Enhancing the effectiveness of English grammar teaching through biomechanical feedback and deep learning algorithms
Abstract
This study investigates the integration of biomechanical feedback—targeting posture, gestures, and articulation mechanics—with a Convolutional Neural Network (CNN) to improve the effectiveness of English grammar instruction. Traditional teaching methods frequently overlook the physical aspects of speech production, which are critical for both written and spoken language proficiency. In this study, 94 participants from China were divided into an Experimental Group (EG) receiving biomechanical feedback and a Control Group (CG) receiving traditional instruction. Key findings show that the EG demonstrated significant improvements in grammar accuracy (16.2%), sentence fluency (12.1%), and error reduction (12.3%) compared to the CG, with statistically significant differences (p < 0.05). The EG reported high satisfaction with the learning process, with 88.3% providing positive feedback on the overall experience. The CNN was instrumental in analyzing linguistic and biomechanical data, enabling personalized feedback that improved participant’ speech clarity, pronunciation accuracy, and grammar retention. These results highlight the potential of integrating physical movement with AI-driven feedback to enhance grammar learning outcomes, offering a more comprehensive and engaging approach to language instruction.
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