Applying Image and Video Processing in English Education: A Technology-Enhanced Learning Framework

Abstract

Image and video processing in English training is a pioneering technology-enhanced learning strategy which addresses 21st-century student needs. This paradigm’s ability to accommodate today’s multimedia-driven learners’ demands makes it crucial. Such a system requires strong infrastructure, teacher training, scalable computing resources, and adaptive content for diverse learning styles. This research proposes the Smart Multimodal Enhanced Interaction Learning Framework (SMEILF), which takes advantage on multimodal content’s strengths. By making learning more interactive, SMEILF intends to boost students’ engagement, comprehension, and memory. The current research examines SMEILF, a comprehensive system that uses real-time image and video processing for personalised feedback and adaptive learning routes. SMEILF uses interactive language classes, pronunciation training, and contextual video analysis. Simulation analysis demonstrates the framework works and could increase learning, this research contributes to technology-enhanced learning by offering a scalable, adaptive, and student-centered approach to English training. The proposed method increases the learning engagement ratio by 98.5%, pronunciation accuracy ratio by 97.6%, scalability ratio by 99.2%, content accessibility ratio by 92.9%, and teacher and student satisfaction ratio by 95.8% compared to other existing methods. The proposed method increases the learning engagement ratio by 98.5%, pronunciation accuracy ratio by 97.6%, scalability ratio by 99.2%, content accessibility ratio by 92.9%, and teacher and student satisfaction ratio by 95.8% compared to other existing methods.