Hostname: page-component-cb9f654ff-5kfdg Total loading time: 0 Render date: 2025-08-21T19:14:27.222Z Has data issue: false hasContentIssue false
  • English
  • Français

EXPLORING THE APPLICABILITY OF SEMANTIC METRICS FOR THE ANALYSIS OF DESIGN PROTOCOL DATA IN COLLABORATIVE DESIGN SESSIONS

Part of: Design Innovation, Information and Knowledge

Published online by Cambridge University Press:  11 June 2020

N. Becattini ,
G. V. Georgiev ,
Y. Barhoush  and
G. Cascini
N. Becattini*
Affiliation:
Politecnico di Milano, Italy
G. V. Georgiev
Affiliation:
University of Oulu, Finland
Y. Barhoush
Affiliation:
University of Oulu, Finland
G. Cascini
Affiliation:
Politecnico di Milano, Italy
*
*niccolo.becattini@polimi.it

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The paper presents the application of non-specialized lexical database and semantic metrics on transcripts of co-design protocols. Three different and previously analyzed design protocols of co-creative sessions in the field of packaging design, carried out with different supporting tools, are used as test-bench to highlight the potential of this approach. The results show that metrics about the Information Content and the Similarity maps with sufficient precision the differences between ICT- and non-ICT-supported sessions so that it is possible to envision future refinement of the approach.

Keywords

text miningbig data analysiscollaborative designresearch methodologies and methodshuman behaviour

Information

Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 1205 - 1214
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), 2020. Published by Cambridge University Press

References

Balters, S. and Steinert, M. (2017), “Capturing emotion reactivity through physiology measurement as a foundation for affective engineering in engineering design science and engineering practices”, Journal of Intelligent Manufacturing, Vol. 28 No. 7, pp. 1585–1607.10.1007/s10845-015-1145-2CrossRefGoogle Scholar
Becattini, N. et al. (2017), “Characterisation of a co-creative design session through the analysis of multi-modal interactions”, International Conference on Engineering Design, Vol. 2017, pp. 479–488.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
Boden, M.A. (2004), The creative mind: Myths and mechanisms, Routledge.10.4324/9780203508527CrossRefGoogle 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://dx.doi.org/10.1016/j.destud.2013.06.001CrossRefGoogle Scholar
Dong, A. (2009), The Language of Design: Theory and Computation, Springer, London.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, Mouton de Gruyter, Berlin and New York, pp. 79–94.Google Scholar
Georgiev, G.V. and Casakin, H. (2019), “Semantic Measures for Enhancing Creativity in Design Education”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 369–378. https://doi.org/10.1017/dsi.2019.40Google 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://dx.doi.org/10.1016/j.knosys.2018.03.016CrossRefGoogle Scholar
Georgiev, G.V. and Taura, T. (2014), “Polysemy in design review conversations”, 10th Design Thinking Research Symposium, Purdue University, West Lafayette, Purdue University, Indiana.Google Scholar
Hay, L. et al. (2017), “A systematic review of protocol studies on conceptual design cognition”, Design Computing and Cognition’16, Springer, Cham, pp. 135–153.CrossRefGoogle Scholar
Hu, W.L., Booth, J. and Reid, T. (2015, August), “Reducing sketch inhibition during concept generation: psychophysiological evidence of the effect of interventions”, ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers Digital Collection.Google Scholar
Kan, J.W. and Gero, J.S. (2017), Quantitative methods for studying design protocols, Springer, Dordrecht.10.1007/978-94-024-0984-0CrossRefGoogle Scholar
Liu, Y. et al. (2014), “A fuzzy psycho-physiological approach to enable the understanding of an engineer's affect status during CAD activities”, Computer-Aided Design, Vol. 54, pp. 19–38.10.1016/j.cad.2013.10.007CrossRefGoogle Scholar
Nguyen, P., Nguyen, T.A. and Zeng, Y. (2018), “Empirical approaches to quantifying effort, fatigue and concentration in the conceptual design process”, Research in Engineering Design, Vol. 29 No. 3, pp. 393–409.10.1007/s00163-017-0273-4CrossRefGoogle 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
Ruckpaul, A., Nelius, T. and Matthiesen, S. (2015), Differences in analysis and interpretation of technical systems by expert and novice engineering designers.Google Scholar
SPARK Project consortium (2018), Deliverable Results of the experiments benchmarking the platform.Google Scholar
Steinert, M. and Jablokow, K. (2013), “Triangulating front end engineering design activities with physiology data and psychological preferences”, DS 75-7: Proceedings of the 19th International Conference on Engineering Design (ICED13), Design for Harmonies, Vol. 7: Human Behaviour in Design, Seoul, Korea, 19-22 August 2013, pp. 109–118.Google Scholar
Taura, T. et al. (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://dx.doi.org/10.1080/09544828.2011.637191CrossRefGoogle 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://dx.doi.org/10.1207/s15326934crj1601_1CrossRefGoogle Scholar
Wilkenfeld, M.J. and Ward, T.B. (2001), “Similarity and emergence in conceptual combination”, Journal of Memory and Language, Vol. 45 No. 1, pp. 21–38. http://doi.org/10.1006/jmla.2000.2772CrossRefGoogle Scholar
Wulvik, A., Menning, A. and Steinert, M. (2017), “A computational approach to expose conversation dynamics in engineering design activities”, DS 87-2 Proceedings of the 21st International Conference on Engineering Design (ICED 17) Vol 2: Design Processes, Design Organisation and Management, Vancouver, Canada, 21-25 August 2017, pp. 101–110.Google Scholar