October 1-2, 2018 • ucla luskin conference center

Terry Vendlinski

Principal at TVATE Consulting

 

Dr. Vendlinski is currently Principal at TVATE Consulting. He received his Ph.D. from the Massachusetts Institute of Technology where his research concerned education technology and assessment. He did his graduate studies in education at the Harvard Graduate School of Education, and was a distinguished graduate of the M.B.A program at St. Mary’s University in San Antonio, Texas. Dr. Vendlinski received his Bachelor of Science degree from the United States Air Force Academy.

Terry Vendlinski’s extensive teaching experience includes teaching C++ and Java programming as both an adjunct professor (California State University, Sacramento) and as a head teaching Assistant (MIT); teaching statistics, finite math, and algebra at the community college level; teaching chemistry, math and computer programming at the secondary level; and teaching 8th grade algebra. He has also gained practical policy experience while assisting the Office of Educational Technology at the U.S. Department of Education with their efforts to fully fund the initial round of the federal E-rate program and as a member of that department’s program to help schools wisely invest State Fiscal Stabilization Funds in educational technology. In his roles as Co-director, Assessment Research and Design in the Center for Technology in Learning at SRI, International and as Senior Researcher at the National Center for Research on Evaluation, Standards and Student Testing, he has authored papers on using Evidence Centered Design to improve inferential validity in technology (including games) based assessment and many technology-based, national, large scale assessments. He has also authored papers on the use of lag sequential analysis and artificial neural networks to help evaluate web-based student problem solving performances and to help evaluate the validity of inferences from various assessments of student learning. Other publications detail his methodologies to integrate neural network analysis with Markov and Logistic models of student ability and learning, as well as an innovative text and software to integrate pre-Algebra with World History. During the last several years, his research has principally concentrated on developing frameworks to improve assessments of and pedagogy to advance student understanding in Math, Science and English Language Arts.