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Liu, Ran; Koedinger, Kenneth R. – International Educational Data Mining Society, 2015
A growing body of research suggests that accounting for student specific variability in educational data can improve modeling accuracy and may have implications for individualizing instruction. The Additive Factors Model (AFM), a logistic regression model used to fit educational data and discover/refine skill models of learning, contains a…
Descriptors: Models, Regression (Statistics), Learning, Classification
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Davidson, Lisa; Wilson, Colin – Second Language Research, 2016
Recent research has shown that speakers are sensitive to non-contrastive phonetic detail present in nonnative speech (e.g. Escudero et al. 2012; Wilson et al. 2014). Difficulties in interpreting and implementing unfamiliar phonetic variation can lead nonnative speakers to modify second language forms by vowel epenthesis and other changes. These…
Descriptors: Second Language Learning, Acoustics, Phonetics, Speech
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Williams, Joseph J.; Griffiths, Thomas L. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2013
Errors in detecting randomness are often explained in terms of biases and misconceptions. We propose and provide evidence for an account that characterizes the contribution of the inherent statistical difficulty of the task. Our account is based on a Bayesian statistical analysis, focusing on the fact that a random process is a special case of…
Descriptors: Experimental Psychology, Bias, Misconceptions, Statistical Analysis
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Schochet, Peter Z.; Chiang, Hanley S. – National Center for Education Evaluation and Regional Assistance, 2010
This paper addresses likely error rates for measuring teacher and school performance in the upper elementary grades using value-added models applied to student test score gain data. Using realistic performance measurement system schemes based on hypothesis testing, we develop error rate formulas based on OLS and Empirical Bayes estimators.…
Descriptors: Teacher Effectiveness, Teacher Evaluation, Student Evaluation, Scores
Meyer, Donald – 1969
One of six summaries of workshop sessions (See TM 000 130), designed to strengthen the evaluation of costly programs and their effects, this handbook presents an analysis of both random and nonrandom sampling errors by application of the Bayesian model. This model attempts to formalize the process and procedures of inference from data through…
Descriptors: Bayesian Statistics, Data Collection, Error Patterns, Models
Wilcox, Rand – 1977
False-positive and false-negative dicisions are the fundamental errors committed with a mastery test; yet the estimation of the likelihood of committing these errors has not been investigated. Accordingly, two methods of estimating the likelihood of committing these errors are described and then investigated using Monte Carlo techniques.…
Descriptors: Bayesian Statistics, Computer Programs, Error Patterns, Item Analysis