Predicting Personalized Student Performance in Computing-Related Majors via Collaborative Filtering

The last decade has seen a significant enrollment surge in various computing-related majors. However, choosing the right computing major is vital to students' academic and career success. Personalized prediction of performance in closely related computing majors will help individual students better find the right major for them. This paper proposes a method of predicting student performance in computing majors. Our method is based on collaborative filtering using enhanced similarity and yields personalized predictions of student grades in courses required for each computing major. Prediction accuracy is enhanced by analyzing computing major-specific course characteristics, such as core courses, course prerequisites, and course levels.