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1. Exploring Generative Design Thinking for Engineering Design and Design Education

   Li, Xingang ; Demirel, H. Onan ; Goldstein, Molly H. ; Sha, Zhenghui ( September 2021 , 2021 ASEE Midwest Section Conference)
2. Design Embedding: Representation Learning of Design Thinking to Cluster Design Behaviors

   https://doi.org/10.1115/DETC2021-72406  

   Hafizur Rahman, M. ; Xie, C. ; Sha, Z. ( November 2021 , ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Design thinking is essential to the success of a design process as it helps achieve the design goal by guiding design decision-making. Therefore, fundamentally understanding design thinking is vital for improving design methods, tools and theories. However, interpreting design thinking is challenging because it is a cognitive process that is hidden and intangible. In this paper, we represent design thinking as an intermediate layer between human designers’ thought processes and their design behaviors. To do so, this paper first identifies five design behaviors based on the current design theories. These behaviors include design action preference, one-step sequential behavior, contextual behavior, long-term sequential behavior, and reflective thinking behavior. Next, we develop computational methods to characterize each of the design behaviors. Particularly, we use design action distribution, first-order Markov chain, Doc2Vec, bi-directional LSTM autoencoder, and time gap distribution to characterize the five design behaviors. The characterization of the design behaviors through embedding techniques is essentially a latent representation of the design thinking, and we refer to it as design embeddings. After obtaining the embedding, an X-mean clustering algorithm is adopted to each of the embeddings to cluster designers. The approach is applied to data collected from a high school solar system design challenge. The clustering results show that designers follow several design patterns according to the corresponding behavior, which corroborates the effectiveness of using design embedding for design behavior clustering. The extraction of design embedding based on the proposed approach can be useful in other design research, such as inferring design decisions, predicting design performance, and identifying design actions identification.

    

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3. Design thinking and agile design: New trends or just good design?

   Svihla, V. ( December 2023 , Foundations of learning and instructional design technology (2nd Ed.))

   West, R. ; Leary, H. (Ed.)

   While most instructional design courses and much of the instructional design industry focus on ADDIE (Analysis, Design, Development, Implementation,Evaluation), approaches such as design thinking, human-centered design, and agile methods like SAM (Successive Approximation Model), have drawn attention.This chapter unpacks what we know about design thinking and presents a concise history of design thinking to situate it within the broader design research fi eld. Itthen traces its emergence in other fi elds. The chapter considers lessons for instructional designers and concludes by setting an agenda to address issues forscholarship, teaching, and practice. 

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4. Game design experience for teachers’ design thinking in participatory design culture

   Pan, Y. ; Ke, F. ; Dai, C-P. ( January 2022 , Proceedings of the 16th International Conference of the Learning Sciences (ICLS) 2022)

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5. Design Thinking in Engineering Course Design

   https://doi.org/10.18260/1-2--30271  

   Fila, Nicholas ; McKIlligan, Seda ; Guerin, Kelly ( June 2018 , 2018 ASEE Annual Conference)

   Design thinking is a robust framework for creatively and effectively identifying and solving important human problems. While design thinking is commonly associated with fields like industrial design, it can be applied to many problem types. For example, several recent examples demonstrate the applicability of design thinking to the design and development of educational materials, courses, and systems. These results suggest that design thinking could be used as a framework to (re)design and develop effective engineering courses. The goal of this project is to understand how nine educators from different backgrounds did or did not use design thinking to redesign a sophomore-level electrical and computer engineering course. The primary source of data was 21 transcribed audio recordings of design meetings and is supplemented with interviews, reflections, and course artifacts. Thematic analysis revealed 10 themes that represent connections and disconnections between the process used and a common five-stage design thinking process (empathize, define, ideate, prototype, and test). These themes demonstrate some of the opportunities and challenges related to design thinking within an engineering course design setting. In particular, they suggest that engineering course design is a relevant context for design thinking, but one to which design thinking methods do not always naturally translated. Future work should focus on better understanding unique applications of design thinking within engineering course design and methods that might to support more designerly behaviors among engineering educators. 

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