Adaptive Recommendation of Teaching Content in Higher Education Using Mobile Interaction Technology

Abstract

With the rapid advancement of mobile interaction technology, teaching methodologies in higher education are increasingly moving toward personalization and intelligence. The use of mobile interaction technology for adaptive recommendation of teaching content has become a critical topic for enhancing educational effectiveness. Existing research in content recommendation, primarily based on collaborative filtering algorithms, often relies on single-dimensional data applications and lacks comprehensive consideration of both location information and temporal effects. Consequently, these approaches fall short in addressing the complex requirements of dynamic learning environments. This study proposes a multi-dimensional dynamic adaptive recommendation system for teaching content based on mobile interaction technology to address the limitations of existing methods. The research encompasses location-based collaborative filtering for teaching content, time-effect-based collaborative filtering, and an integrated multi-dimensional dynamic recommendation model that considers both location and temporal factors. This study is expected to provide a more precise and dynamically adaptive solution for personalized teaching in higher education.