Authentic design commonly involves teams of designers collaborating on ill-structured problems over extended time periods. Nonetheless, design has been studied extensively in sequestered settings, limiting our understanding of design as process and especially of learning design process. This study addresses potential shortcomings of such studies by examining in-situ student team design. The participants of this study are three cohorts of a year-long capstone biomedical engineering design class at The University of Texas. Pilot research demonstrated advantages of a more authentic redesign task over a kit-based design task; students who chose devices to redesign were significantly better at representing perspective taking associated with customers' needs. Pilot research showed that there was no relationship between Early Efficiency (appropriate use of factual and conceptual knowledge) and Final Innovation of design products. I triangulated various methods for studying design: Qualitative research, Hierarchical Linear Modeling, and Social Network Analysis, the latter of which allowed me to generate team-level statistics of interaction (Cohesion), once I devised a practical method to account for missing data in a weighted network. Final Efficiency is a function of Early Innovation, early and late Cohesion, and team feasibility (factual and practical knowledge). Final Innovation is a function of Early Innovation, late Cohesion, and team Voice of the Customer (perspective-taking), with all relationships in both models positive. Measures of both design skills and interaction are required to explain variance in these outcomes. Narratives of team negotiation of design impasses --seemingly insurmountable barriers-- provide deeper understanding of relationships between design process and products. The case study teams spent a large percentage of their time engaged in problem scoping, but framed as engineering science rather than as engineering design. Only when they began prototyping did they transition towards being solution focused and frame the problem as engineering design. This left little time for iteration of the final design. Variance in timing of iteration may account for slight deviations of the case study teams from the statistical model. Recommendations include earlier opportunities to design and support for team collaboration. Social network analysis is recommended when learning is interactional and to support triangulation. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/17411 |
| Date | 13 August 2012 |
| Creators | Svihla, Vanessa |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Format | electronic |
| Rights | Copyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works. |
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