Design creativity and the semantic analysis of conversations in the design studio

Abstract

Design creativity and the semantic analysis of conversations in the design studioThe 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 analyse 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 analysed. 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 Contentwere 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.

The art of craft in the interior

“…The importance of master carpenters' skills became apparent in the forms of the Biedermeier furniture, in the relatively short period of its existence, which demonstrates the beauty of both framework and decoration at the same time [24]. The characteristic features of this style of furniture are laconicism and elegance of form.…”

Natural language processing in-and-for design research

“…A design knowledge graph includes facts like <‘stapler’, ‘comprises’, ‘leaf spring’>, < ‘hammer’, ‘push’, ‘staple’ > that could be utilised or generated in the design process. A design knowledge graph carries informative as well as reasoning advantages over networks (Han et al 2021) that provide pairwise statistical (Sarica, Luo, and Wood 2020), semantic (Casakin and Georgiev 2021) and syntactic (Jang, Jeong, and Yoon 2021) relationships among a large collection of design terms (lexicon).…”

“…time for characterising the design communication. Casakin and Georgiev (2021) train regression models to establish a relationship between these network properties and the following metrics of design outcomes: originality, feasibility, usability, creativity and value.…”

Perceptions of Digital Fabrication in Design Education: Skills, Confidence, Motivation, and Enjoyment

“…Therefore, there is a need to understand this issue further in order to propose intervention programs to improve design education in the digital fabrication context. Although digital fabrication courses share some similarities with the design studio (Celani, 2012;Casakin & Georgiev, 2021), due to its specificity (Mostert-van der Sar et al, 2013;Soomro et al, 2021b), the development of curriculum-based digital fabrication has remained a great challenge. While this might not be considered unique compared to the traditional design studio or workshop approaches, FabLabs offer advanced tools through a unique and well organized space that is accessible to a variety of stakeholders, independently of their design background (Lin et al, 2020;Schad & Jones, 2020;Soomro et al, 2021b;Soomro et al, 2022).…”