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Please use this identifier to cite or link to this item: http://hdl.handle.net/10155/1110

Issue Date: 1-Aug-2019
Title: Supporting student success with machine learning and visual analytics
Authors: Weagant, Riley
Publisher : University of Ontario Institute of Technology
Degree : Master of Science (MSc)
Department : Computer Science
Supervisor : Collins, Christopher
Keywords: Visual analytics
Machine learning
Student retention
Education
Predictive analytics
Abstract: 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 thesis 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 dropout. Finally, we used the confidence level of the model’s prediction to represent a students “likelihood of success”, which is displayed on a beeswarm plot as part of an application intended for use by academic advisors.
Appears in Collections:Electronic Theses and Dissertations (Public)
Faculty of Science - Master Theses

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