Multilevel modeling for the analysis and prediction of school dropout: a systematic review

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de Oliveira, Myke Morais ; Barbosa, Ellen Francine ; Shahriar, H ; Teranishi, Y ; Cuzzocrea, A ; Sharmin, M ; Towey, D ; Majumder, AKMJA ; Kashiwazaki, H ; Yang, JJ ; Takemoto, M ; Sakib, N ; Banno, R ; Ahamed, SI

• Tipo de documento: Artigo Científico
• Fonte: 2023 IEEE 47TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC; v. N/A, p. 10-pg., 2023-01-01.

Resumo

This paper presents a systematic review of the use of multilevel models for the analysis and prediction of school dropout. Several studies were carried out in this theme, but there are still challenges to be addressed. There are many different applications of multilevel modeling for school dropouts, which makes it difficult to synthesize the main contributions and advances in the area. The lack of a holistic view makes it difficult to understand the main advances and research gaps. To shed some light on this scenario, this literature review covered the most investigated factors at the student and school levels, such as demographic, socioeconomic, family background, and student's academic performance variables; the main educational environments in which multilevel models were used for the analysis or prediction of school dropout, such as high school/secondary education, and higher education; and the main multilevel models used in these researches, such as the multilevel logistic regression, and the multilevel linear regression. In addition, we also investigated whether the authors used multivariate exploratory techniques or other artificial intelligence techniques to support the fitting and interpretation of the modeling process. (AU)

• Processo FAPESP: 18/26636-2 - Engenharia de Software e Tecnologias Educacionais: Estabelecendo Mecanismos de Apoio ao Ensino e Aprendizagem
• Beneficiário: Ellen Francine Barbosa
• Modalidade de apoio: Auxílio à Pesquisa - Regular