Leveraging Machine Learning Algorithms for Adaptive Learning Pathways in Undergraduate Mathematics Education: A Case Study

Abstract

With the increasing reliance on technology in education, the need for adaptive learning systems tailored to individual student needs has become paramount. This study employs machine learning algorithms to develop personalized learning pathways for undergraduate mathematics students. Utilizing a dataset comprising over 1,000 student learning interactions, we applied supervised learning techniques, specifically Random Forest and Gradient Boosting, to predict student performance and optimize learning materials. The findings indicate a significant improvement in student engagement and performance, with average assessment scores rising by 15% after implementing the adaptive approaches. These results underscore the necessity of data-driven methodologies in contemporary educational practices, providing insights into the potential for broader applications across various subjects.