Results 21 to 25 of about 782,349 (25)

Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory [PDF]

arXiv, 2019
Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have long been criticized for not being explainable. In this paper, we propose Deep-IRT which is a synthesis of the item response theory (IRT) model and a ...
arxiv  

Comparing item response theory models for ranking incorrect response options [PDF]

arXiv
Previous work has shown that item response theory may be used to rank incorrect response options to multiple-choice items on commonly used assessments. This work has shown that, when the correct response to each item is specified, a nominal response model (NRM) may be used to rank the incorrect options.
arxiv  

Three- and four-parameter item response model in factor analysis framework [PDF]

arXiv
This work proposes a 4-parameter factor analytic (4P FA) model for multi-item measurements composed of binary items as an extension to the dichotomized single latent variable FA model. We provide an analytical derivation of the relationship between the newly proposed 4P FA model and its counterpart in the item response theory (IRT) framework, the 4P ...
arxiv  

Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory [PDF]

arXiv
Analyses of heterogeneous treatment effects (HTE) are common in applied causal inference research. However, when outcomes are latent variables assessed via psychometric instruments such as educational tests, standard methods ignore the potential HTE that may exist among the individual items of the outcome measure.
arxiv  

AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning [PDF]

arXiv
Item response theory (IRT) is a class of interpretable factor models that are widely used in computerized adaptive tests (CATs), such as language proficiency tests. Traditionally, these are fit using parametric mixed effects models on the probability of a test taker getting the correct answer to a test item (i.e., question).
arxiv