Julie Linsey

Empirical Studies of Designer Thinking: Past, Present, and Future

Understanding how designers think is core to advancing design methods, tools, and outcomes. Engineering researchers have effectively turned to cognitive science approaches to studying the engineering design process. Empirical methods used for studying designer thinking have included verbal protocols, case studies, and controlled experiments. Studies have looked at the role of design methods, strategies, tools, environment, experience, and group dynamics. Early empirical studies were casual and exploratory with loosely defined objectives and informal analysis methods. Current studies have become more formal, factor controlled, aiming at hypothesis testing, using statistical design of experiments (DOE) and analysis methods such as analysis of variations (ANOVA). Popular pursuits include comparison of experts and novices, identifying and overcoming fixation, role of analogies, effectiveness of ideation methods, and other various tools. This paper first reviews a snapshot of the different approaches to study designers and their processes. Once the current basis is established, the paper explores directions for future or expanded research in this rich and critical area of designer thinking. A variety of data may be collected, and related to both the process and the outcome (designs). But there are still no standards for designing, collecting and analyzing data, partly due to the lack of cognitive models and theories of designer thinking. Data analysis is tedious and the rate of discoveries has been slow. Future studies may need to develop computer based data collection and automated analyses, which may facilitate collection of massive amounts of data with the potential of rapid advancement of the rate of discoveries and development of designer thinking cognitive models. The purpose of this paper is to provide a roadmap to the vast literature for the benefit of new researchers, and also a retrospective for the community. [DOI: 10.1115/1.4029025]

A study on the role of physical models in the mitigation of design fixation

Designers implement a variety of models and representations during the design process, yet little is known about the cognitive impacts of various representations. This study focuses on how physical models can assist novices in mitigating design fixation on undesirable features. During idea generation, designers tend to fixate on examples they encounter or on their own initial ideas. The first hypothesis states that designers tend to duplicate features of provided examples. The second hypothesis states that this fixation can be mitigated with appropriate warnings. The last hypothesis is that building and testing physical models can help designers in mitigating fixation. To investigate these theories, a quasi-experiment is conducted as part of a freshman class project. Students design, build and test stunt cars in three different experimental conditions, each receiving a different pictorial example: an effective example, a flawed example and a flawed example with warnings about the flaws. The results show that in all the conditions, designers duplicate undesirable features from their examples, even when they received warnings about the flawed features. Copying these flawed features creates more complicated and less effective designs. However, through the physical testing of their designs, participants identify and fix the design flaws. These results indicate that existing designs and experiences have the potential to limit innovation and that designers need to be trained with effective methods for mitigating design fixation. Building prototypes can help designers in identifying the flawed features and in reducing design fixation; hence, the use of physical models in engineering design needs to be encouraged.

Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews

Different from explicit customer needs that can be identified directly by analyzing raw data from the customers, latent customer needs are often implied in the semantics of use cases underlying customer needs information. Due to difficulties in understanding semantic implications associated with use cases, typical text mining-based methods can hardly identify latent customer needs, as opposite to keywords mining for explicit customer needs. This paper proposes a two-layer model for latent customer needs elicitation through use case reasoning. The first layer emphasizes sentiment analysis, aiming to identify explicit customer needs based on the product attributes and ordinary use cases extracted from online product reviews. Fuzzy support vector machines are developed to build sentiment prediction models based on a list of affective lexicons. The second layer is geared towards use case analogical reasoning, to identify implicit characteristics of latent customer needs by reasoning the semantic similarities and differences analogically between the ordinary and extraordinary use cases. Case-based reasoning is utilized to perform case retrieval and case adaptation. A case study of Kindle Fire HD 7 inch tablet is developed to illustrate the potential and feasibility of the proposed method.

Examining design fixation in engineering idea generation: the role of example modality

Design fixation is a major concern in engineering idea generation because it restricts the solution space in which designers search for their ideas. For designers to be more creative, it is essential to mitigate their fixation. The majority of studies in the literature investigate the role of pictorial stimuli in design fixation; however, the role of examples presented in other formats, including physical prototypes, is largely unknown. This paper presents a study that compares design fixation, in novice designers, caused by pictorial and physical representations. The effects of defixating materials proposed by Linsey et al. (2010) are also investigated. The results show that physical formats cause a higher magnitude of fixation than pictorial formats; however, participants utilizing physical examples produce a greater quantity of nonredundant ideas. Consistent with prior studies, the results also indicate that the defixation materials may not facilitate mitigation of novice designers fixation.

