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Kjorte Harra; David Kaplan – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The present work focuses on the performance of two types of shrinkage priors--the horseshoe prior and the recently developed regularized horseshoe prior--in the context of inducing sparsity in path analysis and growth curve models. Prior research has shown that these horseshoe priors induce sparsity by at least as much as the "gold…
Descriptors: Structural Equation Models, Bayesian Statistics, Regression (Statistics), Statistical Inference
Joo, Seang-Hwane; Lee, Philseok – Journal of Educational Measurement, 2022
Abstract This study proposes a new Bayesian differential item functioning (DIF) detection method using posterior predictive model checking (PPMC). Item fit measures including infit, outfit, observed score distribution (OSD), and Q1 were considered as discrepancy statistics for the PPMC DIF methods. The performance of the PPMC DIF method was…
Descriptors: Test Items, Bayesian Statistics, Monte Carlo Methods, Prediction
Gongchang, Yueban; Wang, Yibing – AERA Online Paper Repository, 2020
Location tracking devices are becoming increasingly popular in practice to study movement of customers or track inventory. However, using location tracking devices in education contexts is quite novel. In this paper, we present a robust Bayesian nonparametric mixture model that clusters location data. We successfully apply this model on location…
Descriptors: Bayesian Statistics, Nonparametric Statistics, Multivariate Analysis, Interaction
Hinterecker, Thomas; Knauff, Markus; Johnson-Laird, P. N. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2019
Individuals draw conclusions about possibilities from assertions that make no explicit reference to them. The model theory postulates that assertions such as disjunctions refer to possibilities. Hence, a disjunction of the sort, "A or B or both," where "A" and "B" are sensible clauses, yields mental models of an…
Descriptors: Logical Thinking, Abstract Reasoning, Inferences, Probability
Hsu, Anne S.; Horng, Andy; Griffiths, Thomas L.; Chater, Nick – Cognitive Science, 2017
Identifying patterns in the world requires noticing not only unusual occurrences, but also unusual absences. We examined how people learn from absences, manipulating the extent to which an absence is expected. People can make two types of inferences from the absence of an event: either the event is possible but has not yet occurred, or the event…
Descriptors: Statistical Inference, Bayesian Statistics, Evidence, Prediction
Peralta, Montserrat; Alarcon, Rosa; Pichara, Karim E.; Mery, Tomas; Cano, Felipe; Bozo, Jorge – IEEE Transactions on Learning Technologies, 2018
Educational resources can be easily found on the Web. Most search engines base their algorithms on a resource's text or popularity, requiring teachers to navigate the results until they find an appropriate resource. This makes searching for resources a tedious and cumbersome task. Specialized repositories contain resources that are annotated with…
Descriptors: Educational Resources, Metadata, Foreign Countries, Bayesian Statistics
Nguyen, Huy; Liew, Chun Wai – International Educational Data Mining Society, 2018
Recent works on Intelligent Tutoring Systems have focused on more complicated knowledge domains, which pose challenges in automated assessment of student performance. In particular, while the system can log every user action and keep track of the student's solution state, it is unable to determine the hidden intermediate steps leading to such…
Descriptors: Bayesian Statistics, Intelligent Tutoring Systems, Data Analysis, Error Patterns
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
Pittayachawan, Siddhi; Macauley, Peter; Evans, Terry – Journal of Higher Education Policy and Management, 2016
This article reports how statistical analyses of PhD thesis records can reveal future research capacities for disciplines beyond their primary fields. The previous research showed that most theses contributed to and/or used methodologies from more than one discipline. In Australia, there was a concern for declining mathematical teaching and…
Descriptors: Databases, Doctoral Dissertations, Foreign Countries, Statistical Analysis
Lee, Michael D.; Pooley, James P. – Psychological Review, 2013
The scale-invariant memory, perception, and learning (SIMPLE) model developed by Brown, Neath, and Chater (2007) formalizes the theoretical idea that scale invariance is an important organizing principle across numerous cognitive domains and has made an influential contribution to the literature dealing with modeling human memory. In the context…
Descriptors: Recall (Psychology), Memory, Models, Equations (Mathematics)
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
Griffiths, Thomas L.; Tenenbaum, Joshua B. – Journal of Experimental Psychology: General, 2011
Predicting the future is a basic problem that people have to solve every day and a component of planning, decision making, memory, and causal reasoning. In this article, we present 5 experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This Bayesian model indicates how people should…
Descriptors: Bayesian Statistics, Statistical Inference, Models, Prior Learning
Goldin, Ilya M.; Koedinger, Kenneth R.; Aleven, Vincent – International Educational Data Mining Society, 2012
Although ITSs are supposed to adapt to differences among learners, so far, little attention has been paid to how they might adapt to differences in how students learn from help. When students study with an Intelligent Tutoring System, they may receive multiple types of help, but may not comprehend and make use of this help in the same way. To…
Descriptors: Performance Factors, Intelligent Tutoring Systems, Individual Differences, Prediction
Maguire, Angela M.; Humphreys, Michael S.; Dennis, Simon; Lee, Michael D. – Journal of Memory and Language, 2010
This paper addresses two Global Matching predictions in embedded-category designs: the within-category choice advantage in forced-choice recognition (superior discrimination for test choices comprising a same-category distractor); and the category length effect in forced-choice and old/new recognition (a loss in discriminability with increases in…
Descriptors: Bayesian Statistics, Models, Prediction, Classification
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