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Johnston, Angie M.; Johnson, Samuel G. B.; Koven, Marissa L.; Keil, Frank C. – Developmental Science, 2017
Like scientists, children seek ways to explain causal systems in the world. But are children scientists in the strict Bayesian tradition of maximizing posterior probability? Or do they attend to other explanatory considerations, as laypeople and scientists--such as Einstein--do? Four experiments support the latter possibility. In particular, we…
Descriptors: Young Children, Thinking Skills, Inferences, Bayesian Statistics
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Doroudi, Shayan; Brunskill, Emma – International Educational Data Mining Society, 2017
In this paper, we investigate two purported problems with Bayesian Knowledge Tracing (BKT), a popular statistical model of student learning: "identifiability" and "semantic model degeneracy." In 2007, Beck and Chang stated that BKT is susceptible to an "identifiability problem"--various models with different…
Descriptors: Bayesian Statistics, Research Problems, Models, Learning
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Kaplan, David – Large-scale Assessments in Education, 2016
This paper reviews recent research on causal inference with large-scale assessments in education from a Bayesian perspective. I begin by adopting the potential outcomes model of Rubin ("J Educ Psychol" 66:688-701, 1974) as a framework for causal inference that I argue is appropriate with large-scale educational assessments. I then…
Descriptors: Attribution Theory, Inferences, Bayesian Statistics, Educational Assessment
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Frermann, Lea; Lapata, Mirella – Cognitive Science, 2016
Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words (e.g., "chair" is a member of the furniture category). We present a Bayesian model that, unlike…
Descriptors: Classification, Bayesian Statistics, Models, Cognitive Science
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Fernández-López, María; Marcet, Ana; Perea, Manuel – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2019
In past decades, researchers have conducted a myriad of masked priming lexical decision experiments aimed at unveiling the early processes underlying lexical access. A relatively overlooked question is whether a masked unrelated wordlike/unwordlike prime influences the processing of the target stimuli. If participants apply to the primes the same…
Descriptors: Priming, Decision Making, Language Processing, Bayesian Statistics
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Man, Kaiwen; Harring, Jeffrey R. – Educational and Psychological Measurement, 2019
With the development of technology-enhanced learning platforms, eye-tracking biometric indicators can be recorded simultaneously with students item responses. In the current study, visual fixation, an essential eye-tracking indicator, is modeled to reflect the degree of test engagement when a test taker solves a set of test questions. Three…
Descriptors: Test Items, Eye Movements, Models, Regression (Statistics)
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Bolin, Jocelyn H.; Finch, W. Holmes; Stenger, Rachel – Educational and Psychological Measurement, 2019
Multilevel data are a reality for many disciplines. Currently, although multiple options exist for the treatment of multilevel data, most disciplines strictly adhere to one method for multilevel data regardless of the specific research design circumstances. The purpose of this Monte Carlo simulation study is to compare several methods for the…
Descriptors: Hierarchical Linear Modeling, Computation, Statistical Analysis, Maximum Likelihood Statistics
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Howard, Emma; Meehan, Maria; Parnell, Andrew – Assessment & Evaluation in Higher Education, 2019
In "Maths for Business," a large first-year mathematics module, the continuous assessment component comprises 10 weekly quizzes which combine to contribute 40% of the final module mark. If students did not receive the full five marks on their weekly quiz, they were provided with the opportunity to resubmit their corrected weekly quiz…
Descriptors: Remedial Instruction, College Mathematics, College Freshmen, Undergraduate Students
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Crisp, Gloria; Doran, Erin; Salis Reyes, Nicole A. – Research in Higher Education, 2018
This study models graduation rates at 4-year broad access institutions (BAIs). We examine the student body, structural-demographic, and financial characteristics that best predict 6-year graduation rates across two time periods (2008-2009 and 2014-2015). A Bayesian model averaging approach is utilized to account for uncertainty in variable…
Descriptors: Graduation Rate, Predictor Variables, Student Characteristics, Institutional Characteristics
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Ames, Allison J.; Au, Chi Hang – Measurement: Interdisciplinary Research and Perspectives, 2018
Stan is a flexible probabilistic programming language providing full Bayesian inference through Hamiltonian Monte Carlo algorithms. The benefits of Hamiltonian Monte Carlo include improved efficiency and faster inference, when compared to other MCMC software implementations. Users can interface with Stan through a variety of computing…
Descriptors: Item Response Theory, Computer Software Evaluation, Computer Software, Programming Languages
Siebrase, Benjamin – ProQuest LLC, 2018
Multilayer perceptron neural networks, Gaussian naive Bayes, and logistic regression classifiers were compared when used to make early predictions regarding one-year college student persistence. Two iterations of each model were built, utilizing a grid search process within 10-fold cross-validation in order to tune model parameters for optimal…
Descriptors: Classification, College Students, Academic Persistence, Bayesian Statistics
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Enders, Craig K.; Keller, Brian T.; Levy, Roy – Grantee Submission, 2018
Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables,…
Descriptors: Hierarchical Linear Modeling, Behavioral Science Research, Computer Software, Bayesian Statistics
Suh, Youngsuk; Cho, Sun-Joo; Bottge, Brian A. – Grantee Submission, 2018
This article presents a multilevel longitudinal nested logit model for analyzing correct response and error types in multilevel longitudinal intervention data collected under a pretest-posttest, cluster randomized trial design. The use of the model is illustrated with a real data analysis, including a model comparison study regarding model…
Descriptors: Hierarchical Linear Modeling, Longitudinal Studies, Error Patterns, Change
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Gershman, Samuel J.; Pouncy, Hillard Thomas; Gweon, Hyowon – Cognitive Science, 2017
We routinely observe others' choices and use them to guide our own. Whose choices influence us more, and why? Prior work has focused on the effect of perceived similarity between two individuals (self and others), such as the degree of overlap in past choices or explicitly recognizable group affiliations. In the real world, however, any dyadic…
Descriptors: Social Influences, Social Cognition, Inferences, Models
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Choi, In-Hee; Paek, Insu; Cho, Sun-Joo – Journal of Experimental Education, 2017
The purpose of the current study is to examine the performance of four information criteria (Akaike's information criterion [AIC], corrected AIC [AICC] Bayesian information criterion [BIC], sample-size adjusted BIC [SABIC]) for detecting the correct number of latent classes in the mixture Rasch model through simulations. The simulation study…
Descriptors: Item Response Theory, Models, Bayesian Statistics, Simulation
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