Considers how the order of documents in information retrieval responses are determined and introduces a method that uses a probabilistic model of a document set where documents are regarded as states of a Markov chain and where transition probabilities are directly proportional to similarities between documents. (Author/LRW)

In the No Child Left Behind Act (NCLB), Congress called for the U.S. Department of Education (ED) to conduct a rigorous study of the conditions and practices under which educational technology is effective in increasing student academic achievement. A 2007 report presenting study findings for the 2004-2005 school year, indicated that, after one…

One of the most extensively investigated topics in the adult memory literature, dual memory processes, has had virtually no impact on the study of early memory development. The authors remove the key obstacles to such research by formulating a trichotomous theory of recall that combines the traditional dual processes of recollection and…

This article discusses the potential of Google Scholar as an alternative or complement to the Web of Science and Scopus for measuring the impact of journal articles in education. Three handbooks on research in science education, language education, and educational technology were used to identify a sample of 112 accomplished scholars. Google…

In an undergraduate course on stochastic processes, Markov chains are discussed in great detail. Textbooks on stochastic processes provide interesting properties of finite Markov chains. This note discusses one such property regarding the number of steps in which a state is reachable or accessible from another state in a finite Markov chain with M…

In current psychological research, the analysis of data from computer-based assessments or experiments is often confined to accuracy scores. Response times, although being an important source of additional information, are either neglected or analyzed separately. In this article, a new model is developed that allows the simultaneous analysis of…

Several approaches to reporting subscale scores can be found in the literature. This research explores a multidimensional compensatory dichotomous and polytomous item response theory modeling approach for subscale score proficiency estimation, leading toward a more diagnostic solution. It also develops and explores the recovery of a Markov chain…

Studied the conditions under which the quasi-Markov simplex model fits a linear growth curve covariance structure and determined when the model is rejected. Presents a quasi-Markov simplex model with structured means and gives an example. (SLD)

Hidden Markov models (HMMs) are widely used in bioinformatics, speech recognition and many other areas. This note presents HMMs via the framework of classical Markov chain models. A simple example is given to illustrate the model. An estimation method for the transition probabilities of the hidden states is also discussed.

For complex educational assessments, there is an increasing use of item families, which are groups of related items. Calibration or scoring in an assessment involving item families requires models that can take into account the dependence structure inherent among the items that belong to the same item family. This article extends earlier works in…

A Markov chain Monte Carlo (MCMC) method and a bootstrap method were compared in the estimation of standard errors of item response theory (IRT) true score equating. Three test form relationships were examined: parallel, tau-equivalent, and congeneric. Data were simulated based on Reading Comprehension and Vocabulary tests of the Iowa Tests of…

Recent work has shown that multidimensionally scoring responses from different tests can provide better ability estimates. For educational assessment data, applications of this approach have been limited to binary scores. Of the different variants, the de la Torre and Patz model is considered more general because implementing the scoring procedure…

Compared the quality of item parameter estimates for marginal maximum likelihood (MML) and Markov Chain Monte Carlo (MCMC) with the nominal response model using simulation. The quality of item parameter recovery was nearly identical for MML and MCMC, and both methods tended to produce good estimates. (SLD)

Examines the ways in which level-1 outliers can impact the estimation of fixed effects and random effects in hierarchical models (HMs). Also outlines and illustrates the use of Markov Chain Monte Carlo algorithms for conducting sensitivity analyses under "t" level-1 assumptions, including algorithms for settings in which the degrees of…

Investigated the item-parameter recovery characteristics of a Gibbs sampling method (J. Albert, 1992) for item-response theory item-parameter estimation and compared them to those from the BILOG computer program. Item-parameter recoveries were similar for both approaches for larger data sets, but overall, BILOG performance was superior. (SLD)