Open AccessDecision Trees for the Early Identification of University Students at Risk of Desertion
Author(s) -
Mayra Albán,
David Mauricio
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.44.26862
Subject(s) - chaid , dropout (neural networks) , decision tree , desertion , context (archaeology) , identification (biology) , mathematics education , decision tree learning , computer science , intervention (counseling) , psychology , tree (set theory) , machine learning , political science , mathematics , geography , botany , archaeology , psychiatry , biology , law , mathematical analysis
The student's dropout at the universities is a topic that has generated controversy in Higher Education Institutions. It has negative effects which cause problems in the social, academic and economic context of the students. One of the alternatives used to predict the dropout at the universities is the implementation of machine learning techniques such as decision trees, known as prediction models that use logical construction diagrams to characterize the behavior of students and identify early students that at in risk of leaving university. Based on a survey of 3162 students, it was possible to obtain 10 variables that have influence into the dropout, that’s why, a CHAID decision tree model is proposed that presents the 97.95% of the accuracy in the prediction of the university students’ dropout. The proposed prediction model allows the administrators of the universities developing strategies for effective intervention in order to establish actions that allow students finishing their university careers successful.