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Carly Oddleifson; Stephen Kilgus; David A. Klingbeil; Alexander D. Latham; Jessica S. Kim; Ishan N. Vengurlekar – Grantee Submission, 2025
The purpose of this study was to conduct a conceptual replication of Pendergast et al.'s (2018) study that examined the diagnostic accuracy of a nomogram procedure, also known as a naive Bayesian approach. The specific naive Bayesian approach combined academic and social-emotional and behavioral (SEB) screening data to predict student performance…
Descriptors: Bayesian Statistics, Accuracy, Social Emotional Learning, Diagnostic Tests
Lang, Charles William McLeod – ProQuest LLC, 2015
Personalization, the idea that teaching can be tailored to each students' needs, has been a goal for the educational enterprise for at least 2,500 years (Regian, Shute, & Shute, 2013, p.2). Recently personalization has picked up speed with the advent of mobile computing, the Internet and increases in computer processing power. These changes…
Descriptors: Individualized Instruction, Electronic Learning, Mathematics, Bayesian Statistics
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Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2015
Person-fit assessment may help the researcher to obtain additional information regarding the answering behavior of persons. Although several researchers examined person fit, there is a lack of research on person-fit assessment for mixed-format tests. In this article, the lz statistic and the ?2 statistic, both of which have been used for tests…
Descriptors: Test Format, Goodness of Fit, Item Response Theory, Bayesian Statistics
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
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Zwick, Rebecca; Lenaburg, Lubella – Journal of Educational and Behavioral Statistics, 2009
In certain data analyses (e.g., multiple discriminant analysis and multinomial log-linear modeling), classification decisions are made based on the estimated posterior probabilities that individuals belong to each of several distinct categories. In the Bayesian network literature, this type of classification is often accomplished by assigning…
Descriptors: Classification, Bayesian Statistics, Network Analysis, Probability
Powers, James E. – 1981
The use of Bayesian methodology to assign grades in classroom situations is presented. Assigning a grade is viewed from a criterion, as opposed to norm, referenced perspective. Criteria include mastery of some proportion, determined by the teacher, of the subject matter covered in a course. Different levels of mastery are deemed possible and,…
Descriptors: Academic Achievement, Bayesian Statistics, Grading, Mathematical Formulas
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Smith, Jeffrey K. – Educational and Psychological Measurement, 1980
Weber contends that the use of Rasch analysis, principal components analysis, and classical test analysis shows that an instrument designed to measure a "bilevel dimensionality" in probability achievement measures a single latent trait. That interpretation and the use of Rasch and classical analysis to establish unidimensionality are…
Descriptors: Academic Achievement, Bayesian Statistics, Cognitive Processes, Item Analysis
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Raudenbush, Stephen W.; Bryk, Anthony S. – Journal of Educational Statistics, 1987
Statistical methods are presented for studying "correlates of diversity," defined as characteristics of educational organizations that predict dispersion on the dependent variable. Strategies based on exact distribution theory and asymptotic normal approximation are considered. (TJH)
Descriptors: Academic Achievement, Bayesian Statistics, Estimation (Mathematics), Mathematics Achievement
Lunneborg, Clifford E. – 1971
A Bayesian prediction strategy is outlined in which antecedent measures are divided into two subgroups. One subgroup is used to discriminate among criterion groups, the second to provide normal linear predictions for each group. Individualized regression constants are subsequently obtained by computing probabilities of group membership from the…
Descriptors: Academic Achievement, Achievement Tests, Aptitude Tests, Bayesian Statistics