Intelligent Education Based on Mobile Learning: Transitioning from Traditional Classrooms to Adaptive Learning Environments

Abstract

With the rapid development of information technology, mobile learning has become a key means to enhance educational quality and facilitate personalized learning. Traditional classroom teaching models exhibit limitations in terms of personalization, adaptability, and flexibility. Mobile learning, on the other hand, offers the opportunity for learning anytime and anywhere, addressing the individualized needs of students. However, effectively integrating mobile learning with intelligent education technologies to create learning environments that cater to diverse student needs remains a significant challenge in current educational research. In response, an intelligent education framework based on mobile learning was proposed in this study. This framework aims to drive the transition of education from traditional classrooms to adaptive learning environments by integrating heterogeneous network graphs and students’ personalized preferences. The primary focus of this study includes two parts: first, a method for constructing heterogeneous network graphs based on mobile learning, which seeks to enhance the adaptability of learning environments through multi-source data fusion; second, the personalized integration of longand short-term preferences, along with an interest recommendation mechanism, using intelligent algorithms to provide customized learning path recommendations for students. Through these two aspects, the study seeks to offer effective solutions for the transformation of intelligent education, promote the practical application of personalized learning systems, and provide theoretical support and practical guidance for the development of educational technologies.