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

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.