Search Results for author:
Found 5 papers, 2 papers with code
Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory
Our results show that the IL-HTE model reveals item-level variation masked by average treatment effects, provides more accurate statistical inference, allows for estimates of the generalizability of causal effects, resolves identification problems in the estimation of interaction effects, and provides estimates of standardized treatment effect sizes corrected for attenuation due to measurement error.
Modeling Item Response Theory with Stochastic Variational Inference
Item Response Theory (IRT) is a ubiquitous model for understanding human behaviors and attitudes based on their responses to questions.
Variational Item Response Theory: Fast, Accurate, and Expressive
Item Response Theory (IRT) is a ubiquitous model for understanding humans based on their responses to questions, used in fields as diverse as education, medicine and psychology.
AI and Holistic Review: Informing Human Reading in College Admissions
College admissions in the United States is carried out by a human-centered method of evaluation known as holistic review, which typically involves reading original narrative essays submitted by each applicant.
Curve Fitting from Probabilistic Emissions and Applications to Dynamic Item Response Theory
However, in contemporary settings where item responses are being continuously collected, such as Massive Open Online Courses (MOOCs), interest will naturally be on the dynamics of ability, thus complicating usage of traditional IRT models.
