Development of Dynamic Personalized Learning Paths Based on Knowledge Preferences and the Ant Colony Algorithm

The importance of personalized learning paths is acknowledged as an effective solution for students with diverse learning characteristics. However, implementing learning paths in real classrooms presents challenges, particularly in assigning teachers to create a unique learning path for each student, considering their diverse preferences and the difficulty levels of learning objects, which will be time-consuming. To overcome manual learning path design, we propose an ant colony to create automatic, personalized learning paths based on user preferences and the difficulty level of the learning objects. Furthermore, we have implemented a system capable of generating dynamic personalized learning paths based on knowledge preferences to address issues related to the over- and under-prediction of student ability. Additionally, the system has the ability to automatically classify learning objects according to their difficulty levels, guaranteeing regular updates. We compared user experience perception and the impact of personalized learning paths with conventional e-learning to evaluate the effectiveness of the proposed method. The experiments showed that integrating personalized learning paths resulted in an impressive 71% increase in student performance. In comparison, conventional e-learning only improved student performance by 54%. These findings suggest that the proposed system presents promising opportunities for enhanced learning outcomes.