Empirical Bayes's (EB) estimation has become a popular procedure used to calculate teacher value added, often as a way to make imprecise estimates more reliable. In this article, we review the theory of EB estimation and use simulated and real student achievement data to study the ability of EB estimators to properly rank teachers. We compare the…

Bayesian Knowledge Tracing (BKT) has been in wide use for modeling student skill acquisition in Intelligent Tutoring Systems (ITS). BKT tracks and updates student's latent mastery of a skill as a probability distribution of a binary variable. BKT does so by accounting for observed student successes in applying the skill correctly, where success is…

Students with attentional difficulties are at greater risk for reading difficulties. To address this concern, we examined the extent to which adding a mindful breathing exercise to individual reading fluency interventions would improve gains in reading fluency, student-reported attention, and student-reported stress. In a restricted alternating…

We used the Survey of Attitudes Toward Statistics to (1) evaluate using presemester data the Students' Attitudes Toward Statistics Model (SATS-M), and (2) test the effect on attitudes of an introductory statistics course redesigned according to the Guidelines for Assessment and Instruction in Statistics Education (GAISE) by examining the change in…

Many American universities located in the Middle East try to offer the stamp of higher quality in education that the United States provides and delivers. These institutions are doing an incredible job of providing opportunity for youths of that region to obtain an American education. However, these universities bear the stereotype that they are…

This study reports on the national standardization and validation of the Learning Behaviors Scale (LBS) for use in Trinidad and Tobago. The LBS is a teacher rating scale centering on observable behaviors relevant to identifying childhood approaches to classroom learning. Teachers observed a stratified sample of 900 students across the islands'…

A new variant of the iterative "data = fit + residual" data-analytical approach described by Mosteller and Tukey is proposed and implemented in the context of item response theory psychometric models. Posterior probabilities from a Bayesian mixture model of a Rasch item response theory model and an unscalable latent class are expressed…

Approaches to correcting correlation coefficients for range restriction have been developed under the framework of large sample theory. The accuracy of missing data techniques for correcting correlation coefficients for range restriction has thus far only been investigated with relatively large samples. However, researchers and evaluators are…

The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…

When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two-parameter sensitivity analysis…

Analyzing the rate at which languages change can clarify whether similarities across languages are solely the result of cognitive biases or might be partially due to descent from a common ancestor. To demonstrate this approach, we use a simple model of language evolution to mathematically determine how long it should take for the distribution over…

Empirical Bayes' (EB) estimation is a widely used procedure to calculate teacher value-added. It is primarily viewed as a way to make imprecise estimates more reliable. In this paper we review the theory of EB estimation and use simulated data to study its ability to properly rank teachers. We compare the performance of EB estimators with that of…

The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's "cognitive plausibility." We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition…

In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall…

Our visual brain is remarkable in extracting invariant properties from the noisy environment, guiding selection of where to look and what to identify. However, how the brain achieves this is still poorly understood. Here we explore interactions of local context and global structure in the long-term learning and retrieval of invariant display…