PI: Jonathan Edelman

Abstract

The advancement of machine learning techniques makes it possible to find patterns and derive valuable insights at a fraction of time and cost. In this iteration of the HPDTRP cycle, we work to advance the application of a robust data-driven toolkit by automating the generation of insights from data to support the work of researchers and practitioners.

The Digital Design Observatory leverages machine learning to analyze the microdynamics of design thinking activity and identifies specific Performative Patterns that can be observed in design thinking team sessions. DDO quantifies the constitutive elements of Performative Patterns in the form of facial expressions, gestures, and speech behaviors observed from design teams in more than ten years of research fostered and supported by the HPDTRP (See Report Year 1, Edelman et al. 2019, 2020). DDO augments current modalities for research into—and practice of—innovation and creative collaboration. What are the mechanisms and characteristics of a robust data-driven support toolkit for augmented team coaching and management when iterated in with machine learning?

Team

Joaquin Alberto Santuber Hermosilla, Babajide Alamu Owoyele