ENHANCING SMART EDUCATION: COMPARATIVE ANALYSIS OF HYBRID RECOMMENDATION ALGORITHMS FOR PERSONALIZED LEARNING

Smart education systems have emerged as pivotal tools in modern education, yet the effectiveness of different recommendation algorithms in personalizing learning experiences remains understudied, particularly in higher education contexts. This study evaluates and compares three recommendation algorithms collaborative filtering, content-based filtering, and a hybrid approach within a web-based smart education system designed for computer science education, focusing on their ability to enhance personalized learning experiences. The author developed a web-based smart education system incorporating these three algorithms and tested it using a dataset of 100 users across 20 courses. The system's performance was evaluated using precision, recall, accuracy, and F1-score metrics. A four-week case study with 20 users was conducted to assess practical implementation outcomes. The hybrid algorithm demonstrated superior performance with 85.96% precision, 98.99% recall, 94.33% accuracy, and a 91.98% F1-score, significantly outperforming both collaborative filtering (precision: 68.09%, recall: 94.12%) and content-based filtering (precision: 72.73%, recall: 90.32%). Case study results showed consistent improvement in user performance, with score improvements ranging from 15% to 23%.The hybrid algorithm proves most effective for personalizing educational content delivery, though with higher computational overhead. These findings suggest that hybrid recommendation approaches can significantly enhance smart education systems' ability to provide personalized learning experiences, despite computational challenges in large-scale deployments.