{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1110"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1110","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Supporting student success with machine learning and visual 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.","abstract_html":"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. 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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."]},{"key":"dc:title","label":"Title","values":["Supporting student success with machine learning and visual analytics"]}]}],"canonical_facts":{"dc:contributor.advisor":["Collins, Christopher"],"dc:creator":["Weagant, Riley"],"dc:date.accessioned":["2019-10-28T20:10:38Z","2022-03-29T17:27:08Z"],"dc:date.available":["2019-10-28T20:10:38Z","2022-03-29T17:27:08Z"],"dc:date.issued":["2019-08-01"],"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. 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