Enhancing Student Engagement in Higher Education Classrooms Through Deep Learning Principles (88408)

Session Information:

Tuesday, 26 November 2024 13:00
Session: Poster Session 2
Room: Orion Hall (5F)
Presentation Type: Poster Presentation

All presentation times are UTC + 9 (Asia/Tokyo)

This paper examines how deep learning theories and related strategies can be applied to boost student engagement in higher education classrooms. As educational technology evolves and the demand for skilled professionals rises, the traditional teaching methods have struggled to meet the learning needs of modern students. To address this, innovative approaches are required to inspire students’ motivation. Deep learning, which involves higher-level cognitive processes, encourages critical thinking, knowledge transfer, and innovative application, offering new perspectives for reforming classroom practices. This paper will propose several strategies for inspire students’ motivation. First of all, establishing challenging goals will spark students’ curiosity and drive for exploration. Additionally, creating learning environments will promote critical understanding by incorporating diverse viewpoints, case studies, and other methods to enhance students’ critical thinking skills. Furthermore, integrating meaningful and interconnected content through interdisciplinary approaches and project-based learning will help students build a comprehensive knowledge network. Finally, employing instructional design techniques such as the ARCS model will make courses more engaging and maintain students' attention and participation.

Authors:
Yanjun Zhou, Guangxi College for Preschool Education, China
Xiaopeng Gong, Western Oregon University, United States


About the Presenter(s)
Ms. Zhou Yanjun, Associate Professor and Director at Guangxi College for Preschool Education, focuses on preschool curriculum and teacher training. Currently, she is leading a project on action learning methods in preschool teacher training.

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Posted by Clive Staples Lewis

Last updated: 2023-02-23 23:45:00