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Beauducel, André; Hilger, Norbert – Educational and Psychological Measurement, 2022
In the context of Bayesian factor analysis, it is possible to compute plausible values, which might be used as covariates or predictors or to provide individual scores for the Bayesian latent variables. Previous simulation studies ascertained the validity of mean plausible values by the mean squared difference of the mean plausible values and the…
Descriptors: Bayesian Statistics, Factor Analysis, Prediction, Simulation
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Masaki Eguchi – Vocabulary Learning and Instruction, 2022
Building on previous studies investigating the multidimensional nature of lexical use in task-based L2 performance, this study clarified the roles that the distinct lexical features play in predicting vocabulary proficiency in a corpus of L2 Oral Proficiency Interviews (OPI). A total of 85 OPI samples were rated by three separate raters based on a…
Descriptors: Lexicology, Oral Language, Language Proficiency, Vocabulary Development
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Bozpolat, Ebru – Educational Sciences: Theory and Practice, 2016
The purpose of this study was to reveal whether the low, medium, and high level self-regulated learning strategies of third year students at the Education Faculty of Cumhuriyet University can be predicted by the variables of gender, academic self-efficacy, and general academic average. The study uses the Relational Screening Model. The dependent…
Descriptors: Learning Strategies, Self Efficacy, Foreign Countries, Gender Differences
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Muthen, Bengt; Asparouhov, Tihomir – Psychological Methods, 2012
This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed…
Descriptors: Factor Analysis, Cognitive Ability, Science Achievement, Structural Equation Models
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Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers