Research on the application of augmented reality in English vocabulary teaching: A study on improving learning outcomes through interactive experience

Abstract

Augmented reality (AR) is revolutionizing the way we interact with information by blending the physical and digital worlds to create immersive and interactive environments. In the context of English vocabulary learning, AR offers an innovative approach to enhance engagement, comprehension, and retention. This research aims to improve English vocabulary teaching through AR-based applications, addressing challenges such as pedagogical depth, diverse learning styles, and the integration of real-world contexts. The study focuses on the design and deployment of an AR-based learning application that incorporates gamified elements, interactive visuals, and real-time feedback to facilitate students’ retention and understanding of vocabulary. Data collection methods include tracking user interactions, gathering feedback responses, and assessing performance on vocabulary learning activities. The collected data underwent preprocessing, which involved data cleaning and normalization. Principal component analysis (PCA) was employed to extract irrelevant features from the processed data. The improved weighted hybrid deep feedforward neural network (IWH-DFNN) was utilized to predict student performance and enhance learning outcomes from these interactive experiences. The improved weights hybrid (IWH) approach was applied to optimize the hyperparameters of the deep feedforward neural network (DFNN), thereby increasing the model’s predictive accuracy regarding student performance. The proposed IWH-DFNN model demonstrated superior performance in improving learning outcomes and enhancing the interactive experience, achieving high recall (92.70%), precision (95%), accuracy (97%), F1-score (89%), and minimal accuracy loss (0.03). The findings suggest that AR-based learning environments have the potential to enhance English vocabulary outcomes by integrating machine learning algorithms for adaptive learning within AR settings. This integration creates a more engaging, customized, and efficient learning environment.