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Activity Number:
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182
- Innovations on Teaching Design of Experiments: Active Learning, Data Science, and Computer-Generated Designs
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Type:
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Invited
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract #320511
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Title:
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Innovative Experimental Design Education: Active Learning, Data Science, and Computer-Generated Designs
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Author(s):
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Byran J Smucker* and Nathaniel Stevens* and Jacqueline Asscher* and Alan Vasquez*
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Companies:
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Miami University and University of Waterloo and Kinneret College on the Sea of Galilee and UCLA
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Keywords:
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experiments;
statistics education;
data science education;
design
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Abstract:
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Experimental design is a venerable but increasingly relevant area of statistics whose pedagogy has been understudied in the statistical education literature. This session will present four distinct perspectives on teaching design of experiments (DOE): (1) a relatively traditional course; (2) a course that incorporates active learning concepts; (3) an approach built upon applications to data science, including A/B testing; and (4) a course based upon computer-generated optimal designs. The panel will address questions such as: Do we need separate DOE courses for different groups, including statistics, data science, and engineering students? Should DOE be a compulsory course in a data science degree program? Who should teach these courses? How should traditional DOE courses change for data science students – evolution or revolution? Can an optimal design approach completely replace traditional DOE? Would incorporating more real projects, simulators and case studies improve learning? How should we use textbooks and software? This session will also briefly describe the results of a survey which provides a snapshot of how DOE is currently being taught in North America.
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Authors who are presenting talks have a * after their name.
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