Semantic measures in design conversations as predictors of creative outcomes in design education

Georgi V. Georgiev, Hernan Casakin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The analysis of conversations maintained during the design activity can help to gain a better insight into design thinking and its relation to creativity. A semantic analysis approach was employed to inspect the content of communications and information exchange between students and instructors. The goal was to explore design conversations in terms of Abstraction, Polysemy, Information Content and Semantic Similarity measures, and analyse their relation to the creativity of final design outcomes. These were assessed according to their Originality, Usability, Feasibility, Overall Value and Overall Creativity. To this end, design conversations from the 10th Design Thinking Research Symposium (DTRS10) dataset were used. Main results show a significant relationship between Information Content and Originality and Overall Creativity. For instructors, Semantic measures were mainly related to Feasibility, whereas for students the focus was set on the Overall Value of the final solutions.

Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on Design Creativity, ICDC 2020
EditorsJean-Francois Boujut, Gaetano Cascini, Saeema Ahmed-Kristensen, Georgi V. Georgiev, Netta Iivari
Pages344-351
Number of pages8
ISBN (Electronic)9781912254118
DOIs
StatePublished - 2020
Event6th International Conference on Design Creativity, ICDC 2020 - Oulu, Finland
Duration: 26 Aug 202028 Aug 2020

Publication series

NameProceedings of the 6th International Conference on Design Creativity, ICDC 2020

Conference

Conference6th International Conference on Design Creativity, ICDC 2020
Country/TerritoryFinland
CityOulu
Period26/08/2028/08/20

Keywords

  • Creative measures
  • Design cognition
  • Design creativity
  • Design education
  • Semantic analysis
  • Semantic measures

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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