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Stewart, Wayne; Stewart, Sepideh – PRIMUS, 2014
For many scientists, researchers and students Markov chain Monte Carlo (MCMC) simulation is an important and necessary tool to perform Bayesian analyses. The simulation is often presented as a mathematical algorithm and then translated into an appropriate computer program. However, this can result in overlooking the fundamental and deeper…
Descriptors: Markov Processes, Monte Carlo Methods, College Mathematics, Mathematics Instruction
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Huang, Hung-Yu; Wang, Wen-Chung – Educational and Psychological Measurement, 2013
Both testlet design and hierarchical latent traits are fairly common in educational and psychological measurements. This study aimed to develop a new class of higher order testlet response models that consider both local item dependence within testlets and a hierarchy of latent traits. Due to high dimensionality, the authors adopted the Bayesian…
Descriptors: Item Response Theory, Models, Bayesian Statistics, Computation
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Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction
Bosker, R. J.; Guldemond, H. – 1994
In previous studies of educational systems, Markov chains have often been used in simulations in which history played no role because all events were described as independent of each other. Mono-level dynamics (i.e., the social context in which learning occurs is ignored) have also been applied to simulations. Educational theories, however, are…
Descriptors: Academic Achievement, Core Curriculum, Educational Environment, Educational Policy