Showing papers by "Daniel D. Frey published in 2010"

Research in engineering design: the role of mathematical theory and empirical evidence

Abstract: The editors of this journal have offered an opportunity toreply to Dr. Hazelrigg’s letter in depth. Indeed, with itsnumerous points of critique of the paper ‘‘The Pugh Con-trolled Convergence method’’ (Frey et al. 2009) stated sostrongly, the letter demands a detailed rebuttal. We providea response to the specific points discussed in the letter aswell as the broader issues raised. Writing on these topicshas been an opportunity to explore some issues of interestto us, including the role of mathematical theory andempirical science in design research. To pursue this fully,additional authors participated to add more varied expertiseon social sciences, preference measurement, and industrypractices. We hope that our response will do more thandefend the paper; we hope that it will also suggest someconstructive paths forward in design research.1 The main point: interaction between analysis andsynthesisTo show that a wrong problem is being solved –wrong in the sense that it is not the empirically givenone – is the first ground for rejecting a theory: amatter of irrelevance. A second basis for rejectionwould be to show that improper, inadequate, oroversimplifying assumptions have been made (Mor-genstern 1972).Research in engineering design, like all science, benefitsfrom active critique based on both theory and empiricaldata. Hazelrigg’s letter in effect asserts a veto power of hispreferred mathematical theory over empirical evidence.For example, he writes ‘‘the reader of this or any other suchpaper should never merely assume that it is correct, butverify its validity through personal derivation’’. We agreethat readers should never assume any particular publicationis correct but disagree that personal derivation is anappropriate procedure in this context. If a paper presentsdata inconsistent with the hypotheses of a reader, a math-ematical derivation will not give an adequate justificationto ignore the data. A more appropriate procedure is tocheck the data for accuracy at their source, by replicationof an experiment or by seeking data from other relevantrecords. If the data hold up to review, the deductiveframework of the reader may need to be revised; forexample, by changing its premises or by broadening theframework to incorporate more considerations. In theevaluation of Pugh Controlled Convergence, Hazelrigg’spreferred mathematical framework suggests it will fail, butthe evidence from practice indicates it does not. We submitthat Hazelrigg’s mathematical framework makes improper,inadequate, or oversimplifying assumptions.The primary point of the paper ‘‘The Pugh ControlledConvergence method: model-based evaluation and impli-cations for design theory’’ (Frey et al. 2009) is that deci-sion-making (analysis) and alternative generation(synthesis) have significant interactions that should bemodeled if one is to evaluate design methodologies. Thepaper discusses a documented case study (Khan and Smith

Design pedagogy: Representations and processes

Abstract: This Special Issue of AI EDAM is devoted to research on design pedagogy. In particular, the papers focus on the ways that explicit representations of design knowledge can influence the ways we teach design. The papers use a variety of research approaches and span many areas of design including engineering and architecture, but they all share a concern both for the design process and the practical concerns of design educators in the classroom and in the studio. Design pedagogy raises significant challenges for researchers in design theory, methodology, and artificial intelligence. When people learn engineering design or seek to improve their skill as designers, both the teacher and the student must actively structure their knowledge. The teacher seeks to formalize and structure a body of knowledge gained from design experience. The student is challenged to observe design processes (their own and those of colleagues and teachers). Based on their observations they form hypotheses and test them through projects. The best design processes that emerge from these cycles of practice and reflection must be both effective and teachable. Many design methods have been proposed with great ambitions to improve professional practice. If the improvements do not materialize, which happens all too often, it frequently turns out that the method is not well understood by those seeking to apply it or that the method is not being applied as intended by its developers. It is not an exaggeration to say that a process that cannot be learned or implemented by the majority of designers is a method that cannot succeed in practice. For these reasons, an appreciation of knowledge representation and pedagogy could be a key to better design outcomes. The first three papers seek to link ongoing design instruction to quantitative research. In “The Design Studio “Crit”: Teacher–Student Communication,” the architecture studios at the Technion were in effect used as a research setting. The interactions among students and teachers were recorded. Coding of verbalizations and linkography were used to provide insights into the differences among students and teachers and the various styles of feedback in the studio. Similarly, in “A Study of the Role of User-Centered Design Methods in Design Team Projects,” the design courses at MIT were employed as a source of data. During the design projects, the frequency and duration of interactions with users were recorded. The design outcomes were not a function of the amount of user interactions but appear to be related to their timing. Late in the design process, when design knowledge is most richly represented via prototypes, users can provide a significantly different sort of input to the process than is possible earlier in the process. Thus, in the first two papers, quantitative observations of educational activities give rise to practical advice for design educators. A different approach is taken in “A Course for Teaching Design Research Methodology”; the course is not an object of design research, but rather a means to convey design research methodology. It is interesting to consider how such a course might have affected the other papers in the Special Issue. The next two papers seek to draw upon concepts and methods in social sciences to advance design pedagogy. In the field of cognitive psychology, decision-making heuristics have long been studied as a cause of systematic errors. More recently, heuristics have been analyzed as a strategy for simplifying problem representation that can be effective in realistic contexts. In this second vein, “Cognitive Heuristics in Design: Instructional Strategies to Increase Creativity in Idea Generation” provides evidence that design heuristics can help students generate a greater variety of solutions and solutions that are judged to be more creative. Methods for semistructured interviewing and analysis of the resulting narratives are also emerging from the social sciences. Although other techniques, such as protocol analysis, seek to reduce the influence of subjective experience on the results, some studies now focus on the individual’s perceptions as the primary object of study. The views of 19 experienced design educators are studied in “Scrutinizing Design Educators’ Perceptions of Design Process.” The resulting impression is that experienced designers quickly shift among many representations and processes as they “pick and mix” as the circumstances seem to dictate. In both papers, readers will see the Reprint requests to: Daniel Frey, Department of Mechanical Engineering, 77 Massachusetts Avenue, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA. E-mail: danfrey@mit.edu Artificial Intelligence for Engineering Design, Analysis and Manufacturing (2010), 24, 283–284. # Cambridge University Press, 2010. 0890-0604/10 $25.00 doi:10.1017/S0890060410000193