Enhanced Personalized Learning in E–Learning: Adaptive Optimization Algorithm for Tailored Pathways

This study introduces an innovative Adaptive Learning Path Optimization Algorithm (ALPOA) designed to enhance personalized learning in e-learning environments. The algorithm employs a combination of machine learning techniques and rule-based systems to dynamically adjust and customize learning pathways based on individual student performance, preferences, and behavior. By analyzing real-time data, the algorithm tailors content delivery, recommends relevant resources, and aligns learning activities with evolving knowledge levels. Experimental results reveal notable improvements, including a 15% average increase in test scores, a 25% rise in learner engagement, and a 25% reduction in dropout rates. These outcomes underscore the effectiveness of ALPOA in creating adaptive, efficient, and engaging learning experiences. The study further explores the scalability of the algorithm, demonstrating its applicability across diverse educational contexts such as K-12, higher education, and corporate training. By leveraging real-time analytics and predictive modeling, ALPOA provides a robust framework for addressing the challenges of individualizing education at scale. The proposed system not only optimizes learning outcomes but also promotes user satisfaction by fostering an engaging and personalized learning journey. The findings highlight the transformative potential of adaptive algorithms in e-learning, paving the way for a more inclusive and effective digital education ecosystem. Future research aims to refine ALPOA’s architecture, test its efficacy in various real-world settings, and address challenges related to data security and scalability.