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.
Blog Post
Guiding University Success: How AI and Smart Visuals Are Transforming Student Advising
Publications
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Weagant, R. (2019). Supporting Student Success with Machine Learning and Visual Analytics [Master’s thesis, Ontario Tech University].
@MastersThesis{wea2019a, author = {Riley Weagant}, title = {Supporting Student Success with Machine Learning and Visual Analytics}, school = {University of Ontario Institute of Technology}, year = 2019 } -
Lombardo, M., Weagant, R., & Collins, C. (2017). Exploratory data analysis on student retention [Poster]. UOIT Student Research Showcase.
@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 } -
Weagant, R., Smith, T., & Collins, C. (2015). Student retention: A data driven approach [Poster]. UOIT Student Research Showcase.
@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 }