An intelligent academic recommendation system for learning support in higher education

Abstract

Higher education institutions increasingly rely on data-driven approaches to improve student learning outcomes. However, many academic advisory systems still provide general recommendations without considering individual learning patterns and academic performance. This study proposes an intelligent academic recommendation system that utilizes machine learning techniques to support personalized learning in higher education. The proposed system analyzes student academic data including grade point average, attendance, assignment scores, and study habits to predict academic performance. The proposed approach was evaluated using a dataset consisting of 1000 simulated student records representing academic performance indicators in higher education. Based on prediction results, the system generates personalized learning recommendations to assist students in improving their academic outcomes. Several machine learning algorithms, including Decision Tree, Random Forest, and Support Vector Machine, were evaluated to determine the most suitable predictive model. Experimental results show that the Random Forest algorithm achieved the highest prediction accuracy compared with other models. The developed system provides proactive learning recommendations that can assist both students and academic advisors in making better academic decisions.