Impact of Personalized Learning Paths Supported by Mobile Technology on Student Academic Achievement

Abstract

With the rapid advancement of mobile technology, mobile learning has become an integral component of modern education, particularly in the design and implementation of personalized learning paths. These paths enable tailored learning content and strategies based on students’ interests, abilities, and progress, thereby enhancing knowledge acquisition and improving academic achievement. Recent studies on personalized learning paths have primarily focused on content recommendation and learning outcome assessment, whereas limited attention has been given to student interaction relationships. In mobile learning environments, interactions among students and their mutual influence play a crucial role in optimizing personalized learning paths. However, existing research methodologies predominantly rely on theoretical analysis and static data processing, lacking dynamic modeling of complex student interaction patterns. This study aims to address this gap by analyzing student interactions within personalized learning paths and proposing a relationship discovery model based on an extended mobile interaction graph to help further optimize personalized learning paths. The findings are expected to contribute to the refinement of personalized learning path design and provide educators with real-time data support to enhance student academic achievement. By constructing a more precise interaction relationship discovery model, this study offers a novel theoretical framework and practical guidance for advancing personalized education.