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Conference Session
ChE: Innovation in the Laboratory
Collection
2006 Annual Conference & Exposition
Authors
Brian Lefebvre, Rowan University
Tagged Divisions
Chemical Engineering
overall problem or task. 3. Design an authentic task. 4. Design the task and the learning environment to reflect the complexity of the environment they should be able to function in at the end of learning. 5. Give the learner ownership of the process used to develop a solution. 6. Design the learning environment to support and challenge the learner’s thinking. 7. Encourage testing ideas against alternative views and alternative contexts. 8. Provide opportunity for and support reflection on both the content learning and the learning process.Critics contend that the constructivist approach stimulates learning only in concepts in which thestudents have an existing interest.4 Taken to the extreme, the
Conference Session
ChE: Curriculum Reform & Assessment
Collection
2006 Annual Conference & Exposition
Authors
Jeffrey Froyd; Jean Layne, Texas A&M University; Lale Yurttas, Texas A&M University; David Ford, Texas A&M University
Tagged Divisions
Chemical Engineering
portfolios24,25,26 tailored for this project. Liketeaching/course portfolios, the course folders are a place to assemble artifacts of the teachingprocess that illustrate faculty statements regarding their philosophy and practice and can then beused as catalysts both for faculty reflection and faculty peer review. These documents provide afoundation for greater understanding of the department curriculum as a whole by providing alaunching point for examination of both the content involved in each course (what students needto know) and the tasks in terms of both formative and summative assessment (what students needto be able to do) that are the focus of the curriculum. Documentation that facilitates facultyknowledge and interaction in these areas can have a
Conference Session
ChE: Departmental Issues and Integrating Freshmen into the ChE Program
Collection
2006 Annual Conference & Exposition
Authors
Charles Coronella, University of Nevada-Reno
Tagged Divisions
Chemical Engineering
well with the psychrometric chartand the design diagrams, while verbal learners gain from the classroom discussions and from thebook reading. Active learners especially benefit from the incorporation of this project, since itrequires hands-on building in a group. Reflective learners profit from writing the reports and Page 11.1041.2completing the homework assignments. Both inductive and deductive learning styles areincorporated, since the nature of evaporative cooling is understood by all at some intuitive level,and we analyze the cooling by looking at rates of heat transfer, mass transfer, andthermodynamics. Finally, iterative design benefits
Conference Session
ChE: Experimental Design & Error Analysis
Collection
2006 Annual Conference & Exposition
Authors
Stephanie Farrell, Rowan University
Tagged Divisions
Chemical Engineering
of the report. Forexample, human error was frequently cited as a source of error in experimental procedure – withthe implication that this is acceptable, legitimate, or unavoidable. In the laboratory, studentsfailed to use techniques to reduce experimental error when necessary. Data were often notreported correctly to reflect uncertainty in measurement, and simple statistical techniques wererarely used to analyze error.A variety of methods for the introduction of error analysis to lower level engineering studentshave been described by other educators. Sterrett and Helgeson2 used parametric computersimulations to introduce error analysis to sophomores in a design course. Reardon3 introduceslinear regression and propagation of error analysis
Conference Session
ChE: Experimental Design & Error Analysis
Collection
2006 Annual Conference & Exposition
Authors
Milo Koretsky, Oregon State University; Shoichi Kimura, Oregon State University; Connelly Barnes, Oregon State University; Danielle Amatore, Oregon State University; Derek Meyers-Graham, Oregon State University
Tagged Divisions
Chemical Engineering
anincreasingly essential skill for engineers. This requires not only knowledge of statisticalconcepts related to DOE, but also the ability to integrate this methodology with fundamentalengineering principles toward designing and understanding experiments. However, currentengineering curriculums have not fully adapted to this need in the engineering industry. In the1970s and 1980s, the absence of sound statistical methods in the engineering work place led to acrisis in US industry where a large percentage of the market share went overseas. This crisis wasfirst reflected in the manufacture of automobiles and then in the process-oriented manufacture ofintegrated circuits.1,2 Only with the industrial investment towards quality, largely through the