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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
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
McNeish, Daniel – Educational and Psychological Measurement, 2017
In behavioral sciences broadly, estimating growth models with Bayesian methods is becoming increasingly common, especially to combat small samples common with longitudinal data. Although Mplus is becoming an increasingly common program for applied research employing Bayesian methods, the limited selection of prior distributions for the elements of…
Descriptors: Models, Bayesian Statistics, Statistical Analysis, Computer Software
Mahmud, Jumailiyah – Educational Research and Reviews, 2017
With the development in computing technology, item response theory (IRT) develops rapidly, and has become a user friendly application in psychometrics world. Limitation in classical theory is one aspect that encourages the use of IRT. In this study, the basic concept of IRT will be discussed. In addition, it will briefly review the ability…
Descriptors: Item Response Theory, Fundamental Concepts, Maximum Likelihood Statistics, Psychometrics
Chen, Yetian; González-Brenes, José P.; Tian, Jin – International Educational Data Mining Society, 2016
Skill prerequisite information is useful for tutoring systems that assess student knowledge or that provide remediation. These systems often encode prerequisites as graphs designed by subject matter experts in a costly and time-consuming process. In this paper, we introduce "Combined student Modeling and prerequisite Discovery"…
Descriptors: Bayesian Statistics, Prerequisites, Graphs, Intelligent Tutoring Systems
Freeman, Suzanne C.; Carpenter, James R. – Research Synthesis Methods, 2017
Network meta-analysis (NMA) combines direct and indirect evidence from trials to calculate and rank treatment estimates. While modelling approaches for continuous and binary outcomes are relatively well developed, less work has been done with time-to-event outcomes. Such outcomes are usually analysed using Cox proportional hazard (PH) models.…
Descriptors: Bayesian Statistics, Network Analysis, Meta Analysis, Data
Doroudi, Shayan; Brunskill, Emma – Grantee Submission, 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, Statistical Analysis, Models
Cook, Joshua; Lynch, Collin F.; Hicks, Andrew G.; Mostafavi, Behrooz – International Educational Data Mining Society, 2017
BKT and other classical student models are designed for binary environments where actions are either correct or incorrect. These models face limitations in open-ended and data-driven environments where actions may be correct but non-ideal or where there may even be degrees of error. In this paper we present BKT-SR and RKT-SR: extensions of the…
Descriptors: Models, Bayesian Statistics, Data Use, Intelligent Tutoring Systems
Huang, Jiajing; Liang, Xinya; Yang, Yanyun – AERA Online Paper Repository, 2017
In Bayesian structural equation modeling (BSEM), prior settings may affect model fit, parameter estimation, and model comparison. This simulation study was to investigate how the priors impact evaluation of relative fit across competing models. The design factors for data generation included sample sizes, factor structures, data distributions, and…
Descriptors: Bayesian Statistics, Structural Equation Models, Goodness of Fit, Sample Size
Fawcett, Lee – Journal of Statistics Education, 2018
In this article we discuss our attempt to incorporate research-informed learning and teaching activities into a final year undergraduate Statistics course. We make use of the Shiny web-based application framework for R to develop "Shiny apps" designed to help facilitate student interaction with methods from recently published papers in…
Descriptors: Undergraduate Students, Foreign Countries, Statistics, Mathematics Instruction
Gardner, Josh; Brooks, Christopher – Journal of Learning Analytics, 2018
Model evaluation -- the process of making inferences about the performance of predictive models -- is a critical component of predictive modelling research in learning analytics. We survey the state of the practice with respect to model evaluation in learning analytics, which overwhelmingly uses only naïve methods for model evaluation or…
Descriptors: Prediction, Models, Evaluation, Evaluation Methods
Luo, Yong; Dimitrov, Dimiter M. – Educational and Psychological Measurement, 2019
Plausible values can be used to either estimate population-level statistics or compute point estimates of latent variables. While it is well known that five plausible values are usually sufficient for accurate estimation of population-level statistics in large-scale surveys, the minimum number of plausible values needed to obtain accurate latent…
Descriptors: Item Response Theory, Monte Carlo Methods, Markov Processes, Outcome Measures
Du, Yu; McMillan, Neil; Madan, Christopher R.; Spetch, Marcia L.; Mou, Weimin – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2017
The authors investigated how humans use multiple landmarks to locate a goal. Participants searched for a hidden goal location along a line between 2 distinct landmarks on a computer screen. On baseline trials, the location of the landmarks and goal varied, but the distance between each of the landmarks and the goal was held constant, with 1…
Descriptors: Cues, Spatial Ability, Memory, Bayesian Statistics
Ames, Allison; Myers, Aaron – Educational Measurement: Issues and Practice, 2019
Drawing valid inferences from modern measurement models is contingent upon a good fit of the data to the model. Violations of model-data fit have numerous consequences, limiting the usefulness and applicability of the model. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model-data fit models…
Descriptors: Bayesian Statistics, Psychometrics, Models, Predictive Measurement
Fox, Jean-Paul; Marianti, Sukaesi – Journal of Educational Measurement, 2017
Response accuracy and response time data can be analyzed with a joint model to measure ability and speed of working, while accounting for relationships between item and person characteristics. In this study, person-fit statistics are proposed for joint models to detect aberrant response accuracy and/or response time patterns. The person-fit tests…
Descriptors: Accuracy, Reaction Time, Statistics, Test Items

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