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Author Opazo, D.; Moreno, S.; Alvarez-Miranda, E.; Pereira, J. doi  openurl
  Title Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities Type
  Year 2021 Publication Mathematics Abbreviated Journal Mathematics  
  Volume 20 Issue 9 Pages 2599  
  Keywords machine learning; first-year student dropout; universities  
  Abstract Student dropout, defined as the abandonment of a high education program before obtaining the degree without reincorporation, is a problem that affects every higher education institution in the world. This study uses machine learning models over two Chilean universities to predict first-year engineering student dropout over enrolled students, and to analyze the variables that affect the probability of dropout. The results show that instead of combining the datasets into a single dataset, it is better to apply a model per university. Moreover, among the eight machine learning models tested over the datasets, gradient-boosting decision trees reports the best model. Further analyses of the interpretative models show that a higher score in almost any entrance university test decreases the probability of dropout, the most important variable being the mathematical test. One exception is the language test, where a higher score increases the probability of dropout.  
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  ISSN 2227-7390 ISBN Medium  
  Area Expedition Conference  
  Notes Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1463  
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