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Uncovering the Best Skill Multimap by Constraining the Error Probabilities of the Gain-Loss Model

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  • Pasquale Anselmi
  • Egidio Robusto
  • Luca Stefanutti

Abstract

The Gain-Loss model is a probabilistic skill multimap model for assessing learning processes. In practical applications, more than one skill multimap could be plausible, while none corresponds to the true one. The article investigates whether constraining the error probabilities is a way of uncovering the best skill assignment among a number of alternatives. A simulation study shows that this approach allows the detection of the models that are closest to the correct one. An empirical application shows that it allows the detection of models that are entirely derived from plausible assumptions about the skills required for solving the problems. Copyright The Psychometric Society 2012

Suggested Citation

  • Pasquale Anselmi & Egidio Robusto & Luca Stefanutti, 2012. "Uncovering the Best Skill Multimap by Constraining the Error Probabilities of the Gain-Loss Model," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 763-781, October.
    • Handle: RePEc:spr:psycho:v:77:y:2012:i:4:p:763-781
    DOI: 10.1007/s11336-012-9286-0
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    References listed on IDEAS

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    1. Jimmy de la Torre & Jeffrey Douglas, 2008. "Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 595-624, December.
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    Cited by:

    1. Luca Stefanutti & Debora Chiusole & Pasquale Anselmi & Andrea Spoto, 2020. "Extending the Basic Local Independence Model to Polytomous Data," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 684-715, September.
    2. Pasquale Anselmi & Egidio Robusto & Luca Stefanutti & Debora Chiusole, 2016. "An Upgrading Procedure for Adaptive Assessment of Knowledge," Psychometrika, Springer;The Psychometric Society, vol. 81(2), pages 461-482, June.

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