Abstract
In the rapidly evolving digital landscape, virtual reality (VR) has emerged as a transformative tool in education, particularly in language acquisition. Traditional language learning methods often struggle to provide immersive, interactive, and engaging experiences that foster real-world communication skills. VR environments are increasingly transforming English language acquisition by providing immersive and interactive learning experiences that enhance communication skills development. This paper explores the impact of immersive VR technologies on English language acquisition, focusing on communication skills development through dynamic and interactive learning experiences. A novel Dynamic Monarch Butterfly Optimized Bidirectional Gated Recurrent Unit (DMBO-BiGRU) model is presented to analyze learner interactions within VR environments, assessing communication patterns, language complexity, and learning progression. The dataset, collected from student interactions in VR settings, was preprocessed using noise filtering and normalization to ensure data quality. t-Distributed Stochastic Neighbor Embedding (t-SNE) is employed to extract key features, such as fluency, vocabulary usage, and contextual appropriateness. The DMBO-BiGRU model leverages adaptive learning strategies to provide tailored feedback and optimizes language acquisition based on learners’ communication skills. The effectiveness of the suggested method is demonstrated through experimental results, which indicate good accuracy and positive VR learning scores. The results underscore how immersive, customized, and interactive experiences provided by VR-integrated deep learning models hold the potential to transform language acquisition, showing accuracy of 97.2%, VR score of 97.52%, training gain of 98.49%, detection score of 92.6%, and a retention rate of 96% at iteration 13. This work contributes to the growing field of technology-enhanced education, emphasizing the role of VR and AI-driven methods in enhancing English communication skills.
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