Analysing transcripts of design activity typically involve either close reading or manual coding of data, which limits the amount of data that can be analysed. In contrast, we explore a machine-learning based linguistic analysis tool called Empath to identify patterns of reasoning in design talk. The data we use derives from the Design Thinking Research Symposium (DTRS) shared-data workshops which we analyse to look at two contrasting aspects of design talk: the expression of tentativeness, characterising designers' generative thinking; and the articulation of explanations, characterising their deductive or analytical thinking. We show, at the level of speech turns, how tentativeness and explanation relate to, and overlap, each other. Finally, we discuss the limitations of this ‘linguistic analysis at scale’ approach.

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Design thinking concepts, such as storytelling, framing, and co-evolution, have been established from close readings of design activity. The increase in easy-to-use computational methodologies provides an opportunity to validate these concepts more widely. Among these concepts, storytelling is already operationalised through various computational approaches. In this chapter, we create one corpus of design activity data from the four shared-data Design Thinking Research Symposium (DTRS) workshops and use Linguistic Inquiry and Word Count (LIWC) in attempting to automatically detect components of stories. However, the conversational nature of the data indicates that further development in methodology is needed. The contribution of the chapter lies both in outlining how an automated method for identifying stories could work and showing how the DTRS corpus can be compared with other large datasets outside of the design discipline. This represents a further step on the way to understanding design thinking in conversational contexts.@en