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Dropout and transfer paths: What are the risky profiles when analyzing university persistence with machine learning techniques?

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  • Luis J Rodríguez-Muñiz
  • Ana B Bernardo
  • María Esteban
  • Irene Díaz

Abstract

University dropout is a growing problem with considerable academic, social and economic consequences. Conclusions and limitations of previous studies highlight the difficulty of analyzing the phenomenon from a broad perspective and with bigger data sets. This paper proposes a new, machine-learning based method, able to examine the problem using a holistic approach. Advantages of this method include the lack of strong distribution hypothesis, the capacity for handling bigger data sets and the interpretability of the results. Results are consistent with previous research, showing the influence of personal and contextual variables and the importance of academic performance in the first year, but other factors are also highlighted with this model, such as the importance of dedication (part or full time), and the vulnerability of the students with respect to their age. Additionally, a comprehensive graphic output is included to make it easier to interpret the discovered rules.

Suggested Citation

  • Luis J Rodríguez-Muñiz & Ana B Bernardo & María Esteban & Irene Díaz, 2019. "Dropout and transfer paths: What are the risky profiles when analyzing university persistence with machine learning techniques?," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-20, June.
  • Handle: RePEc:plo:pone00:0218796
    DOI: 10.1371/journal.pone.0218796
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    References listed on IDEAS

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    1. Daniel Cohen & Marcelo Soto, 2007. "Growth and human capital: good data, good results," Journal of Economic Growth, Springer, vol. 12(1), pages 51-76, March.
    2. Matt Bergman, 2016. "From Stopout to Scholar: Pathways to Graduation through Adult Degree Completion Programs," International Journal of Information Communication Technologies and Human Development (IJICTHD), IGI Global, vol. 8(4), pages 1-12, October.
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