INFORMATION REDUCTION AND STUDIO PROJECT FRAMEWORKS
Editor: Erik Bohemia, Ahmed Kovacevic, Lyndon Buck, Peter Childs, Stephen Green, Ashley Hall, Aran Dasan
Author: Fry, Richard Eldon
Institution: Brigham Young University, United States of America
Section: Design and Engineering Education Practices
Studio projects increase from simple & straightforward to complex & indeterminate as undergraduate industrial design students’ progress through their educational experiences. As project complexity increases, students are faced with information overload and can struggle to move forward in a meaningful way. Complex Problem Solving studies and Cognitive Load Theory suggest information reduction as a way to grasp the critical aspects of a problem and move beyond the impasse inherent with too much information. Segmentation and chunking are common strategies for information reduction but the abstraction inherent in the chunking process provides better conceptual understanding. The simplified but meaningful results from the chunking process can then be leveraged to create a model or framework that helps students organise and clarify what they have observed as well as point to new opportunities for design activity. Despite the fear of oversimplification, significantly abstracted models have great “explanatory or predictive power” and can lead to rich results. Reviewing the concepts of complexity, cognitive load, and contrasting segmentation with data chunking, this paper will then highlight a portion of a student project where information reduction provided understanding beyond initial student impressions and encouraged them to move forward.