JoWUA

Abstract

Ubiquitous Language Learning (ULL) employs modern computing technologies to deliver language instruction adaptively and context-sensitively across different settings. This paper looks at heuristic solutions for enabling ULL systems, specifically the integration of learning systems and educational pedagogies to unsupervised, customized, and self-paced learning pathways. We study several algorithms that enable content recommendation, feedback, and learner profiling, including supervised and unsupervised machine learning, reinforcement learning, and deep learning models. GEM-EDU Model are guided by some educational theories, including constructionism, situated learning, and cognitive scaffolding, so that the algorithmic GEM-EDU frameworks address the relevant language acquisition mechanics—cognitive, emotional, or psychomotor. Furthermore, we propose a conceptual GEM-EDU framework that defines algorithmic components along the primary attributes of ULL systems: adaptability, context-awareness, user interaction, interaction, and information privacy. Initial deployment reviews and case studies are conducted to document best practices and current gaps in the literature on algorithmic implementation of ULL. With this study, we lay the groundwork for continued development of ULL systems that are intelligent, scalable, and ethically designed, which will enable mobile and digital societies to foster technologies supporting lifelong language learning.