Predicting University Students' Academic Success and Choice of Major using Random Forests

Predicting University Students' Academic Success and Choice of Major using Random Forests C edric Beaulac Jeffrey S. Rosenthal August 31,2017 Abstract In this paper, a large data set containing every course taken by every undergraduate student in a major university in Canada over 10 years is analyzed. 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 choice is predicted using once again the first few courses they've registered to. A classification tree is an intuitive and powerful classifier and building a random forest of trees lowers the variance of the classifier and also prevents overfitting. Random forests also allow for reliable variable importance measurements. These measures explain what variables are useful to both of the classifiers and can be used to better understand what is statistically related to the students' choices. The results are two accurate classifiers and a variable importance analysis that provides useful information to the university. Keywords: Higher Education, Students' Success and Choice, Machine Learning, Classification Tree, Random Forest, Variable Importance 1 Introduction As the demand for qualified labour increases it becomes more and more important to understand what motivates students to complete their program and how they select their majors. In parallel, universities are continuously trying to improve their programs and attract more students. It would be useful for a university to be able to predict whether or not a student that begins a program will complete it.