Predicting University Students' Academic Success and Major using Random Forests

TL;DR

This study develops random forest classifiers to predict university students' graduation and major choices based on early course data, providing accurate predictions and insights for university administration.

Contribution

The paper introduces two novel random forest classifiers for predicting graduation and major selection using early academic data, with variable importance analysis.

Findings

Accurate prediction of graduation using first two semesters' courses.

Effective prediction of students' majors based on initial course registration.

Variable importance analysis reveals key factors influencing student outcomes.

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.