ALPOA: Adaptive Learning Path Optimization Algorithm for Personalized E-Learning Experiences

In this study, we propose the Adaptive Learning Path Optimization Algorithm (ALPOA) to enhance personalized e-learning experiences by tailoring content delivery based on individual learner profiles. ALPOA employs a hybrid optimization framework combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to dynamically adjust learning paths. The algorithm considers multiple factors such as learner proficiency, learning speed, engagement level, and content difficulty. Experimental results demonstrate that ALPOA outperforms traditional static e-learning models, achieving a 25% improvement in learning efficiency, a 30% increase in learner engagement, and a 20% reduction in content redundancy. The model was tested on a dataset of 1,500 learners, showing a 97% accuracy in predicting optimal learning paths and a 15% higher knowledge retention rate compared to benchmark algorithms. ALPOA’s scalability and adaptability make it a promising solution for personalized education systems, fostering improved learning outcomes and satisfaction. Future work will focus on integrating real-time feedback mechanisms and expanding the algorithm to support diverse learning environments.