Supporting student success with machine learning and visual analytics
| dc.contributor.advisor | Collins, Christopher | |
| dc.contributor.author | Weagant, Riley | |
| dc.date.accessioned | 2019-10-28T20:10:38Z | |
| dc.date.accessioned | 2022-03-29T17:27:08Z | |
| dc.date.available | 2019-10-28T20:10:38Z | |
| dc.date.available | 2022-03-29T17:27:08Z | |
| dc.date.issued | 2019-08-01 | |
| dc.degree.discipline | Computer Science | en |
| dc.degree.level | Master of Science (MSc) | en |
| dc.description.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. | en |
| dc.description.sponsorship | University of Ontario Institute of Technology | en |
| dc.identifier.uri | https://hdl.handle.net/10155/1110 | |
| dc.language.iso | en | en |
| dc.subject | Visual analytics | en |
| dc.subject | Machine learning | en |
| dc.subject | Student retention | en |
| dc.subject | Education | en |
| dc.subject | Predictive analytics | en |
| dc.title | Supporting student success with machine learning and visual analytics | en |
| dc.type | Thesis | en |