A systematic study on the application of computer multimodal discourse analysis in English educational assessment

This paper explores the innovative application of computer multimodal discourse analysis technology in English education assessment, aiming to provide a scientific basis and technical support for accurate evaluation of English teaching effectiveness. Multimodal discourse analysis integrates text, speech, image, and other channels, which is still in its exploratory phase in educational evaluation. The study reviews multimodal learning theory and computer-assisted language learning (CALL), highlighting potential and challenges. It introduces a multimodal analysis system framework incorporating natural language processing (NLP), speech recognition (ASR), sentiment analysis, and computer vision. This system comprehensively captures language expression, pronunciation accuracy, and non-verbal behavior (like facial expressions), enabling holistic assessment of learners' language abilities. Case studies validate the system's effectiveness, particularly in identifying learning gaps and emotional responses. Ethical considerations, including data privacy and cross-cultural adaptability, are discussed, emphasizing the need for ethical principles and technical standards in multimodal evaluation. In conclusion, multimodal discourse analysis enriches English education assessment, offering insights that deepen understanding of learning processes, inform teaching strategies, and support personalized learning development. Future research should focus on optimizing algorithms, enhancing system intelligence, and exploring broader application scenarios to advance English education assessment modernization.