Design creativity and the semantic analysis of conversations in the design studio
Casakin, Hernan; Georgiev, Georgi V. (2020-12-01)
Hernan Casakin & Georgi V. Georgiev (2021) Design creativity and the semantic analysis of conversations in the design studio, International Journal of Design Creativity and Innovation, 9:1, 61-77, DOI: 10.1080/21650349.2020.1838331
Copyright © 2021 Informa UK Limited. This is an Accepted Manuscript version of the following article, accepted for publication in International Journal of Design Creativity and Innovation. Hernan Casakin & Georgi V. Georgiev (2021) Design creativity and the semantic analysis of conversations in the design studio, International Journal of Design Creativity and Innovation, 9:1, 61-77, DOI: 10.1080/21650349.2020.1838331. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
https://creativecommons.org/licenses/by-nc/4.0/
https://urn.fi/URN:NBN:fi-fe2022030321746
Tiivistelmä
Abstract
The analysis of conversations during design activity can facilitate deeper insights into design thinking and its relation to creativity. A semantic analysis approach was employed to explore the semantic content of communication and information exchange between students and instructors. The goal was to examine design conversations in terms of abstraction, Polysemy, Information Content, and Semantic Similarity measures, and analyze their relation to the creativity of final solutions. These design outcomes were assessed according to their Originality, Usability, Feasibility, Overall Value, and Overall Creativity. Consequently, 35 design conversations from the 10th Design Thinking Research Symposium (DTRS10) dataset were analyzed. The main results showed that Information Content and Semantic Similarity predicted Originality, and Information Content alone predicted Overall Creativity. Likewise, Abstraction predicted Feasibility, while Semantic Similarity, Information Content, and Polysemy predicted Overall Value. In context of instructors, Semantic Similarity predicted Usability, and Polysemy predicted Feasibility. For students, Semantic Similarity predicted Overall Value. On the whole, Semantic Similarity and Information Content were the most prolific measures, and therefore could be considered for promoting creativity in the design studio. The implications of using support tools such as automated systems are also discussed.
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