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Lozano, José H.; Revuelta, Javier – Educational and Psychological Measurement, 2023
The present paper introduces a general multidimensional model to measure individual differences in learning within a single administration of a test. Learning is assumed to result from practicing the operations involved in solving the items. The model accounts for the possibility that the ability to learn may manifest differently for correct and…
Descriptors: Bayesian Statistics, Learning Processes, Test Items, Item Analysis
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Hirschfeld, Robert R.; Thomas, Christopher H.; McNatt, D. Brian – Educational and Psychological Measurement, 2008
The authors explored implications of individuals' self-deception (a trait) for their self-reported intrinsic and extrinsic motivational dispositions and their actual learning performance. In doing so, a higher order structural model was developed and tested in which intrinsic and extrinsic motivational dispositions were underlying factors that…
Descriptors: Deception, Predictor Variables, Motivation, Incentives
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Kingma, Johannes; Reuvekamp, Johan – Educational and Psychological Measurement, 1987
This package contains four FORTRAN 77 programs for Markov Model based learning experiments. One program estimates the likelihood of a one-stage learning model, another delivers estimates for a two-stage learning model. The third and fourth were designed for hypothesis testing the parameter differences between and within experimental conditions,…
Descriptors: Computer Software Reviews, Hypothesis Testing, Input Output, Learning Processes
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Kingma, Johannes; Reuvekamp, Johan – Educational and Psychological Measurement, 1987
This paper describes a PASCAL program that computes both different types of transitions and learning statistics suitable for learning experiments in which a two-stage Markov model is used. The frequency counts of the different transitions are used for estimating the parameters of the two-stage Markov model. (Author/LMO)
Descriptors: Computer Software Reviews, Error of Measurement, Goodness of Fit, Input Output