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Satake, Eiki; Vashlishan Murray, Amy – Teaching Statistics: An International Journal for Teachers, 2015
This paper presents a comparison of three approaches to the teaching of probability to demonstrate how the truth table of elementary mathematical logic can be used to teach the calculations of conditional probabilities. Students are typically introduced to the topic of conditional probabilities--especially the ones that involve Bayes' rule--with…
Descriptors: Teaching Methods, Probability, Bayesian Statistics, Mathematical Logic
Chow, Alan F.; Van Haneghan, James P. – Educational Studies in Mathematics, 2016
This study reports the results of a study examining how easily students are able to transfer frequency solutions to conditional probability problems to novel situations. University students studied either a problem solved using the traditional Bayes formula format or using a natural frequency (tree diagram) format. In addition, the example problem…
Descriptors: Probability, College Students, Mathematical Formulas, Bayesian Statistics
Goodman, Noah D.; Tenenbaum, Joshua B.; Feldman, Jacob; Griffiths, Thomas L. – Cognitive Science, 2008
This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space--a concept language of logical rules. This article compares the model predictions to human generalization judgments in several…
Descriptors: Mathematics Education, Concept Formation, Models, Prediction
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

Laird, Nan M.; Louis, Thomas A. – Journal of Educational Statistics, 1989
Based on the Gaussian model, methods for using measurements that depend on the true attribute to compute rankings are proposed and compared. Measurements based on an empirical Bayes model produce estimates that differ from ranking observed data. Ranking methods are illustrated with school achievement data. (TJH)
Descriptors: Bayesian Statistics, Class Rank, Mathematical Formulas, Mathematical Models

Guttman, Irwin; Olkin, Ingram – Journal of Educational Statistics, 1989
A model for student retention and attrition is presented. Focus is on alternative models for the "dampening" in attrition rates as educational programs progress. Maximum likelihood estimates for the underlying parameters in each model and a Bayesian analysis are provided. (TJH)
Descriptors: Bayesian Statistics, Grade Repetition, Mathematical Formulas, Mathematical Models