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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.  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2227-7390 ISBN Medium  
  Area Expedition Conference  
  Notes Approved  
  Call Number UAI @ alexi.delcanto @ Serial (up) 1463  
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