Integrating Application Based Modules into the Stochastic Processes Curriculum

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Integrating ApplicationBased Modules into the
Stochastic Processes Curriculum
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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

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Personnel

• Principal Investigator
• Timothy I. Matis
• tmatis_at_nmsu.edu
• Co-Principal Investigator
• Linda Ann Riley
• linriley_at_nmsu.edu

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Industrial Partners

• Fort Bliss Federal Credit Union
• Sandia National Labs
• Ethicon Johnson Johnson
• Celestica

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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

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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

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Shortcomings of Traditional Instruction
TechniquesB. 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

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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.

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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).

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DVD Contents

• Viewable DVD Files
• Problem Description
• Data Description
• Credits
• DVD-ROM
• Raw Data Files
• Supporting Documents
• Student Resources (sample Mathematica programs)

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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.

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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.

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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

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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

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Metrics and Evaluation Tools Contd

• 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

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Metrics and Evaluation Tools Contd

• 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

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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