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Semantic Measures for Enhancing Creativity in Design Education

Part of: Design Creativity

Published online by Cambridge University Press:  26 July 2019

Georgi V. Georgiev  and
Hernan Casakin
Georgi V. Georgiev*
Affiliation:
Center for Ubiquitous Computing, University of Oulu, Finland;
Hernan Casakin
Affiliation:
School of Architecture, Ariel University, Israel
*
Contact: Georgiev, Georgi V., University of Oulu, Center for Ubiquitous Computing, Finland, georgi.georgiev@oulu.fi

Abstract

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Analysing verbal data produced during the design activity is helpful to gain a better understanding of design creativity. To understand exchange of information in terms of creative outcomes, a semantic analysis approach was used to measure the semantic content of communications between students and teachers. The goal was to use this tool to analyse design conversations, and to investigate their relation to design creativity, assessed in terms of originality, usability, feasibility, aesthetics, elaboration, overall value and overall creativity. Abstraction, Polysemy, Information Content and Semantic Similarity were employed to explore 35 design conversations from the DTRS10 dataset. Main findings suggest that a significant relationship exists between Information Content and Originality, and between Information Content and Overall creativity of the produced design outcomes. Significant relations were also found between Abstraction, Polysemy, Information Content, and Feasibility, as well as between Semantic Similarity and Overall Value of the outcomes. Implications for the use of semantic measures for encouraging creativity in the design studio are discussed.

Keywords

CreativityDesign cognitionDesign educationSemantic analysisDesign creativity
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 369 - 378
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

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