Enhancing language acquisition through personalized learning: The role of collaborative filtering and recommender systems in TESOL

Abstract

This paper explores the transformative potential of Collaborative Filtering (CF) and Recommender Systems (RS) in Teaching English to Speakers of Other Languages (TESOL). By leveraging data-driven insights from learner interactions, these technologies offer personalized learning experiences that significantly enhance language acquisition, engagement, and retention. Through empirical evidence and quantitative analyses, we demonstrate the positive impact of CF and RS on learners' proficiency, vocabulary acquisition, and communicative competence. The integration of CF and RS into TESOL not only facilitates adaptive learning pathways but also addresses practical implementation challenges, including privacy, ethical concerns, and technological barriers. This study underscores the efficacy of personalized learning recommendations in creating more engaging, efficient, and effective language learning environments.