Simulation is an effective tool for analyzing probability models as well as for facilitating understanding of concepts in probability and statistics. Unfortunately, implementing a simulation from scratch often requires users to think about programming issues that are not relevant to the simulation itself. We have developed a Python package called…

This working group addresses the challenge and opportunity presented by the use of simulations of teaching practice as an educative tool for preservice and practicing teachers. We focus particularly on the development of teachers' skills in enacting the content-intensive work of teaching, including how simulations may be able to cultivate or…

The purpose of this study is to explore factors that affect exclusively physical, virtual and both a Virtual-Physical (VP) and Physical-Virtual (PV) sequence of manipulatives within Studio Physics environment. In this environment students actively work together to learn through real world experimentation and simulation based activities. Activities…

The emphases of student involvement and meaningful engagement in the learner-centered education model have created a new paradigm in an effort to generate a more engaging learning environment. This study examines the success of using different simulation platforms in creating a market simulation to teach business processes in the accounting…

Meta-analyses often include only a small number of studies ([less than or equal to]5). Estimating between-study heterogeneity is difficult in this situation. An inaccurate estimation of heterogeneity can result in biased effect estimates and too narrow confidence intervals. The beta-binominal model has shown good statistical properties for…

The California Budget Challenge produced by Next10 provides a useful and intuitive tool for instructors to introduce students to public budgeting. Students will reason through a series of budgeting decisions using information provided on the fiscal and practical implications of their choices. The Challenge is updated with each budget cycle, so it…

This study examined the use of Bayesian analysis methods for the estimation of item parameters in a two-parameter logistic item response theory model. Using simulated data under various design conditions with both informative and non-informative priors, the parameter recovery of Bayesian analysis methods were examined. Overall results showed that…

This paper presents a computational model for simulating how people learn from serious games. While avoiding the combinatorial explosion of a games micro-states, the model offers a meso-level pathfinding approach, which is guided by cognitive flow theory and various concepts from learning sciences. It extends a basic, existing model by exposing…

Statistical inference involves drawing scientifically-based conclusions describing natural processes or observable phenomena from datasets with intrinsic random variation. We designed, implemented, and validated a new portable randomization-based statistical inference infrastructure (http://socr.umich.edu/HTML5/Resampling_Webapp) that blends…

Despite common operationalization, measurement efficiency of computerized adaptive testing should not only be assessed in terms of the number of items administered but also the time it takes to complete the test. To this end, a recent study introduced a novel item selection criterion that maximizes Fisher information per unit of expected response…

Over the past several decades, the use of simulated or standardized clients has been an important area of pedagogy for social work education in training students to work with individuals, families, and groups in practice settings, often in preparation for a practicum placement. Little in the extant literature, however, has focused on the possible…

With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex…

Measurement specialists strive to shorten assessment time without compromising precision of scores. Computerized Adaptive Testing (CAT) has rapidly gained ground over the past decades to fulfill this goal. However, parameters for implementation of CATs need to be explored in simulations before implementation so that it can be determined whether…

Throughout the different phases of a drug development program, randomized trials are used to establish the tolerability, safety and efficacy of a candidate drug. At each stage one aims to optimize the design of future studies by extrapolation from the available evidence at the time. This includes collected trial data and relevant external data.…

Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher has to make and defend.…