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Factors Associated With Persistence in Science and Engineering Majors: An Exploratory Study Using Classification Trees and Random Forests
Author(s) -
Mendez Guillermo,
Buskirk Trent D.,
Lohr Sharon,
Haag Susans
Publication year - 2008
Publication title -
journal of engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.896
H-Index - 108
eISSN - 2168-9830
pISSN - 1069-4730
DOI - 10.1002/j.2168-9830.2008.tb00954.x
Subject(s) - persistence (discontinuity) , logistic regression , mathematics education , random forest , science and engineering , population , graduate students , perspective (graphical) , psychology , computer science , statistics , mathematics , engineering , machine learning , artificial intelligence , demography , pedagogy , sociology , engineering ethics , geotechnical engineering
Many students who start college intending to major in science or engineering do not graduate, or decide to switch to a non‐science major. We used the recently developed statistical method of random forests to obtain a new perspective of variables that are associated with persistence to a science or engineering degree. We describe classification trees and random forests and contrast the results from these methods with results from the more commonly used method of logistic regression. Among the variables available in Arizona State University data, high school and freshman year GPAs have highest importance for predicting persistence; other variables such as number of science and engineering courses taken freshman year are important for subgroups of the student population. The method used in this study could be employed in other settings to identify faculty practices, teaching methods, and other factors that are associated with high persistence to a degree.