A Systematic Mapping on the Use of Data Mining for the Face-to-Face School Dropout Problem

• Autor(a) principal: Sousa, Leandro Rondado de [UNESP]
• Data de Publicação: 2021
• Outros Autores: Carvalho, Veronica Oliveira de [UNESP], Penteado, Bruno Elias, Affonso, Frank Jose [UNESP], Csapo, B., Uhomoibhi, J.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.5220/0010476300360047; http://hdl.handle.net/11449/218997
• topic: School Dropout; Face-to-Face; Data Mining; Systematic Mapping

Dropout is a critical problem that affects institutions worldwide. Data mining is an analytical solution that has been used to deal with it. Typically, data mining follows a structured process containing the following general steps: data collection, pre-processing, pattern extraction, post-processing (validation). Until know, it is not known how data mining has been used to address the dropout problem in face-to-face education considering all steps of the process. For that, a Systematic Literature Mapping was conducted to identify and analyze the primary studies available in the literature to address some research questions. The aim was to provide an overview of the aspects related to data mining steps in the presented context, without going into details about specific techniques, but about the solutions themselves (for example, imbalanced techniques, instead of SMOTE). 118 papers were selected considering a period of 10 years (01/01/2010 to 31/12/2020).