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1. Lost in Translation: Examining the Complex Relationship Between Prototyping and Communication

   https://doi.org/10.1115/1.4049885  

   Krishnakumar, Sandeep; Berdanier, Catherine; McComb, Christopher; Menold, Jessica (September 2021, Journal of Mechanical Design)

   null (Ed.)

   Abstract Prototyping plays a pivotal role in the engineering design process. Prototypes represent physical or digital manifestations of design ideas, and as such act as effective communication tools for designers. While the benefits of prototyping are well-documented in research, the fundamental ways in which the construction of a prototype affects designers' reflection on and evaluation of their design outcomes and processes are not well understood. The relationships between prototypes, designers' communication strategies, and recollection of design processes is of particular interest in this work, as preliminary research suggests that novice designers tend to struggle to clearly articulate the decisions made during the design process. This work serves to extend prior work and build foundational knowledge by exploring the evaluation of design outcomes and decisions, and communication strategies used by novice designers during prototyping tasks. A controlled in situ study was conducted with 45 undergraduate engineering students. Results from qualitative analyses suggest that a number of rhetorical patterns emerged in students' communications, suggesting that a complicated relationship exists between prototyping and communication. 

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2. Predicting human design decisions with deep recurrent neural network combining static and dynamic data

   https://doi.org/10.1017/dsj.2020.12  

   Rahman, Molla Hafizur; Yuan, Shuhan; Xie, Charles; Sha, Zhenghui (January 2020, Design Science)

   Computational modeling of the human sequential design process and successful prediction of future design decisions are fundamental to design knowledge extraction, transfer, and the development of artificial design agents. However, it is often difficult to obtain designer-related attributes (static data) in design practices, and the research based on combining static and dynamic data (design action sequences) in engineering design is still underexplored. This paper presents an approach that combines both static and dynamic data for human design decision prediction using two different methods. The first method directly combines the sequential design actions with static data in a recurrent neural network (RNN) model, while the second method integrates a feed-forward neural network that handles static data separately, yet in parallel with RNN. This study contributes to the field from three aspects: (a) we developed a method of utilizing designers’ cluster information as a surrogate static feature to combine with a design action sequence in order to tackle the challenge of obtaining designer-related attributes; (b) we devised a method that integrates the function–behavior–structure design process model with the one-hot vectorization in RNN to transform design action data to design process stages where the insights into design thinking can be drawn; (c) to the best of our knowledge, it is the first time that two methods of combining static and dynamic data in RNN are compared, which provides new knowledge about the utility of different combination methods in studying sequential design decisions. The approach is demonstrated in two case studies on solar energy system design. The results indicate that with appropriate kernel models, the RNN with both static and dynamic data outperforms traditional models that only rely on design action sequences, thereby better supporting design research where static features, such as human characteristics, often play an important role. 

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3. From Information to Ideas: How Designers Structure Information to Support Idea Generation

   https://doi.org/10.1115/DETC2020-22527  

   Damen, Nicole B.; Toh, Christine A. (August 2020, International Design Engineering Technical Conferences)

   null (Ed.)

   Design can be seen as a series of decisions that are informed by information that the designer has gathered from the environment and transformed into actionable knowledge. The sheer volume and variety of available information compels designers to impose structure upon the desired information, which in turn may affect subsequent design activities. To better understand how information may inform design decisions, this study investigates the relationship between designers’ information organization behaviors and their generated ideas by recruiting eight professionals (four from software design and four from graphic design) for individual 3-hour design sessions. They were asked to generate ideas for a design problem (reducing pedestrian accidents in Nebraska) using the provided information. Results reveal that designers structured the information in three different ways (Clusters, Relations, and Nests), and both designer background and organizational strategy display different roles in the features generated in their ideas. 

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4. NOVICE DESIGNERS' USE OF PROTOTYPES AS COMMUNICATION TOOLS

   https://doi.org/10.1017/pds.2021.489  

   Krishnakumar, Sandeep; Lauff, Carlye; McComb, Christopher; Berdanier, Catherine; Menold, Jessica (August 2021, Proceedings of the Design Society)

   Abstract Prototypes are critical design artifacts, and recent studies have established the ability of prototypes to facilitate communication. However, prior work suggests that novice designers often fail to perceive prototypes as effective communication tools, and struggle to rationalize design decisions made during prototyping tasks. To understand the interactions between communication and prototypes, design pitches from 40 undergraduate engineering design teams were collected and qualitatively analysed. Our findings suggest that students used prototypes to explain and persuade, aligning with prior studies of design practitioners. The results also suggest that students tend to use prototypes to justify design decisions and adverse outcomes. Future work will seek to understand novice designers’ use of prototypes as communication tools in further depth. Ultimately, this work will inform the creation of pedagogical strategies to provide students with the skills needed to effectively communicate design solutions and intent. 

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5. Human Inductive Biases in Design Decision Making

   https://doi.org/10.1115/DETC2020-22252  

   Shergadwala, Murtuza N.; Panchal, Jitesh H. (August 2020, ASME 2020 International Design Engineering Technical Conferences and Computers and Information Science in Engineering)

   null (Ed.)

   Abstract Designers make information acquisition decisions, such as where to search and when to stop the search. Such decisions are typically made sequentially, such that at every search step designers gain information by learning about the design space. However, when designers begin acquiring information, their decisions are primarily based on their prior knowledge. Prior knowledge influences the initial set of assumptions that designers use to learn about the design space. These assumptions are collectively termed as inductive biases. Identifying such biases can help us better understand how designers use their prior knowledge to solve problems in the light of uncertainty. Thus, in this study, we identify inductive biases in humans in sequential information acquisition tasks. To do so, we analyze experimental data from a set of behavioral experiments conducted in the past [1–5]. All of these experiments were designed to study various factors that influence sequential information acquisition behaviors. Across these studies, we identify similar decision making behaviors in the participants in their very first decision to “choose x”. We find that their choices of “x” are not uniformly distributed in the design space. Since such experiments are abstractions of real design scenarios, it implies that further contextualization of such experiments would only increase the influence of these biases. Thus, we highlight the need to study the influence of such biases to better understand designer behaviors. We conclude that in the context of Bayesian modeling of designers’ behaviors, utilizing the identified inductive biases would enable us to better model designer’s priors for design search contexts as compared to using non-informative priors. 

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