Search

Qi I Diao

from Pennington, NJ
Age ~43
Get Report
Also known as:
Diao Qi
Related to:
Q I Diao
Connected to:
David D Diao, 80Ibrahima Diao, 54Jenny R Diao, 26Jie Diao, 48Jun Diao, 42Kevin Diao, 33Mamadou Diao, 43Mi Diao, 51Sophie Diao, 32John Diarbakerly, 72

Qi Diao Phones & Addresses

  • Pennington, NJ
  • Salinas, CA
  • Monterey, CA
  • Lansing, MI
  • Okemos, MI
  • East Lansing, MI
  • Lanagan, MO

Resumes

Resumes

Qi Diao Photo 1

Senior Psychometrician

View page
Location:
10 Cheyenne Dr, Pennington, NJ 08534
Industry:
Education Management
Work:
Educational Testing Service (Ets)
Senior Psychometrician
Act Aug 2017 - Mar 2018
Senior Research Scientist
Pacific Metrics 2015 - Aug 2017
Senior Research Scientist
Ctb Mcgraw Hill 2013 - 2015
Research Scientist Ii
Ctb Mcgraw Hill 2009 - 2013
Research Scientist I
Education:
Michigan State University 2004 - 2009
PhD, Measurement and Quantitative Methods Michigan State University 2005 - 2007
Master, Statistics University of Science and Technology of China 1999 - 2004
BA, English and Science Program
Skills:
Statistical Modeling
Statistics
R
Research Design
Quantitative Research
Data Analysis
Research
Psychometrics
Program Evaluation
Qi Diao Photo 2

Qi Diao

View page
Qi Diao Photo 3

Qi Diao

View page
Location:
Salinas, California Area
Industry:
Research
Education:
Michigan State University 2004 - 2009
PhD, Measurement and Quantitative Methods Michigan State University 2005 - 2007
Master, Statistics University of Science and Technology of China 1999 - 2004
BA, English and Science Program
Skills:
Statistical Modeling
R
Statistics

Publications

Us Patents

Method And Platform For Optimizing Learning And Learning Resource Availability

View page
US Patent:
20130022953, Jan 24, 2013
Filed:
Jul 11, 2012
Appl. No.:
13/546542
Inventors:
Wim J. van der Linden - Monterey CA, US
Qi Diao - Salinas CA, US
Jie Li - Seaside CA, US
Assignee:
CTB/McGraw-Hill, LLC - Monterey CA
International Classification:
G09B 5/00
US Classification:
434322
Abstract:
A platform and method for improving learning within a learning model uses a mathematical optimization algorithm to maximize learning gains through efficient resource allocation that accounts for practical constraints, such as teacher or other resource availability, and probability of success for individual learners on learning nodes given learner profile and resource and instructional configurations. One practical output from this platform and method is a schedule that contains an assignment of learners to learning nodes and teaching resources by learning session over the course of several days.
Control profile
Qi I Diao from Pennington, NJ, age ~43 Get Report