Application of Deep Learning Technology in English Practical Teaching

Abstract

Practical teaching emphasizes hands-on learning experiences that engage students in real-world applications of theoretical knowledge. Through practical teaching methods, students gain valuable skills, problem-solving abilities, and critical thinking capacities essential for their future careers. Practical teaching often involves interactive activities, experiments, projects, and simulations tailored to the subject matter, allowing students to explore concepts in depth and develop a deeper understanding of the material. This paper explores the application of deep learning technology in English practical teaching, leveraging Stacked Logistic Deep Learning (SLDL). The study aims to enhance the effectiveness of English language instruction by integrating deep learning techniques into practical teaching methodologies. Through simulated experiments and empirical validations, the efficacy of SLDL-enhanced practical teaching approaches is evaluated. Results demonstrate significant improvements in student proficiency and engagement compared to traditional methods. Simulation analysis expressed that students exposed to SLDL-enhanced practical teaching methods achieved an average score increase of 20% in English language assessments. Additionally, the SLDL model facilitated personalized learning experiences tailored to individual student needs, leading to more effective language acquisition and retention. These findings underscore the potential of deep learning technology, particularly SLDL, in revolutionizing English practical teaching and fostering enhanced learning outcomes.