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Greeno, James G.; And Others – Psychometrika, 1971
Earlier Analyses are shown to involve special cases of the equations developed here; also that a general four-state chain has the same parameter space as an all-or-none model if and only if its representation with an observable absorbing state is lumpable into a Markov chain with three states. (Author/GS)
Descriptors: Learning Theories, Mathematical Models, Probability, Research Methodology

Groen, Guy J. – Psychometrika, 1971
The problem of whether a precise connection exists between the stochastic processes considered in mathematical learning theory and the Guttman simplex is investigated. The approach used is to derive a set of conditions which a probabilistic model must satisfy in order to generate inter-trial correlations with the perfect simplex property.…
Descriptors: Correlation, Learning Theories, Mathematical Models, Probability

Verhelst, N. D.; Glas, C. A. W. – Psychometrika, 1993
A model for describing dynamic processes is constructed by combining the Rasch model with the concept of structurally incomplete designs. This is accomplished by mapping each item on a collection of virtual items, one of which is assumed to be presented to the respondent depending on preceding responses or feedback. (SLD)
Descriptors: Equations (Mathematics), Feedback, Generalization, Learning Theories

Falmagne, Jean-Claude – Psychometrika, 1989
A stochastic version of a knowledge space is developed in which knowledge states are considered as possible epochs in a subject's learning history. It specifies how a subject is channeled through and progresses along a "gradation." The model's application to artificial data is described, based on maximum likelihood methods. (TJH)
Descriptors: Equations (Mathematics), Knowledge Level, Latent Trait Theory, Learning Processes