Gradient-enhanced evolutionary multi-objective optimization (GEEMOO): Balancing relevance, learning outcomes, and diversity in educational recommendation systems

Abstract

The increasing complexity of educational recommendation systems, driven by the need to balance content relevance, learning outcomes, and diversity, demands advanced optimization solutions that overcome the limitations of traditional methods. As educational technology is exponentially improving, multi-objective optimization plays a vital role in adapting learning experiences to individual requirements. This study tackles the Gradient-Enhanced Evolutionary Multi-objective Optimization (GEEMOO) algorithm, which is considered as a hybrid framework that deals with three conflicting objectives: Relevance, Learning Outcomes, and Diversity. GEEMOO associates gradient-based methods for rapid integration with the correlative power of evolutionary strategies to deliver high-quality Pareto-optimal solutions. Extensive experimentation, using real-world datasets, has shown that GEEMOO consistently exceeded benchmark algorithms performance (NSGA-II and MOPSO) across key metrics, achieving greater Hypervolume, Generational Distance, and diversity indicators. While maintaining robust solution diversity, GEEMOO stands as an ideal solution for large-scale educational recommendation systems efficiency, requiring fewer fitness evaluations. GEEMOO showed better performance than NSGA-II and MOPSO in both convergence (Hypervolume: 0.85, Generational Distance: 0.02) and diversity (Spread Indicator: 0.88, Crowding Distance: 0.92). Although it required a bit more runtime (150 seconds compared to 120 seconds for NSGA-II), GEEMOO achieved this with fewer fitness evaluations (50,000 versus 60,000 for NSGA-II), highlighting its computational efficiency. The algorithm successfully balanced conflicting objectives, providing Pareto-optimal solutions that cater to various educational goals. This work traits GEEMOO’s adaptability and credibility to demonstrate how personalized learning models are adjusted, offering a solid groundwork for improving educational technology in both research and practice.