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Cognitive Trait Model: Measurement Model for Mastery Level and Progression of Learning

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  • Jaehwa Choi

    (Department of Educational Leadership, Graduate School of Education and Human Development, The George Washington University, Washington, DC 20010, USA)

Abstract

This paper seeks to establish a framework which operationalizes cognitive traits as a portion of the predefined mastery level, the highest level expected to successfully perform all of the relevant tasks of the target trait. This perspective allows us to use and interpret the cognitive trait levels in relative quantities (e.g., %s) of the mastery level instead of relative standings (i.e., rankings) on an unbounded continuum. To facilitate the proposed perspective, this paper presents an analytical framework that has support on the [0, 1] trait continuum with truncated logistic link functions. The framework provides a solution to cope with the chronic question of “relative standings or magnitudes of learning outcome?” in measuring cognitive traits. The proposed framework is articulated relative to the traditional models and is illustrated with both simulated and empirical datasets within the Bayesian framework, estimated with the Markov chain Monte Carlo method.

Suggested Citation

  • Jaehwa Choi, 2022. "Cognitive Trait Model: Measurement Model for Mastery Level and Progression of Learning," Mathematics, MDPI, vol. 10(15), pages 1-21, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2651-:d:874300
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    References listed on IDEAS

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    2. Jean-Paul Fox & Cees Glas, 2001. "Bayesian estimation of a multilevel IRT model using gibbs sampling," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 271-288, June.
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