Design of an Adaptive Learning System for Music Education Empowered by Interactive Mobile Technologies

Abstract

With the widespread adoption of mobile devices and the expansion of 5G networks, interactive mobile technologies have broken the temporal and spatial constraints of traditional music education. Learners can now access diverse educational resources and engage in flexible interactions via mobile platforms. However, current recommendation systems for music education primarily rely on basic label matching, which fails to accommodate learners’ varying performance levels, learning paces, and the dynamic demands of mobile contexts. This limitation hinders the progress of digital transformation in music education. Existing approaches suffer from significant drawbacks: collaborative filtering models focus solely on binary learner-resource interactions and overlook the impact of multi-directional interactions; content-based recommendations emphasize musical attributes of resources but ignore real-time mobile interaction features; and graph neural network (GNN) models lack optimization for progressive music skill development and real-time feedback in practice scenarios, resulting in suboptimal alignment between recommendations and learning trajectories. To address these challenges, this study proposes a music education resource recommendation model based on a multi-directional mobile interaction graph attention network (GAN). The main innovations of this research include: (1) integrating multi-directional mobile interaction features with spatiotemporal dynamics to overcome the limitations of single-interaction modeling in traditional systems; (2) combining GAN with mobile interaction-aware modules to dynamically capture interaction weights and adapt to real-time learning contexts; and (3) embedding principles of progressive musical skill development into the learning algorithm to enhance alignment between recommended resources and learners’ advancement. This study provides a technical foundation for adaptive learning systems empowered by interactive mobile technologies, promoting greater precision and personalization in music education resource recommendation.