A Study on Optimization Paths for Digital Deep Learning in Specialized English Education in Higher Vocational Colleges from the Perspective of Cognitive Load Theory
Year: 2025
Type: article
Abstract: This study focuses on the optimization paths for digital deep learning in specialized English education in higher vocational colleges from the perspective of Cognitive Load Theory. Based on expounding the core concepts and classifications of Cognitive Load Theory, and by reviewing the current status of relevant domestic and international research, it deeply analyzes the current situation and existing problems of digitalization in specialized English education in higher vocational colleges. Furthermore, starting from Cognitive Load Theory, it proposes targeted optimization paths, including reducing extraneous cognitive load, enhancing germane cognitive load, and reasonably addressing intrinsic cognitive load. The aim is to provide theoretical support and practical guidance for improving the effectiveness of digital deep learning in specialized English education in higher vocational colleges and to promote the effective enhancement of students' specialized English proficiency.
DOI: 10.54097/z35csg85
Author: Xiang Huang
Institution: Jiangsu Maritime Institute
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4413400107