Big Data Analysis and Machine Learning for Predicting Individual Differences and Learning Paths in Education

This study explores the application of big data analytics and machine learning in identifying individual learner differences and predicting learning paths within the educational sector. As education becomes increasingly digitized, personalized learning has emerged as a key strategy for enhancing educational quality. The rapid advancement of big data analytics and machine learning offers new tools and methods that provide precise identification of individual learner differences and predict learning trajectories. Employing a literature review and theoretical analysis, this study systematically examines the current status of these technologies in education. It focuses on their theoretical foundations and mechanisms for identifying learner differences and predicting learning paths. Through an in-depth analysis of relevant literature, the study summarizes the principal application scenarios and technical methods of these technologies in education and discusses their advantages and challenges in personalized learning. Findings indicate that big data analytics and machine learning can process and analyze massive volumes of educational data to precisely identify learners' styles, interests, and capability differences, thereby providing a scientific basis for personalized teaching. Moreover, these technologies can predict future learning trajectories by analyzing historical learning data, aiding educators in developing more effective teaching strategies. In conclusion, while big data analytics and machine learning hold great potential in education, they also face challenges related to data privacy and technical complexity, necessitating further research and practical exploration.