Supporting student success with machine learning and visual analytics
Date
2019-08-01
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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.
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Keywords
Visual analytics, Machine learning, Student retention, Education, Predictive analytics