Parallel Computing with a Bayesian Item Response Model

A Bayesian Nonparametric Model Fit statistic of Item Response Models

Bayesian Estimation and Model Choice in Item Response Models

Item response models are essential tools for analyzing results from many placement tests. Such models are used to quantify the probability of correct response as a function of unobserved examinee ability and other parameters explaining the diiculty and the discriminatory power of the questions in the test. Some of these models also incorporate a threshold parameter for the probability of the co...

A Skew Item Response Model

We introduce a new skew-probit link for item response theory (IRT) by considering an accumulated skew-normal distribution. The model extends the symmetric probit-normal IRT model by considering a new item (or skewness) parameter for the item characteristic curve. A special interpretation is given for this parameter, and a latent linear structure is indicated for the model when an augmented like...

A Bayesian Student Model without Hidden Nodes and its Comparison with Item Response Theory

The Bayesian framework offers a number of techniques for inferring an individual’s knowledge state from evidence of mastery of concepts or skills. A typical application where such a technique can be useful is Computer Adaptive Testing (CAT). A Bayesian modeling scheme, POKS, is proposed and compared to the traditional Item Response Theory (IRT), which has been the prevalent CAT approach for the...

Bayesian Estimation of Item Response Curves

We will consider dichotomous responses to a set of test items which are designed to measure the abilities of individuals. We assume that each item is characterized by an item response curve, a function of ability which is indexed by unknown parameters called item parameters. We consider a Bayesian method for estimating these parameters when the abilities of the individuals are assumed to have a...