Application of machine learning in online education: Personalized learning path planning and optimization

With the rapid growth of online education, an increasing number of people are turning to digital platforms for learning. However, due to the relatively recent development of online education, the quality of available resources varies significantly. To address this issue, this study explores the application of machine learning in online education, focusing specifically on how to design and optimize personalized learning paths. The goal is to enhance learning effectiveness by tailoring educational resources to individual needs. In this approach, student learning data is collected and analyzed using the K-means clustering algorithm to categorize students into distinct learning groups. Based on each group’s learning objectives and focus areas, customized resources are recommended to provide a more targeted and optimized learning experience. Through user collaborative filtering, users were asked to rate the recommended courses and improve resource recommendation. Then, based on the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm, the content features of learning resources were analyzed and targeted learning path recommendations were made. Finally, through deep Q-learning network (DQN), personalized learning path planning was further strengthened and improved, achieving functions such as personalized learning path recommendation, intelligent tutoring system, dynamic difficulty adjustment, and optimized resource allocation. By combining machine learning methods to optimize the learning path of online education, the learning efficiency of students was significantly improved; their learning experience was optimized; the coherence of learning was greatly improved. The satisfaction of students with the online education platform improved by using machine learning under high-frequency usage ranged from 4.6 to 4.9, which was considered very satisfactory. After learning, the learning performance score was as high as 4.7 to 4.9, which was considered excellent. Machine learning has enormous application value in personalized learning path planning and optimization.