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Detalhes da Produção
| Tipo | Artigo Publicado |
| Grupo | Produção Bibliográfica |
| Descrição | BALANIUK, R. ; do Prado, Hércules Antonio ; Guadagnin, R. ; FERNEDA, Edilson ; COBBE, P.. Predicting evasion candidates in higher education institutions. Lecture Notes in Computer Science, v. 6918, p. 143-151, 2011. |
| Autor | Hercules Antonio do Prado |
| Ano | 2011 |
Informações Complementares
| Ano do artigo | 2011 |
| Descricão e Informacões Adicionais | Abstract: Since the nineties, Data Mining (DM) has shown to be a privileged partner in business by providing the organizations a rich set of tools to extract novel and useful knowledge from databases. In this paper, a DM application in the highly competitive market of educational services is presented. The course abandonment problem by students represents a serious risk for the education service providers. Identifying students who intend to transfer to a competing school, or to drop out altogether, before they initiate the transfer process or simply stop attending classes has been a very difficult proposition for colleges or universities. Prior research indicates that student retention plays an important role in achieving advantage over competitors, since it translates into a solid financial base onto which to build the educational services business. Additionally, an increased graduation rate contributes to fulfilling the education service provider s social charter, which is to better prepare a new generation of citizens. This paper presents a successful data mining application for evasionrisk prediction in a higher education institution in the Federal District of Brazil. A model was built by combining a set of classifiers into a committee machine to predict the likelihood that a student who completed his/her second term will remain in the institution until graduation. The model has shown to be predictive for evasion in a high accuracy level and has been useful for managing the students portfolio. |
| Divulgacão Científica | NAO |
| Idioma | Inglês |
| ISSN | 03029743 |
| Meio de Divulgação | NAO_INFORMADO |
| Natureza | COMPLETO |
| Página Final | 151 |
| Página Inicial | 143 |
| Relevância | NAO |
| Título do Artigo | Predicting evasion candidates in higher education institutions |
| Título do Períodico ou Revista | Lecture Notes in Computer Science |
| Volume | 6918 |