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Skaggs, Gary; Wilkins, Jesse L. M.; Hein, Serge F. – International Journal of Testing, 2016
The purpose of this study was to explore the degree of grain size of the attributes and the sample sizes that can support accurate parameter recovery with the General Diagnostic Model (GDM) for a large-scale international assessment. In this resampling study, bootstrap samples were obtained from the 2003 Grade 8 TIMSS in Mathematics at varying…
Descriptors: Achievement Tests, Foreign Countries, Elementary Secondary Education, Science Achievement
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Khajah, Mohammad; Lindsey, Robert V.; Mozer, Michael C. – International Educational Data Mining Society, 2016
In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better.…
Descriptors: Bayesian Statistics, Data Analysis, Prediction, Intelligent Tutoring Systems
Gonzalez-Brenes, Jose P.; Mostow, Jack – International Educational Data Mining Society, 2012
This work describes a unified approach to two problems previously addressed separately in Intelligent Tutoring Systems: (i) Cognitive Modeling, which factorizes problem solving steps into the latent set of skills required to perform them; and (ii) Student Modeling, which infers students' learning by observing student performance. The practical…
Descriptors: Intelligent Tutoring Systems, Academic Achievement, Bayesian Statistics, Tutors
Xu, Yanbo; Mostow, Jack – International Educational Data Mining Society, 2012
A long-standing challenge for knowledge tracing is how to update estimates of multiple subskills that underlie a single observable step. We characterize approaches to this problem by how they model knowledge tracing, fit its parameters, predict performance, and update subskill estimates. Previous methods allocated blame or credit among subskills…
Descriptors: Teaching Methods, Comparative Analysis, Prediction, Mathematics
Rindskopf, David; Shadish, William; Hedges, Larry V. – Online Submission, 2012
This conference presentation demonstrates a multilevel model for analyzing single case designs. The model is implemented in the Bayesian program WinBUGS. The authors show how it is possible to estimate a d-statistic like the one in Hedges, Pustejovsky and Shadish (2012) in this program. Results are demonstrated on an example.
Descriptors: Effect Size, Computation, Hierarchical Linear Modeling, Research Design
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Huang, David Y. C.; Lanza, H. Isabella; Murphy, Debra A.; Hser, Yih-Ing – International Journal of Behavioral Development, 2012
This study used data from 5,382 adolescents from the 1997 United States (US) National Longitudinal Survey of Youth (NLSY97) to investigate developmental pathways of alcohol use, marijuana use, sexual risk behaviors, and delinquency across ages 14 to 20; examine interrelationships among these risk behaviors across adolescence; and evaluate…
Descriptors: Adolescents, Drinking, Depression (Psychology), Marijuana
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White, Corey N.; Brown, Scott; Ratcliff, Roger – Journal of Experimental Psychology: Human Perception and Performance, 2012
Two Bayesian observer models were recently proposed to account for data from the Eriksen flanker task, in which flanking items interfere with processing of a central target. One model assumes that interference stems from a perceptual bias to process nearby items as if they are compatible, and the other assumes that the interference is due to…
Descriptors: Tests, Bayesian Statistics, Cognitive Processes, Task Analysis
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Taylor, Eric G.; Ahn, Woo-kyoung – Cognitive Psychology, 2012
Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C…
Descriptors: Evidence, Prior Learning, Bayesian Statistics, Correlation
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Zhu, Xiaowen; Stone, Clement A. – Educational and Psychological Measurement, 2012
This study examined the relative effectiveness of Bayesian model comparison methods in selecting an appropriate graded response (GR) model for performance assessment applications. Three popular methods were considered: deviance information criterion (DIC), conditional predictive ordinate (CPO), and posterior predictive model checking (PPMC). Using…
Descriptors: Bayesian Statistics, Item Response Theory, Comparative Analysis, Models
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Grasshoff, Ulrike; Holling, Heinz; Schwabe, Rainer – Psychometrika, 2012
In this paper, optimal designs will be derived for estimating the ability parameters of the Rasch model when difficulty parameters are known. It is well established that a design is locally D-optimal if the ability and difficulty coincide. But locally optimal designs require that the ability parameters to be estimated are known. To attenuate this…
Descriptors: Item Response Theory, Test Items, Psychometrics, Statistical Analysis
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Gopnik, Alison; Wellman, Henry M. – Psychological Bulletin, 2012
We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…
Descriptors: Causal Models, Theory of Mind, Probability, Cognitive Development
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Jo, Booil; Stuart, Elizabeth A. – Journal of Research on Educational Effectiveness, 2012
The authors thank Dr. Lindsay Page for providing a nice illustration of the use of the principal stratification framework to define causal effects, and a Bayesian model for effect estimation. They hope that her well-written article will help expose education researchers to these concepts and methods, and move the field of mediation analysis in…
Descriptors: Bayesian Statistics, Educational Experiments, Educational Research, Observation
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Anobile, Giovanni; Cicchini, Guido Marco; Burr, David C. – Cognition, 2012
Mapping of number onto space is fundamental to mathematics and measurement. Previous research suggests that while typical adults with mathematical schooling map numbers veridically onto a linear scale, pre-school children and adults without formal mathematics training, as well as individuals with dyscalculia, show strong compressive,…
Descriptors: Reading Achievement, Numbers, Bayesian Statistics, Preschool Children
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Scherer, Ronny; Meßinger-Koppelt, Jenny; Tiemann, Rüdiger – International Journal of STEM Education, 2014
Background: Complex problem-solving competence is regarded as a key construct in science education. But due to the necessity of using interactive and intransparent assessment procedures, appropriate measures of the construct are rare. This paper consequently presents the development and validation of a computer-based problem-solving environment,…
Descriptors: Computer Assisted Testing, Problem Solving, Chemistry, Science Tests
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Johnson, Timothy R. – Applied Psychological Measurement, 2013
One of the distinctions between classical test theory and item response theory is that the former focuses on sum scores and their relationship to true scores, whereas the latter concerns item responses and their relationship to latent scores. Although item response theory is often viewed as the richer of the two theories, sum scores are still…
Descriptors: Item Response Theory, Scores, Computation, Bayesian Statistics
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