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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, [email protected]

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

References

Adams, R.S. (2015), “Design review conversations: The dataset”, In: Adams, R. S. and Siddiqui, J. A. (Eds.), Analyzing Design Review Conversations, West Lafayette, Indiana: Purdue University Press.Google Scholar
Adams, R.S. and Siddiqui, J.A. (2013), Purdue DTRS – Design Review Conversations Database, XRoads Technical Report, TR-01-13, West Lafayette, Indiana: Purdue University.Google Scholar
Amabile, T. (1996), Creativity in Context: Update to the Social Psychology of Creativity, Boulder, CO: Westview.Google Scholar
Ashton, P. (1998), “Learning theory through practice: encouraging appropriate learning”, Design Management Journal, Vol. 9 No. 2, pp. 64–68.Google Scholar
Bird, S., Klein, E. and Loper, E. (2009), Natural Language Processing with Python, Sebastopol, California: O'Reilly Media.Google Scholar
Blanchard, E., Harzallah, M. and Kuntz, P. (2008), “A generic framework for comparing semantic similarities on a subsumption hierarchy”, In: Ghallab, M., Spyropoulos, C.D., Fakotakis, N. and Avouris, N. (Eds.), ECAI 2008: 18th European Conference on Artificial Intelligence including Prestigious Applications of Intelligent Systems (PAIS 2008), IOS Press, Patras, Greece, pp. 20–24.Google Scholar
Cash, P., Stanković, T. and Štorga, M. (2014), “Using visual information analysis to explore complex patterns in the activity of designers”, Design Studies, Vol. 35 No. 1, pp. 1–28. http://doi.org/10.1016/j.destud.2013.06.001Google Scholar
Christensen, B.T. and Ball, L.J. (2016), “Dimensions of creative evaluation: Distinct design and reasoning strategies for aesthetic, functional and originality judgments”, Design Studies, Vol. 45, pp. 116–136. https://doi.org/10.1016/j.destud.2015.12.005Google Scholar
Cross, N. (1983), “The relevance of cognitive styles in design education”, Design Methods and Theories, Vol. 17 No. 1.Google Scholar
Csikszentmihalyi, M. (1997), Creativity - flow and the psychology of discovery and invention, New York: Harper Perennial.Google Scholar
Demirbaş, O.O. and Demirkan, H. (2003), “Focus on architectural design process through learning styles”, Design Studies, Vol. 24 No. 5, pp. 437–456. http://doi.org/10.1016/S0142-694X(03)00013-9Google Scholar
Dong, A. (2009), The Language of Design: Theory and Computation, London: Springer.Google Scholar
Fauconnier, G. and Turner, M. (2003), “Polysemy and Conceptual Blending”, In: Nerlich, B., Herman, V., Todd, Z. and Clarke, D. (Eds.), Polysemy: Flexible Patterns of Meaning in Mind and Language, Berlin and New York: Mouton de Gruyter, pp. 79–94.Google Scholar
Georgiev, G.V. and Georgiev, D.D. (2018), “Enhancing User Creativity: Semantic Measures for Idea Generation”, Knowledge-Based Systems, Vol. 151, pp. 1–15. http://doi.org/10.1016/j.knosys.2018.03.016Google Scholar
Georgiev, G.V., Nagai, Y. and Taura, T. (2008), “Method of design evaluation focused on relations of meanings for a successful design”, Tenth International Design Conference Design 2008, May 19–22, Dubrovnik, Croatia, pp. 1149–1158.Google Scholar
Georgiev, G.V., Nagai, Y. and Taura, T. (2010), “A method for the evaluation of meaning structures and its application in conceptual design”, Journal of Design Research, Vol. 8 No. 3, pp. 214–234. http://doi.org/10.1504/jdr.2010.032607Google Scholar
Georgiev, G.V. and Taura, T. (2014), “Polysemy in design review conversations”, 10th Design Thinking Research Symposium, Purdue University, West Lafayette, Indiana: Purdue University.Google Scholar
Gero, J.S. (2011), “Fixation and commitment while designing and its measurement”, The Journal of Creative Behavior, Vol. 45 No. 2, pp. 108–115.Google Scholar
