Predicting University Students’ Academic Success and Major Using Random Forests

Cédric Beaulac () and Jeffrey S. Rosenthal
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Cédric Beaulac: University of Toronto
Jeffrey S. Rosenthal: University of Toronto

Research in Higher Education, 2019, vol. 60, issue 7, No 6, 1048-1064

Abstract: Abstract In this article, a large data set containing every course taken by every undergraduate student in a major university in Canada over 10 years is analysed. Modern machine learning algorithms can use large data sets to build useful tools for the data provider, in this case, the university. In this article, two classifiers are constructed using random forests. To begin, the first two semesters of courses completed by a student are used to predict if they will obtain an undergraduate degree. Secondly, for the students that completed a program, their major is predicted using once again the first few courses they have registered to. A classification tree is an intuitive and powerful classifier and building a random forest of trees improves this classifier. Random forests also allow for reliable variable importance measurements. These measures explain what variables are useful to the classifiers and can be used to better understand what is statistically related to the students’ situation. The results are two accurate classifiers and a variable importance analysis that provides useful information to university administrations.

Keywords: Higher education; Student retention; Academic success; Machine learning; Classification tree; Random forest; Variable importance (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11162-019-09546-y

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