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Rich Pictures, as defined by Peter Checkland, pictorially and informally capture the structures, viewpoints, responsibilities, and processes of a situation and its inherent relationships. Thus, these pictures aid users to think systemically about a situation. This session will introduce systemic thinking to participants through the use of the modeling technique of Rich Pictures. Vanasupaa, Rogers, and Chen, at the 38 th ASEE/IEEE Frontiers in Education Conference discussed the findings of a pilot study on the outcome of the use of Rich Pictures by materials engineering students. Initial findings indicate that using Rich Pictures can broaden students' ability to participate in systemic thinking. The session proposed here builds on the Vanasupaa et al study to introduce the potential of Rich Pictures to engineering educators. Of specific interest is the building of Rich Pictures by individuals as well as teams of individuals. The goals of this session are: (1) to introduce participants to Rich Pictures as an information modeling technique, (2) to illustrate uses of Rich Pictures for classroom settings (individual and team applications), and (3) to introduce and discuss systems thinking and its applications.

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Since the fall of 2003 the instructor of the Introduction to Engineering Design (IED) course in the College of Engineering and Computer Science at the University of Tennessee at Chattanooga has used peer evaluations as one means to measure performance of student oral presentations.The goal of the peer evaluations is to (1) provide a means of evaluation other than the instructors and to (2) provide students practice in critical thinking. Each student's presentation is evaluated in seven areas -organization, language use, material coverage, physical presence, vocal presence, visual aid use, and length. The students, as well as the instructor, rate each category based on a 5 point scale where 1 is "unacceptable" and 5 is "outstanding". In comparing the average student evaluations with those of the instructor, it first appears that students have difficulty critically reviewing other's work. However, a more in-depth look at the data reveals that students can differentiate between good, average, and poor performance, though not per the given scale standards. It is possible to use the study findings to develop a simple conversion model that converts student evaluations to predict true student performance. This, however, is left for future activity.

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