A Hybrid Machine Learning Model for Personalized Elective Course Recommendations in Higher Education

Abstract

Academic recommendation systems have emerged as key tools for optimizing course selection in higher education, allowing personalized curriculum planning based on each student's profile. This study presents one of the first integrations of Principal Component Analysis (PCA) and clustering techniques with deep neural networks for elective course recommendations in the context of Peruvian higher education. The model was developed using academic records from 120 students at the Universidad Nacional de San Martín, applying PCA for dimensionality reduction and K-Means clustering, with the elbow method identifying five optimal clusters. A regression-based neural network was then used to predict the course selection likelihood. The evaluation metrics showed an MSE of 2.9632, RMSE of 1.7214, and R2 of 0.8518, confirming adequate generalization and model accuracy. The findings highlight the feasibility of predictive models in academic decision making and suggest further improvements through the inclusion of diverse data sources. However, the current system is limited to academic history data, excluding variables such as personal interests, learning styles, and career goals, which could enhance the precision of recommendations in future implementations.