Open AccessDropout and transfer paths: What are the risky profiles when analyzing university persistence with machine learning techniques?
Author(s) -
Luis J. Rodrı́guez-Muñiz,
Ana Bernardo,
María Esteban,
Irene Dı́az
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0218796
Subject(s) - interpretability , dropout (neural networks) , machine learning , perspective (graphical) , computer science , persistence (discontinuity) , artificial intelligence , vulnerability (computing) , data science , cognitive psychology , psychology , engineering , geotechnical engineering , computer security
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.