A Strategic Study of Using Deep Learning to Improve the Effectiveness of English Education in Colleges and Universities

Abstract

Abstract Traditional English education at the tertiary level has predominantly been characterized by rote memorization and recitation, which notably hampers the development of higher-order cognitive skills and the capacity to tackle real-world problems. This paper advocates for a transformative shift towards deep learning within higher English education, positing it as a pivotal metric of student learning efficacy. We introduce a learning architecture model that emphasizes engagement, spatial, and experiential learning dimensions. This model integrates data on learner interactions and deep learning activities to reconstruct the paradigm of English education at the collegiate level. Utilizing a six-dimensional learning process questionnaire scale as a metric for deep learning, we employed SPSS to analyze variations in the adoption of deep learning strategies by students pre-and post-intervention. Additionally, classroom observations and interviews were methodically conducted to document and analyze the learning dynamics, outcomes, and feedback within the deep learning-oriented English classroom. Post-implementation data revealed a significant enhancement in English composition scores, with an average increase of nearly 15 points, from 67.0678 to 81.7508. Correlation analysis further demonstrated a coefficient of 0.051 between deep learning practices and the English proficiency of college students, indicating a positive linear relationship. This study contributes to the existing literature on routine English instruction in higher education by providing a replicable model for enhancing English teaching methodologies and, consequently, elevating the standard of English education at the foundational level.