Jeffrey A Douglas

About

Jeffrey A Douglas is a professor associated with the University of Illinois at Urbana-Champaign. His research focuses on various aspects of item response theory, cognitive diagnosis, and educational measurement. His work includes developing models for joint analysis of quality of life and survival, nonparametric monotone item response models, and methods for improving the accuracy of item response theory parameter estimates through simultaneous estimation and the incorporation of ancillary variables. He has contributed to the implementation of optimal design for item calibration in computerized adaptive testing and has explored topics in nonlinear time series, statistical issues in time series analysis, and educational measurement. His research also supports cognitive learning and adaptive testing, emphasizing the development of statistical models and methods to enhance assessment accuracy and efficiency.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Statistics
• Mathematics
• Econometrics
• Mathematical optimization
• Algorithm
• Engineering

Selected publications

• Bayesian Estimation of the DINA Q matrix

  Psychometrika · 2017-08-31 · 119 citations

  articleSenior author

  Cognitive diagnosis models are partially ordered latent class models and are used to classify students into skill mastery profiles. The deterministic inputs, noisy "and" gate model (DINA) is a popular psychometric model for cognitive diagnosis. Application of the DINA model requires content expert knowledge of a Q matrix, which maps the attributes or skills needed to master a collection of items. Misspecification of Q has been shown to yield biased diagnostic classifications. We propose a Bayesi…

  PublisherDOI

• Tracking Skill Acquisition With Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model With Covariates

  Journal of Educational and Behavioral Statistics · 2017-07-26 · 115 citations

  articleSenior author

  A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are applied to a computer-based assessment with a learning intervention. The results show the potential application of the proposed model to track the change of…

  PublisherDOI

• A Hidden Markov Model for Learning Trajectories in Cognitive Diagnosis With Application to Spatial Rotation Skills

  Applied Psychological Measurement · 2017-09-05 · 79 citations

  articleOpen accessSenior authorCorresponding

  The increasing presence of electronic and online learning resources presents challenges and opportunities for psychometric techniques that can assist in the measurement of abilities and even hasten their mastery. Cognitive diagnosis models (CDMs) are ideal for tracking many fine-grained skills that comprise a domain, and can assist in carefully navigating through the training and assessment of these skills in e-learning applications. A class of CDMs for modeling changes in attributes is proposed…

  PublisherOA PDFDOI

• Utilizing Response Time Distributions for Item Selection in CAT

  Journal of Educational and Behavioral Statistics · 2012-01-13 · 72 citations

  articleSenior author

  Traditional methods for item selection in computerized adaptive testing only focus on item information without taking into consideration the time required to answer an item. As a result, some examinees may receive a set of items that take a very long time to finish, and information is not accrued as efficiently as possible. The authors propose two item-selection criteria that utilize information from a lognormal model for response times. The first modifies the maximum information criterion to ma…

  PublisherDOI

• A Semiparametric Model for Jointly Analyzing Response Times and Accuracy in Computerized Testing

  Journal of Educational and Behavioral Statistics · 2012-11-16 · 64 citations

  articleSenior author

  The item response times (RTs) collected from computerized testing represent an underutilized type of information about items and examinees. In addition to knowing the examinees’ responses to each item, we can investigate the amount of time examinees spend on each item. Current models for RTs mainly focus on parametric models, which have the advantage of conciseness, but may suffer from reduced flexibility to fit real data. We propose a semiparametric approach, specifically, the Cox proportional…

  PublisherDOI

Recent grants

• NIH Grant R01CA081068

  NIH · $229k · 2003

• Collaborative Research: Constrained Finite Mixture Models for Psychological Diagnosis and Educational Assessment

  NSF · $84k · 2007–2010

Frequent coauthors

• William Stout

  University of Illinois Urbana-Champaign

  22 shared

• Louis A. Roussos

  Cognizant (United States)

  21 shared

• Ursula A. Matulonis

  16 shared

• Robert M. Wenham

  16 shared

• William P. McGuire

  VCU Massey Comprehensive Cancer Center

  16 shared

• Howard M. Mackey

  16 shared

• Deborah K. Armstrong

  16 shared

• Stephen A. Cannistra

  16 shared

Awards & honors

• Associate Editor, Statistics and Its Interface (2007-)
• Associate Editor, Psychometrika (2003-)
• Co-director of Educational and Psychological Measurement Lab

Similar researchers at University of Illinois Urbana-Champaign

• Dr. Sabyasachi Chatterjeeh-70
• Ruoqi Yuh-61
• Trevor H Parkh-61