Personalizing Learning Paths: A Study of Adaptive Learning Algorithms and Their Effects on Student Outcomes

Abstract

Background. In recent years, the demand for personalized learning experiences has increased, particularly in the context of adaptive learning algorithms that cater to individual student needs. However, there is still a lack of comprehensive studies that evaluate the effectiveness of these algorithms on student outcomes. This study seeks to address this gap by examining how adaptive learning algorithms can personalize learning paths and improve academic performance. Purpose. The research aims to explore the correlation between the implementation of these algorithms and student engagement, retention, and success rates. Method. To achieve this, a quantitative research method was employed, involving the collection of data from 200 students in an online learning environment. The students were divided into two groups: one using a traditional learning model and the other exposed to adaptive learning algorithms. Student outcomes, including engagement metrics, test scores, and retention rates, were tracked over a semester. Results. The results revealed a significant improvement in student engagement and academic performance in the group that utilized adaptive learning algorithms compared to the traditional learning group. Moreover, students in the adaptive learning group demonstrated higher retention rates and greater satisfaction with their learning experiences. Conclusion. In conclusion, the study suggests that adaptive learning algorithms play a crucial role in enhancing personalized learning paths, ultimately leading to improved student outcomes. These findings highlight the importance of integrating adaptive technologies in modern educational systems to foster more effective learning environments.