what the resources used in VLE that have greater influence on student performance.