Integrated circuit learning path planning and personalized recommendation system based on genetic algorithm optimization

Abstract

In the digital age, integrated circuit learning path planning and personalized recommendation are crucial to improving learning outcomes. Traditional education models are difficult to meet the personalized needs of learners. This study constructs an integrated circuit learning path planning and personalized recommendation system based on genetic algorithm optimization. Learning path planning is abstracted as a combinatorial optimization problem, and an objective function with constraints such as time and mastery is constructed, and a dynamic weight adjustment strategy is introduced. The genetic algorithm is improved in many aspects such as population initialization, crossover strategy, and mutation mechanism to achieve collaborative work among various modules of the system. The experiment is based on real data from 5000 users of an online learning platform. The results show that the average path score of the improved genetic algorithm group is about eight points higher than that of the traditional genetic algorithm group, and the mastery improvement is 0.08 higher than that of the traditional group, and the user feedback satisfaction is higher. The system significantly improves the efficiency and personalization of learning path planning, providing a new solution for the field of integrated circuit education and personalized recommendation.