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Gordon Ford College of Business Staff


John Erickson, PhD
John Erickson, PhD
- Assistant Professor - Analytics and Information Systems
Teaching Area(s)

Introduction to Analytics

Data Visualization

Education

PhD, Data Science, Worcester Polytechnic Institute, 2021

MS, Predictive Analytics, Northwestern University, 2017

BS, Economics, Specialization in Actuarial Science, Michigan State University, 2014

Biography

Dr. John Erickson is an Assistant Professor of Information Systems in the College of Business. Dr. Erickson received a BS in Economics with a specialization in Actuarial Science from Michigan State University, a MS in Predictive Analytics from Northwestern University and a PhD in Data Science from Worcester Polytechnic Institute. His research focuses on natural language processing and predictive modeling within intelligent systems. This work has expanded to utilizing modern machine and deep learning to explore potential bias within predictive models across multiple domains. Additionally, his work focuses on bridging the gap between structured problems, such as multiple choice or fill in the blank, and open response problems in mathematics on intelligent tutoring systems by developing predictive models to automatically grade student open responses and suggest responses to teachers. John and his wife Jessica, a dietitian, are excited to start the next chapter of their life in Bowling Green and at Western Kentucky University. They have a tabby cat, named Abbey, and are in the process of adopting a chocolate lab puppy. Both John and Jessica are avid Green Bay Packers fans. They both love hiking, golfing, and outdoor activities.

Recent Publications

"Blending Learning Analytics and Embodied Design to Model Students’ Comprehension of Measurement Using their Actions, Speech, and Gestures" (with A Harrison, H. Smith, A. Varatharaj, and A.F. Botelho), International Journal of Child-Computer Interaction Special Issue on Learning Analytics of Embodied Design: Enhancing Synergy, 2021.

"Precise Unbiased Estimation in Randomized Experiments Using Auxiliary Observational Data" (with J.A. Gagnon-Bartsch, A.C. Sales, E. Wu, A.F. Botelho, L.W. Miratrix, and N.T. Heffernan), arXiv, 2021.

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 Last Modified 3/29/22