Please use this identifier to cite or link to this item:
http://hdl.handle.net/10155/1110
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Issue Date: | 8 |
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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