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

With the rapid advancement of information technology and the widespread adoption of mobile devices, a paradigm shift has been observed in education—from traditional classroom- based instruction to more flexible and personalized mobile learning environments. Mobile learning has not only eliminated spatial and temporal constraints but has also enabled new application scenarios for intelligent education. However, existing learning content recommendation systems exhibit notable limitations in addressing the dynamically evolving nature of learning environments and individualized learning needs. A predominant focus on students’ static characteristics and historical learning behaviors has resulted in the neglect of the dynamic changes in the learning environment and students’ time management. Consequently, optimizing the timing of content delivery to accommodate individual learning requirements and contextual variability has emerged as a critical research challenge. To address this issue, a learning content recommendation model tailored for mobile learning environments was proposed in this study. The model comprises three main components: an encoding layer, a Transformer layer, and a prediction layer. The encoding layer combines the graph structure of the mobile learning network with the learning content recommendation problem by encoding student and interactive learning content nodes and corresponding time information. The Transformer layer adjusts the temporal influence of each node and aggregates the embeddings of both nodes and time. The prediction layer leverages the output embeddings from the Transformer layer—infused with temporal features—to perform learning content delivery prediction. Through the construction and optimization of this model, the objective is to enhance the precision and efficiency of content delivery, thereby improving educational quality and learning outcomes in mobile learning environments.