Predictive models for higher education dropout: A systematic literature review

Authors

  • Marcelo Ferreira Tete Universidade Federal de Goiás (UFG)
  • Marcos de Moraes Sousa Universidade Federal de Goiás (PPGADM – UFG)
  • Talia Santos de Santana Goiano Federal Institute (IF Goiano) – Campus Ceres
  • Salatyel Fellipe Silva Federal University of Goiás (UFG) https://orcid.org/0000-0003-2180-9258

DOI:

https://doi.org/10.14507/epaa.30.6845

Keywords:

higher education dropout, state of the art, predictive models, university management

Abstract

School dropout is considered a complex problem and one that cuts across several levels of analysis. The development of predictive models has been a more dynamic and proactive response to tackle this problem. This research offers a systematic literature review on dropout prediction in higher education. The analysis period was from 2010 to 2020, searching scientific studies in six databases and working with a sample of 48 studies. The results indicate methodological and contextual characteristics of the cutting-edge literature on dropout prediction and enable the proposition of a research agenda for future studies. The analysis revealed an absence of research reporting or proposing management actions and educational policies that go beyond applying dropout predictive models.

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Author Biographies

Marcelo Ferreira Tete, Universidade Federal de Goiás (UFG)

Marcelo Ferreira Tete has a PhD in administration from the University of Brasília (UnB). He is professor of administration at the School of Administration, Accounting, and Economics (FACE) of the Federal University of Goiás (UFG). Professor Tete’s research topics are sociotechnical transitions and innovation systems in areas such as higher education, renewable energy, urban mobility and smart cities. Member of the Sustainability Transitions Research Network (STRN) and founding member of the research network Sustainability Transitions Brazil (STB).

Marcos de Moraes Sousa, Universidade Federal de Goiás (PPGADM – UFG)

Marcos de Moraes Sousa has a PhD in administration from the University of Brasília (UnB) and is a professor at the Graduate Program in Administration of the Federal University of Goiás (PPGADM – UFG). He is also professor at the Graduate Program in Professional and Technological Education of the Federal Institute of Goiás (ProfEPT – IF Goiano). Professor Sousa’s research topics are justice administration, public administration, student retention and dropout, and public sector innovation.

Talia Santos de Santana, Goiano Federal Institute (IF Goiano) – Campus Ceres

Thalia Santos de Santana is professor of computing at the Goiano Federal Institute – Campus Ceres (IF Goiano). She has bachelor’s degree in information systems and is a master’s student at the Graduate Program in Computer Science (PPGCC) of the Institute of Informatics at the University of Goiás (INF – UFG).

Salatyel Fellipe Silva, Federal University of Goiás (UFG)

Salatyel Fellipe has a specialist degree in higher education teaching from the Brazilian School of Education and Culture (FABEC) and has a specialist degree in administration of managerial processes from the Lapa Educational Faculty (FAEL). He has a bachelor’s degree in computer engineering from the Federal University of Goiás (UFG), is member of the Public Management and Sustainability research network of the UFG and is member of the research network Sustainability Transitions Brazil (STB).

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Published

2022-10-04

How to Cite

Tete, M. F., Sousa, M. de M., de Santana, T. S., & Silva, S. F. (2022). Predictive models for higher education dropout: A systematic literature review. Education Policy Analysis Archives, 30, (149). https://doi.org/10.14507/epaa.30.6845

Issue

Vol. 30 (2022)

Section

Articles

License

Copyright (c) 2022 Marcelo Ferreira Tete, Marcos de Moraes Sousa Sousa, Thalia Santos Santana, Salatyel Fellipe Silva

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.