Exploring the Optimization Model of University English Blended Teaching Mode Combined with Deep Learning

Abstract

With university English blended teaching mode combined with deep learning marks a significant advancement in language education. With integrating deep learning techniques into blended teaching approaches, educators can leverage vast amounts of educational data to enhance the effectiveness and personalization of English language instruction. Deep learning algorithms analyze student interactions, performance data, and learning preferences to tailor instructional content and activities to individual needs. This model enables educators to create dynamic and adaptive learning experiences that blend traditional classroom instruction with online resources and digital tools. The paper presents a comprehensive examination of blended teaching methodologies within the realm of English education, integrating principles of deep learning to enhance learning outcomes. Through a series of experiments and analyses, the study investigates the effectiveness of various teaching components, the utilization of online resources, and the application of diverse deep learning architectures. Findings underscore the significance of a balanced integration of traditional pedagogies and digital resources, with online materials emerging as a pivotal aspect of the teaching strategy. The study conducted experiments evaluating the effectiveness of blended teaching methodologies in English education, with numerical scores ranging from 4.2 to 9.0 across different parameters. Results indicated that experiments with higher scores for traditional teaching effectiveness (ranging from 6.5 to 9.2) and deep learning integration (ranging from 3.9 to 9.1) tended to yield superior overall learning outcomes (ranging from 4.3 to 9.0). Furthermore, online resources received consistently high weighted scores (ranging from 7.2 to 9.5), underscoring their significant contribution to the overall teaching strategy.