Exploring Strategies to Enhance Students' Information Literacy in Open Education through Mobile Learning Technologies

Abstract

The lack of information literacy among learners in open education has become a core bottleneck hindering global access to high-quality education. Existing mobile learning interventions suffer from unclear objectives and fragmented processes, with disputes over the fragmented value of mobile learning remaining unaddressed. Traditional learning analytics frameworks are also inadequate to meet the real-time and contextual needs of ubiquitous mobile learning environments. This study aims to develop a mobile-specific information literacy enhancement framework, ML-ILMDF, based on a contextualized literacy development model. The framework specifies its technical implementation details and establishes dynamic coupling strategies for context and literacy to provide precise interventions. The study systematically validates the educational effectiveness, core mechanism rationality, and engineering feasibility of the framework. A quasi-experimental study with 320 global open university learners, lasting 16 weeks, integrates multi-source mobile data collection, five-dimensional information literacy assessments, and sub-studies using dynamic hypergraphs and collaborative filtering recommendation algorithms for comparison. Simultaneously, the technical performance evaluation of the framework is conducted. The innovation of this study lies in proposing a contextualized literacy development model that achieves dynamic coupling between micro-contexts and macro-literacy, clarifying the mobile-specific technical architecture and implementation details of ML-ILMDF to overcome the limitations of traditional frameworks. It empirically addresses the fragmented nature of mobile learning and offers a practical, actionable paradigm for cultivating information literacy in open education through mobile learning technologies.