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On the Link between Cognitive Diagnostic Models and Knowledge Space Theory

Author

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  • Jürgen Heller
  • Luca Stefanutti
  • Pasquale Anselmi
  • Egidio Robusto

Abstract

The present work explores the connections between cognitive diagnostic models (CDM) and knowledge space theory (KST) and shows that these two quite distinct approaches overlap. It is proved that in fact the Multiple Strategy DINA (Deterministic Input Noisy AND-gate) model and the CBLIM, a competence-based extension of the basic local independence model (BLIM), are equivalent. To demonstrate the benefits that arise from integrating the two theoretical perspectives, it is shown that a fairly complete picture on the identifiability of these models emerges by combining results from both camps. The impact of the results is illustrated by an empirical example, and topics for further research are pointed out. Copyright The Psychometric Society 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:psycho:v:80:y:2015:i:4:p:995-1019
    DOI: 10.1007/s11336-015-9457-x
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    References listed on IDEAS

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    1. Jimmy de la Torre, 2011. "The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 179-199, April.
    2. Jimmy Torre & Jeffrey Douglas, 2004. "Higher-order latent trait models for cognitive diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 69(3), pages 333-353, September.
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    4. 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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    6. Curtis Tatsuoka & Ferenc Varadi & Judith Jaeger, 2013. "Latent Partially Ordered Classification Models and Normal Mixtures," Journal of Educational and Behavioral Statistics, , vol. 38(3), pages 267-294, June.
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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. Stefano Noventa & Andrea Spoto & Jürgen Heller & Augustin Kelava, 2019. "On a Generalization of Local Independence in Item Response Theory Based on Knowledge Space Theory," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 395-421, June.
    3. Youn Seon Lim, 2023. "Book Review," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 1092-1095, September.
    4. 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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