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

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)57-87
Number of pages31
JournalJournal of Educational and Behavioral Statistics
Volume43
Issue number1
DOIs
StatePublished - Feb 1 2018

Keywords

  • Markov chain Monte Carlo
  • cognitive diagnostic models
  • hidden Markov model
  • higher order
  • longitudinal
  • skill change
  • spatial cognition

ASJC Scopus subject areas

  • Education
  • Social Sciences (miscellaneous)

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