October 22, 2020, 10 - 12 am EDT
Martha Gardner (GE): "Case Studies for Statistical Engineering"
Case studies have long been useful tools in helping people new to a discipline understand the unique aspects of the discipline and how to best utilize the approaches of the discipline in their daily work. In this talk, I will discuss the importance of case studies in understanding the new discipline of Statistical Engineering and how the International Statistical Engineering Association is compiling and sharing these case studies with the broader community.
Leo C.E. Huberts and Ronald J.M.M. Does (Department of Operations Management, University of Amsterdam, the Netherlands): "Statistical Engineering and Machine Learning: A Case Study to Predict Student Success or Failure"
A quote from a high school principal: “Early Warning Indicator Reports were invaluable to the success of our School”. These reports monitor students throughout their school career and warn teachers and staff of students with high dropout risks. Also, monitoring allows for the identification of students who are insufficiently challenged and will benefit from more stimulating classroom material. Several approaches for monitoring student progress are evaluated to answer three research questions related to this problem:
(1) What determines student performance?(2) How can statistics and machine learning tools be used in monitoring student progress?(3) Which methods can be used for predictive monitoring of student results?
The authors will share how they worked together with a Dutch high school and combined hierarchical Bayesian modeling with statistical and predictive monitoring procedures. And, how machine learning tools (recurrent neural networks) were applied . The final results give a clear blueprint for student progress monitoring.
Geoff Vining (Virginia Tech):"Teaching Students How to Address Complex, Unstructured Problems: Covid-19 Project with Socially Determined"