Goldschmidt, G. (2014), Linkography: Unfolding the Design Process, Cambridge, Massachusetts: MIT Press.Google Scholar
Goldschmidt, G., Casakin, H., Avidan, Y. and Ronen, O. (2015), “Three studio critiquing cultures: Fun follows function or function follows fun?”, 10th Design Thinking Research Symposium, Purdue University, West Lafayette, Indiana: Purdue University.Google Scholar
Guilford, J.P. (1981), “Potentiality for creativity”, In: Gowan, J.C., Khatena, J. and Torance, E.P. (Eds.), Creativity: Its educational implications (2nd ed.), Dubuque, IA: Kendall Hunt, pp. 1–5.Google Scholar
Helms, M. and Goel, A. K. (2014), “The Four-Box method: problem formulation and analogy evaluation in biologically inspired design”, Journal of Mechanical Design, Vol. 136 No. 11, p. 111106.Google Scholar
Hill, A., Song, S., Dong, A. and Agogino, A. (2001), “Identifying shared understanding in design using document analysis”, ASME 13-th International Conference on Design Theory and Methodology Design Engineering Technical Conferences, Pittsburgh, Pennsylvania, September 9–12.Google Scholar
Kreitler, S. and Casakin, H. (2009), “Self-perceived creativity: The perspective of design”, European Journal of Psychological Assessment, Vol. 25 No. 3, pp. 194–203. http://doi.org/10.1027/1015-5759.25.3.194Google Scholar
Mabogunje, A. and Leifer, L.J. (1997), “Noun phrases as surrogates for measuring early phases of the mechanical design process”, 1997 ASME Design Engineering Technical Conferences: DETC ’97, Sacramento, California, September 14–17, American Society of Mechanical Engineers.Google Scholar
Resnik, P. (1995), “Using information content to evaluate semantic similarity in a taxonomy”, IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1: Morgan Kaufmann Publishers, pp. 448–453.Google Scholar
Siang, J.K.K., Chia, P.Z., Koronis, G. and Silva, A. (2018), “Exploring the use of a full factorial design of experiment to study design briefs for creative ideation”, ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Quebec City, Quebec, Canada, August 26–29, V007T06A008-V007T06A008, http://doi.org/10.1115/DETC2018-85794Google Scholar
Sarkar, P. and Chakrabarti, A. (2011), “Assessing design creativity”, Design Studies, Vol. 32 No. 4, pp. 348–383. http://doi.org/10.1016/j.destud.2011.01.002Google Scholar
Taura, T., Yamamoto, E., Fasiha, M.Y.N., Goka, M., Mukai, F., Nagai, Y. and Nakashima, H. (2012), “Constructive simulation of creative concept generation process in design: a research method for difficult-to-observe design-thinking processes”, Journal of Engineering Design, Vol. 23 No. 4, pp. 297–321. http://doi.org/10.1080/09544828.2011.637191Google Scholar
Uluoǧlu, B. (2000), “Design knowledge communicated in studio critiques”, Design Studies, Vol. 21 No. 1, pp. 33–58. http://doi.org/10.1016/S0142-694X(99)00002-2Google Scholar
Ward, T.B., Patterson, M.J. and Sifonis, C.M. (2004), “The Role of Specificity and Abstraction in Creative Idea Generation”, Creativity Research Journal, Vol. 16 No. 1, pp. 1–9. http://doi.org/10.1207/s15326934crj1601_1Google Scholar
Yamamoto, E., Goka, M., Yusof, M., Fasiha, N., Taura, T. and Nagai, Y., (2009), “Virtual modeling of concept generation process for understanding and enhancing the nature of design creativity”, In DS 58-2: Proceedings of ICED 09, the 17th International Conference on Engineering Design, Vol. 2, Design Theory and Research Methodology, Palo Alto, CA, USA, August 24–27.Google Scholar
Zangwill, N. (2014), Aesthetic judgment, Stanford Encyclopedia of Philosophy, 02-28-2003/10-22-2007, available: https://plato.stanford.edu/entries/aesthetic-judgment/ [accessed 11 November 2018].Google Scholar