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Tracking Skill Acquisition With Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model With Covariates

Author

Listed:
  • Shiyu Wang

    (University of Georgia)

  • Yan Yang
  • Steven Andrew Culpepper
  • Jeffrey A. Douglas

    (University of Illinois at Urbana-Champaign)

Abstract

A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are applied to a computer-based assessment with a learning intervention. The results show the potential application of the proposed model to track the change of students’ skills directly and provide immediate remediation as well as to evaluate the efficacy of different interventions by investigating how different types of learning interventions impact the transitions from nonmastery to mastery.

Suggested Citation

  • Shiyu Wang & Yan Yang & Steven Andrew Culpepper & Jeffrey A. Douglas, 2018. "Tracking Skill Acquisition With Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model With Covariates," Journal of Educational and Behavioral Statistics, , vol. 43(1), pages 57-87, February.
    • Handle: RePEc:sae:jedbes:v:43:y:2018:i:1:p:57-87
    DOI: 10.3102/1076998617719727
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    References listed on IDEAS

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    Cited by:

    1. Matthew J. Madison & Laine P. Bradshaw, 2018. "Assessing Growth in a Diagnostic Classification Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 963-990, December.

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