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An Upgrading Procedure for Adaptive Assessment of Knowledge

Author

Listed:
  • Pasquale Anselmi

    (University of Padua)

  • Egidio Robusto

    (University of Padua)

  • Luca Stefanutti

    (University of Padua)

  • Debora Chiusole

    (University of Padua)

Abstract

In knowledge space theory, existing adaptive assessment procedures can only be applied when suitable estimates of their parameters are available. In this paper, an iterative procedure is proposed, which upgrades its parameters with the increasing number of assessments. The first assessments are run using parameter values that favor accuracy over efficiency. Subsequent assessments are run using new parameter values estimated on the incomplete response patterns from previous assessments. Parameter estimation is carried out through a new probabilistic model for missing-at-random data. Two simulation studies show that, with the increasing number of assessments, the performance of the proposed procedure approaches that of gold standards.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:psycho:v:81:y:2016:i:2:d:10.1007_s11336-016-9498-9
    DOI: 10.1007/s11336-016-9498-9
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    References listed on IDEAS

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    8. Jürgen Heller & Luca Stefanutti & Pasquale Anselmi & Egidio Robusto, 2015. "On the Link between Cognitive Diagnostic Models and Knowledge Space Theory," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 995-1019, December.
    9. Jürgen Heller & Luca Stefanutti & Pasquale Anselmi & Egidio Robusto, 2016. "Erratum to: On the Link between Cognitive Diagnostic Models and Knowledge Space Theory," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 250-251, March.
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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.

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