Tendencias en modelos informativos sobre la retención – deserción universitaria

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1 Cita (Scopus)


The generation of data in educational environments must be used to extract relevant and timely information that improves the quality and productivity of academic institutions. This research aims to analyze current studies on student retention and / or dropout in order to determine the variables that affect them and trends in data analysis models. The guidelines of Roy Hubara and Sturm (2019) were followed to review articles from four databases, classifying them according to the approach used. The results obtained indicate that the variables that affect student retention are related to cognitive, social and organizational factors and that the tendency is to develop predictive-prescriptive models for the study of these concepts. Finally, it is proposed to develop predictive models based on statistics and learning models to improve student retention and dropout rates.

Título traducido de la contribuciónTrends in information models on retention-university dropout
Idioma originalEspañol
Páginas (desde-hasta)55-68
Número de páginas14
PublicaciónRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
EstadoPublicada - feb. 2020

Nota bibliográfica

Publisher Copyright:
© 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Palabras clave

  • Artificial intelligence
  • Data analytics
  • Student dropout
  • Student retention


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