Predicting Dropout in Higher Education: a Systematic Review

  • Jailma Januário da Silva Universidade de São Paulo
  • Norton Trevisan Roman Universidade de São Paulo

Resumo


In this article, we present a systematic literature review, carried out from February to March 2020, on the application of a machine learning technique to predict student dropout in higher education institutions. Besides describing the protocol followed during our research, which includes the research questions, searched databases and query strings, along with criteria for inclusion and exclusion of articles, we also present our main results, in terms of the attributes used by current research on this theme, along with adopted approaches, specific algorithms, and evalution metrics. The Decision Tree technique is the most used for the construction of models, and accuracy and recall and precision being the most used metric for evaluating models.

Palavras-chave: systematic review, dropout analysis, higher education

Referências

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Publicado
22/11/2021
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SILVA, Jailma Januário da; ROMAN, Norton Trevisan. Predicting Dropout in Higher Education: a Systematic Review. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO, 32. , 2021, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 1107-1117. DOI: https://doi.org/10.5753/sbie.2021.217437.