1.G. Instructional Materials

Satisfactory rating requires development of instructional or education materials.

Developing new instructional and educational materials is a constant and ongoing aspect of my work with students at JMU.

In addition to routinely preparing exams and lecture materials for my courses, I have also developed several new labs. Representative examples of those labs are attached here. As part of my bootstrapping approach to keeping the labs current as the software and algorithms evolve, I have mentored students as they develop new labs for future students in ISAT 341. I have also been preparing chapters for an introductory textbook on the R Statistical Software Package which I use with my students in GISAT 251. In addition, I have recently been contracted by W. H. Freeman (publishers) to provide course materials in support of their new book to be published in 2014, entitled Statistics in Practice.

GISAT 251 (Statistics) R Tutorials:

  • Bar Charts
  • Segmented Bar Charts
  • Pie Charts
  • Pareto Charts
  • QQ Plots

ISAT 341 (Simulation & Modeling) Labs:

  • Monte Carlo Calculation of Pi
  • Monte Carlo Simulation of the Monty Hall Problem
  • Numerical Weather Prediction Case Study

ISAT 341 (Simulation & Modeling) Labs – Developed by Students with my Guidance:

  • Manual Discrete Event Simulation in Java (Von Wald)
  • Introduction to Monte Carlo Simulation using @RISK (Lucchesi)
  • Monte Carlo Simulation of a Pizza Place (Kingett)
  • Monte Carlo Saving & Loan Simulation (Von Wald)
  • System Dynamics Simulation of Lac Operon (Heydinger)

ISAT 344 (Intelligent Systems)/IES 5005 (Data Mining) Labs:

  • Neural Networks for Regression
  • Multilayer Feedforward Neural Network for Image Classification
  • Text Analysis in R
  • Naive Bayes Classifier for Spam Filtering
  • K-Means Clustering
  • K-Nearest Neighbors (KNN) Classification
  • Performance Measures for Classifiers