The Uncertainty Repository Project is creating an online repository that will enable faculty from a number of universities and engineering disciplines to integrate uncertainty concepts with their traditional courses. The set of courses that can be impacted by the project is very large. Because the concepts of uncertainty are critically important in research and industry and because these concepts are traditionally taught in undergraduate curricula, the uncertainty repository project fills a critical need. The repository consists of teaching materials dedicated to uncertainty. These materials include examples of uncertainty that are commonly encountered in industry. The applications range from manufacturing variations to decision-making under uncertainty. In addition to the on-line materials, the project will provide a computer-based learning environment in which students will be presented with worked out examples and practice problems that have interactive components. Instructors will be able to easily find examples applicable to the courses that they are teaching and this enables faculty unfamiliar with uncertainty to be able to integrate the concepts into their teaching.

The investigators are evaluating how well the repository fosters uncertainty literacy. Evaluation efforts will meansure non-deterministic thinking among students. It will also measure faculty familiarity with uncertainty. Because the project evaluation is performed in collaboration with industry there is a great assurance that the project will address the concepts that are critical to engineers in industry. The project is providing a direct link between the idealized classroom teaching environments common in many universities with the uncertainty engineering reality faced by industry engineers.

This project is producing students capable of integrating model uncertainties into design problems and who are able to perform uncertainty analysis in a number of settings and a variety of engineering courses. The program is also helping faculty develop greater skill and knowledge in uncertainty analysis so they are better able to integrate these concepts into their courses.

Agency
National Science Foundation (NSF)
Institute
Division of Undergraduate Education (DUE)
Type
Standard Grant (Standard)
Application #
1245070
Program Officer
Abby Ilumoka
Project Start
Project End
Budget Start
2013-06-15
Budget End
2018-03-31
Support Year
Fiscal Year
2012
Total Cost
$199,879
Indirect Cost
Name
Missouri University of Science and Technology
Department
Type
DUNS #
City
Rolla
State
MO
Country
United States
Zip Code
65409