Intelligent Recommender Systems in Mobile-assisted Language Learning (MALL): A Study of BERT-based Vocabulary Learning

With the popularity and development of mobile technology, mobile-assisted language learning (MALL) has gradually become essential to language learning. Intelligent recommender systems are increasingly important in MALL to improve learning efficiency and personalized experience. This paper explores how BERT (Bidirectional Encoder Representations from Transformers), a model with significant performance in natural language processing, can provide intelligent recommendations for vocabulary learning in MALL. First, it briefly introduces the basics of BERT and how it works. Then, it delves into how BERT can be utilized for data pre-processing, feature extraction, and model training in a MALL environment. In addition, this paper describes how to design an intelligent recommender system based on the user's learning history and preferences and proposes some ideas for integration with other technologies. This study suggests a new methodological framework by applying BERT technology in intelligent recommender systems within the MALL environment. Our research demonstrates how the deep learning capabilities of BERT can potentially enhance the personalized learning experience and efficiency of MALL systems. It provides a new perspective and theoretical foundation for applying deep learning technology in language learning, although further empirical research is required to validate these theoretical assumptions. Finally, an outlook on the future of BERT application in MALL and some suggestions are introduced. This paper aims to provide a reference framework for researchers and educators to understand and apply BERT in the field of MALL deeply.