A STEP BEYOND TO OVERCOME DESIGN FIXATION: A DESIGN BY ANALOGY APPROACH

Design fixation is a phenomenon that negatively impacts design outcomes, especially when it occurs during the ideation stage of a design process. This study expands our understanding of design fixation by presenting a review of de-fixation approaches, as well as metrics employed to understand and account for design fixation. The study then explores the relevant ideation approach of Design-by-Analogy (DbA) to overcome design fixation, with a fixation experiment of 73 knowledge-domain experts. The study provides a design fixation framework and constitutes a genuine contribution to effectively identify approaches to mitigate design fixation in a wide range of design problems.

METHODS FOR PROTOTYPING STRATEGIES IN CONCEPTUAL PHASES OF DESIGN: FRAMEWORK AND EXPERIMENTAL ASSESSMENT

Prototyping may be simultaneously one of the most important and least formally explored areas of design. Over the last few decades, designers and researchers have developed many methodologies for ideation, product architecture, design selection, and many other aspects of the design process. However, there have been relatively few methodologies published regarding the efficient and effective development of prototypes for new products. This research explores a methodology for enhancing the prototyping process. It is founded on extensive literature review of the best practices of engineering prototype development. These findings have been aggregated and form the foundation of a methodology for formulating prototyping strategies. This methodology has then been experimentally evaluated in a controlled design environment, and its effect on the performance of prototypes has been demonstrated. The method consists of a set of guiding questions with corresponding flowcharts and foundational equations that assist the designer to make choices about how to approach the prototyping process in an efficient and effective manner.

Empirical Studies of Design Thinking: Past, Present, Future

Empirical methods used for studying design thinking have included verbal protocols, case studies, and controlled experiments. Studies have looked at the role of design methods, strategies, tools, environment, experience, and group dynamics. Early empirical studies were casual and exploratory with loosely defined objectives and informal analysis methods. Current studies have become more formal, factor controlled, aiming at hypothesis testing, using statistical DOE and analysis methods such as ANOVA. Popular pursuits include comparison of experts and novices, identifying and overcoming fixation, role of analogies, effectiveness of ideation methods, and other various tools. A variety of data may be collected, related to both the process and the outcome (designs).There are still no standards for designing, collecting and analyzing data, partly due to the lack of cognitive models and theories of design thinking. Data analysis is tedious and the rate of discoveries has been slow. Future studies may need to develop computer based data collection and automated analyses, which may facilitate collection of massive amounts of data with the potential of rapid advancement of the rate of discoveries and development of cognitive models of design thinking.Copyright

PerSketchTivity: An Intelligent Pen-Based Educational Application for Design Sketching Instruction

Design sketching is an important and versatile skill for engineering students to master in order to translate their design thoughts effectively onto a visual medium, whether it is to proficiently produce hand-drawn sketches onto paper, seamlessly interact with intelligent sketch-based modeling interfaces, or reap the various educational benefits associated with drawing. Traditional instructional approaches for teaching design sketching are frequently constrained by the availability of experienced human instructors or the lack of supervised learning from self-practice, while relevant intelligent educational applications for sketch instruction have focused more on assessing users’ art drawings or cognitive developmental progress. We introduce PerSketchTivity, an intelligent pen-based computing educational application that not only teaches engineering students how to hone and practice their design sketching skills through stylus-and-touchscreen interaction, but also aiding their motivation and self-regulated learning through real-time feedback. From the qualitative results of our usability tests of our application from eight university student participants of varying skill levels and disciplines, we observed that participants well-rated the usability of the application while also providing valuable feedback to improve the application even further.

Mechanix: A Sketch-Based Tutoring System that Automatically Corrects Hand-Sketched Statics Homework

With the rise in classroom populations—in both physical classrooms and online learning environments such as massively open online courses—instructors are struggling to provide relevant and personalized feedback on student work. As a result, many instructors choose to structure their homework assignments and assessments via multiple-choice questions or other more automatable techniques, rather than assign complete problems and diagrams. In this work, we aim to provide a new solution to the instructors of introductory engineering courses. We leveraged the power of sketch-recognition and artificial intelligence to create Mechanix, a sketch-based system that tutors students through drawing and solving free-body diagrams. Mechanix can support problems that have only a single answer, as well as questions for which many answers might apply (i.e. design this vs. solve this).

Mechanix: A Sketch-Based Educational Interface

At the university level, high enrollment numbers in classes can be overwhelming for professors and teaching assistants to manage. Grading assignments and tests for hundreds of students is time consuming and has led towards a push for software-based learning in large university classes. Unfortunately, traditional quantitative question-and-answer mechanisms are often not sufficient for STEM courses, where there is a focus on problem-solving techniques over finding the right answers. Working through problems by hand can be important in memory retention, so in order for software learning systems to be effective in STEM courses, they should be able to intelligently understand students sketches. Mechanix is a sketch-based system that allows students to step through problems designed by their instructors with personalized feedback and optimized interface controls. Optimizations like color-coding, menu bar simplification, and tool consolidation are recent improvements in Mechanix that further the aim to engage and motivate students in learning.