Research Methodology

(With an emphasis on engineering design research)

The objective underlying this study is the improvement of design research. Some issues that should be addressed through this study are:

1.         How research projects dealing with design could be improved through studying the failures and successes of previous research with respect to their stated goals?

2.         How research could be designed with state-of-the-art design tools to adhere to the principle of reflexive practice?

3.         How should design research be evaluated? Research projects whose (stated) goal is to impact engineering practice must be evaluated in practice using a case-study approach. Other projects can undergo evaluations that fall under controlled or semi-controlled experiments.


The study of research methodology borrows from other disciplines (e.g., education, public policy) that have started to deal with these issues more than a decade ago. See more information in the first three publications


Publications include:

1.             Reich, Y. (Ed) (1994), ``Special issue on research methodology.'' Vol. 8, no. 4 of the journal Artificial Intelligence for Engineering Design, Analysis, and Manufacturing (AI EDAM).

2.             Reich, Y. (1994), Forward to a Special Issue of AI EDAM on Research Methodology, AI EDAM, 8(4):261-262.
In this issue papers have dealt with general issues of research methodology, evaluation of research results, description of long term projects, and specific projects that had to deal with tough questions about how to perform their studies. (Postscript file, 55K; PDF, 91K zipped)

3.             Reich, Y. (1994), Layered Models of Research Methodologies, AI EDAM, 8(4):263-274.
(Postscript file, 226K; PDF, 807K gzipped)

4.             Reich, Y. (1994), Bibliography on Research Methodology, AI EDAM, 8(4):355-366.
(Postscript file, 185K; PDF, 734K gzipped)

5.             Reich, Y. (1994), The Study of Design Research Methodology, Transactions of the ASME, Journal of Mechanical Design, 117(2A):211-214.
(Postscript file, 118K; PDF, 296K gzipped).

6.             Reich, Y. (1993), The Development of Bridger: A Methodological Study of Research on Machine Learning in Design, Artificial Intelligence in Engineering, 8(3):217-231.
This paper presents a reflection on the course of the development of Bridger. The aim is to give a more accurate description of the steps that were carried out and their underlying reasons than is usually given in a paper. (Postscript file, 359K; PDF, 1M gzipped)

7.             Reich, Y. (1994), What is Wrong with CAE And Can it be Fixed, In Preprints of Bridging the Generations: An International Workshop on the Future Directions of Computer-Aided Engineering, (Pittsburgh, PA), Rehak, D. (ed.), Department of Civil Engineering, Carnegie Mellon University.
The paper analyzes two projects under the guidance of Steven J. Fenves (for whose honor the workshop was organized) and concludes that contextualized research is the way to improve CAE research. Such research is exemplified in the n-dim project. (Postscript file, 117K; PDF, 285K gzipped).

8.             Reich, Y. (1992), The Theory Practice Problem of Technology, Tech. Rep. EDRC 12-51-92, Engineering Design Research Center, Carnegie Mellon University, Pittsburgh, PA.
This is a long document, very different from the other papers. It has some history of CAE and design research, some philosophy, and some future directions connected to the n-dim project. It is relevant to quality management, knowledge management, and other practices that involve theory and practice in cultural context. (Gzipped Postscript 110K; Zipped PDF, 1.47M)

9.             Reich, Y. (1995), The Value of Knowledge, International Journal of Human-Computer Studies, 42(1):3-30.
The issue of knowledge evaluation is raised often in relation to building intelligent systems, knowledge acquisition, or machine learning. There are many proposals, both quantitative and qualitative that float around. This paper organizes these by using the ideas of measurement theory. The paper illustrates the ideas through the evaluation of the design knowledge acquired by Bridger, an experimental system for learning knowledge on cable-stayed bridge design. (Postscript file, 136K compressed; PDF, 185K zipped)

10.         Y. Reich and S. V. Barai, “Evaluating Machine Learning Models for Engineering Problems,” Artificial Intelligence in Engineering, vol. 13, no. 3, pp. 257-272, 1999.
[gzipped postscript| zipped PDF]

11.         Y. Reich, E. Kolberg, and I. Levin, “Designing contexts for learning design,” International Journal of Engineering Education, 22(3):489-495, 2006.



Copyright © 1997-2005 Yoram Reich
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Last modified: 8/20/2006 10:40:00 AM