I-Corps L: TheDesignExchange- Nuturing a National Design Innovation Ecosystem

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

Through the NSF Innovations Corps for Learning Program (I-Corps L), this project will transform TheDesignExchange into a high impact sustainable enterprise, nurturing a national design innovation ecosystem. Human-centered design is emerging as an important approach to innovation and product development. Although there are many programs to introduce the principles of human-centered design to interested individuals, progressing from these programs to becoming an expert is poorly supported. This is especially true for those that focus on the research portion of the human-centered process, those that design and conduct research studies with potential customers and users that inform innovation and product development efforts. TheDesignExchange aims to support individuals as they progress through the levels of design mastery, while encouraging the development of best practices for conducting design research and applying human-centered design methods. TheDesignExchange will match design methods to design problems in order to help designers and design researchers to make informed decisions about when and how to apply methods, and facilitates discussions between practitioners in an exchange of professional support. Another goal is to leverage knowledge gained from theDesignExchange to identify and teach the underlying skills associated with the various methods, providing recent graduates a more fluid understanding of how, why, and when to apply specific design research methods. TheDesignExchange has the potential to produce broad impacts as a source of information on innovation and the design process for the design community, and for those interested in joining these fields, while acting as an online meeting place for community discussions. TheDesignExchange team has created a taxonomy of design methods collected from an in-depth analysis of 82 design process models, and the collection of over 300 design methods. Ongoing work falls into four categories: (1) refinement of the taxonomy through a series of workshops held with design practitioners; (2) collection of relevant design process case studies analyzed for the use of design methods; and (3) development of a design method recommendation system, using machine learning algorithms; (4) development of an interactive and intelligent web portal. By analyzing whether methods frequently co-occur with one another or not, it was found that predictions based on method covariance have higher precision-recall performance than predictions based on problem content; and by using spectral clustering on the method covariance data, methods can be automatically divided into expert-given groupings with 92% accuracy.