1. Integrating Application Based Modules into the Stochastic Processes Curriculum

2. Project Sponsor National Science Foundation Directorate for Education and Human Resources Division of Undergraduate Education Course Curriculum and Lab Improvement Program Educational Materials Development Proof-of-Concept Project #0230643

3. Personnel Principal Investigator: Timothy I. Matis tmatis@nmsu.edu Co-Principal Investigator: Linda Ann Riley linriley@nmsu.edu

4. Industrial Partners Fort Bliss Federal Credit Union Sandia National Labs Ethicon – Johnson & Johnson Celestica

5. Motivation for Project ► Address Learning Challenges Frequently Encountered by Undergraduate Students in this Course, B. Nelson, ABET 2000 Assessment at NMSU ► Align the Undergraduate Stochastic Processes Curriculum with that of the IE Discipline (Application of Subject Matter), W. Kuo and B. Deuermeyer, J. Buzacott ► Collaborate with Industry in Curriculum Development, W. Kuo

6. Common Undergraduate Learning Challenges ► Difficulty understanding the theoretical aspects of the topic ► Failure to fully comprehend the probability modeling process ► Difficulty transferring knowledge to “real” industrial problems

7. Shortcomings of Traditional Instruction Techniques B. Nelson ► Expect too much -- Primary focus is on theoretical development of the topic ► Expect too little -- Presentation of many formulas without supporting structure ► Failure to distinguish between probability models and the analysis methods

8. Application-Based Instructional Modules A set of application-based modules are being developed as part of this project to address the common learning challenges of undergraduate students in an applied stochastic processes course.

9. Module Composition Each module develops a “real” problem from a particular industry/government agency whose solution involves stochastic processes The problem is presented to the students by industrial representatives in a consulting- type framework through digital video media (DVD).

10. DVD Contents ► Viewable DVD Files  Problem Description  Data Description  Credits ► DVD-ROM  Raw Data Files  Supporting Documents  Student Resources (sample Mathematica ® programs)

11. Classroom Implementation The modules are to be supplementary to regular lectures. A time frame of 3-4 weeks per module is appropriate. Students are to work in teams to solve the problem, i.e. formulate a stochastic model, parameterize the model with the given data, and perform an appropriate analysis. A technical report should be written as if to be presented to the collaborating industry.

12. Module Features The modules are different from a typical case study in that the problems have not been previously solved and the students are not guided towards any modeling approach. The problems are typically of sufficient complexity to require the use of a computer.

13. Expected Outcomes ► An improved learning environment for the students ► Higher levels of knowledge transfer by the students to “real” industrial problems ► Broad implementation of modules

14. Metrics and Evaluation Tools Expected Outcome 1: Improved Learning Environment ► Metrics -- Quantified measures of the perceived usefulness and enjoyment of the modules by the students ► Evaluation Tools -- Attitudinal survey to be administered by departmental secretary

15. Metrics and Evaluation Tools Cont’d Expected Outcome 2: Knowledge Transfer ► Metrics -- Quantified measures of the students ability to synthesize this material to “real-world” problems ► Evaluation Tools -- In-class case studies to be evaluated holistically by industrial partners and academic evaluators

16. Metrics and Evaluation Tools Cont’d Expected Outcome 3: Implementation of Modules ► Metrics – Both quantitative and qualitative assessments of module usefulness ► Evaluation Tools – Peer-review consisting of comprehensive written evaluations by Dr. Jeff Kharoufeh at AFIT and Dr. John Hassenbein at UT-Austin

17. Opportunities for Participation in Project ► Serve as a formal or informal reviewer of the modules ► Implement modules into your institutions stochastic processes curriculum on a trial basis ► Broaden the base of industrial partners ► Collaborate in the planned follow-on proposal submission in June 2004