Machine learning in education

What is it about?

The choice of college majors plays a critical role in determining the future opportunities and earnings of college graduates. Students make their college major decisions due to heterogeneous tastes and abilities, and these individual characteristics determine students’ performance in campus. However, existing methods on major recommendation have several limitations such as incomplete features and characteristics. In this paper we propose a machine-learning based college major recommendation approach, called CMRS, for prospective freshmen to choose desired majors. Our approach compared different state-of-art machine learning models to figure out the best one as our recommendation approach, namely Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (GNB), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), Con- volutional Neural Network (CNN) and Recurrent Neural Network (RNN) as well as collaborative filtering (CF). For this purpose we classified a dataset of more than 2000 college students from different majors in China.

Featured Image

Photo by Kevin Ku on Unsplash

Photo by Kevin Ku on Unsplash

Why is it important?

We compared the mean overall classification accuracies as well as standard deviation. The experimental results demonstrate that Random Forest exhibited superior results to other considered models in terms of overall accuracy and robustness, which produces the optimal college major recommendation accuracy of 97.87% and f-score of 96.60%.