A Knowledge Conversion Model Based on the Cognitive Load Theory for Architectural Design Education
Yun- Wu 1, Shin Liao 2, Ming-Hui Wen 3 * , Kuo-Hua Weng 1
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1 Department of Architecture, China University of Technology
2 Department of Graphic Arts and Communications, National Taiwan Normal University
3 Department of Commercial Design and Management, National Taipei University of Business
* Corresponding Author

Abstract

The education of architectural design requires balanced curricular arrangements of respectively theoretical knowledge and practical skills to really help students build their knowledge structures, particularly helping them in solving the problems of cognitive load. The purpose of this study is to establish an architectural design knowledge conversion model, helping students to obtain architectural design knowledge through a learning process of knowledge sharing/socialization, extraction, externalization, integration, creation and internalization. This model can help students to effectively solve the problems of cognitive load in the learning process, achieve knowledge construction and storage through meaningful learning, apply their knowledge in their future designs and, ultimately, improve their design capability. This study starts with a literature review on theories of knowledge conversion and cognitive load to establish an architectural design knowledge conversion model complemented with especially designed curricular contents and activities. The model is applied in actual teaching to find out if the application of this model has a positive influence on the students in their learning of architectural design, solving the ill-defined problems in their learning and easing their cognitive load.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

https://doi.org/10.12973/eurasia.2017.01230a

EURASIA J Math Sci Tech Ed, 2017 - Volume 13 Issue 6, pp. 2367-2385

Publication date: 15 Jun 2017

Article Views: 1079

Article Downloads: 541

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