Uncovering the best skill multimap by constraining the error probabilities of the gain-loss model

DOI10.1007/s11336-012-9286-0zbMath1284.62680OpenAlexW2065780033MaRDI QIDQ692417

Egidio Robusto, Pasquale Anselmi, Luca Stefanutti

Publication date: 5 December 2012

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-012-9286-0

zbMATH Keywords

learning processknowledge space theoryknowledge structureconstrained parameter estimationgain-loss modelskill multimap

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (8)

An upgrading procedure for adaptive assessment of knowledge ⋮ On the polytomous generalization of knowledge space theory ⋮ On the assessment of learning in competence based knowledge space theory ⋮ Extending the basic local independence model to polytomous data ⋮ Modeling learning in knowledge space theory through bivariate Markov processes ⋮ Markov solution processes: modeling human problem solving with procedural knowledge space theory ⋮ Uncovering the best skill multimap by constraining the error probabilities of the gain-loss model ⋮ Constructing, improving, and shortening tests for skill assessment

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• How to assess a model's testability and identifiability
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• Knowledge Spaces
• A class of stochastic procedures for the assessment of knowledge

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