Learning path planning methods based on learning path variability and ant colony optimization

Abstract

With the advancement of education information, the scale of online education has been expanding, which brings challenges to students' learning path planning, i.e., course and learning method planning. To address the limitations of learning path planning such as insufficient personalization, the study proposes a learning path planning method based on learning path variability and ant colony optimization. First, dynamic time regularization is used to obtain learning path variability, and the K-means algorithm is used to classify students' learning types. Subsequently, an ant colony optimization algorithm is used to generate learning paths. Finally, the effectiveness of the method is tested. The results show that the loss value of the ant colony optimization algorithm converges to a minimum value of 0.1, which has the best stability of the loss function curve and the fastest convergence speed compared to other algorithms. Under the same experimental environment, the accuracy of the algorithm is as high as 0.9, which is conducive to the search for the optimal solution. The path planning method designed by the research can effectively grasp the learning characteristics and habits of students, and the accurate classification degree can reach 96.6%. With this learning path planning method, the average video learning time of students reaches a maximum of 80 min, while the average completion rate of students' course objectives is stable at 90%, which is about 20% higher than that of the GA-based learning path planning method. The method can significantly improve academic performance and educational outcomes. The method thus grasps the type of student learning, stimulates students' interest in learning, improves the effect of online learning, helps to promote education informatization and provides a boost to the deep reform of education.