The Application of a Mobile Learning-Based Interactive Education Platform for the Creation of Animation in University Settings

Abstract

Animation creation, as an interdisciplinary subject that blends art and technology, places high demands on students’ creativity, comprehensive skills, and practical capabilities. Traditional teaching models often fall short in meeting students’ needs for personalized learning and real-time interaction, highlighting the urgent need for innovation through mobile learning platforms. Although some existing studies have attempted to introduce personalized recommendation mechanisms into educational platforms, their application in animation education remains limited, characterized by simplistic models and insufficient exploration of interactive behaviors. In particular, leveraging learners’ implicit mobile interaction relationships for resource recommendation is still in its early stages. This study focuses on the design and implementation of a mobile learning-based interactive education platform tailored for university-level animation creation, aiming to enhance the precision of resource recommendations and the personalization of learning experiences. The research addresses two main aspects: first, it systematically explores the issue of personalized animation learning resource recommendation based on implicit interaction relationships, highlighting their potential value in recommendation mechanisms; second, it proposes a recommendation model integrating coupled graph modeling, attribute representation learning, interaction representation learning, and a prediction layer, offering a technical framework for intelligent recommendation in animation education. The findings are expected to promote deeper integration of mobile learning platforms in university animation education and enhance the effectiveness of personalized teaching.