Contributors:
Riley Weagant, Christopher Collins, Taylor Smith, and Michael Lombardo
Post-secondary institutions have a wealth of student data at their disposal. This data has recently been used to explore a problem that has been prevalent in the education domain for decades. Student retention is a complex issue that researchers are attempting to address using machine learning. This research describes our attempt to use academic data from Ontario Tech University to predict the likelihood of a student withdrawing from the university after their upcoming semester. We used academic data collected between 2007 and 2011 to train a random forest model that predicts whether or not a student will drop out. Finally, we used the confidence level of the model’s prediction to represent a student’s “likelihood of success”, which is displayed on a bee swarm plot as part of an application intended for use by academic advisors.
Publications
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R. Weagant, “Supporting Student Success with Machine Learning and Visual Analytics,” Master Thesis, 2019.
@MastersThesis{wea2019a,
author = {Riley Weagant},
title = {Supporting Student Success with Machine Learning and Visual Analytics},
school = {University of Ontario Institute of Technology},
year = 2019
} -
M. Lombardo, R. Weagant, and C. Collins, “Exploratory Data Analysis on Student Retention,” UOIT Student Research Showcase, 2017.
@poster{lom2017,
author = {Michael Lombardo and Riley Weagant and Christopher Collins},
title = {Exploratory Data Analysis on Student Retention},
booktitle = {UOIT Student Research Showcase},
year = 2017
} -
R. Weagant, T. Smith, and C. Collins, “Student Retention: A Data Driven Approach,” UOIT Student Research Showcase, 2015.
@poster{wea2015,
author = {Riley Weagant and Taylor Smith and Christopher Collins},
title = {Student Retention: A Data Driven Approach},
booktitle = {UOIT Student Research Showcase},
year = 2015
}