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A Diagnostic Tree Model for Adaptive Assessment of Complex Cognitive Processes Using Multidimensional Response Options

Author

Listed:
  • Mark L. Davison
  • David J. Weiss

    (University of Minnesota)

  • Joseph N. DeWeese

    (University of Minnesota)

  • Ozge Ersan

    (Turkish Ministry of National Education)

  • Gina Biancarosa
  • Patrick C. Kennedy

    (University of Oregon)

Abstract

A tree model for diagnostic educational testing is described along with Monte Carlo simulations designed to evaluate measurement accuracy based on the model. The model is implemented in an assessment of inferential reading comprehension, the Multiple-Choice Online Causal Comprehension Assessment (MOCCA), through a sequential, multidimensional, computerized adaptive testing (CAT) strategy. Assessment of the first dimension, reading comprehension (RC), is based on the three-parameter logistic model. For diagnostic and intervention purposes, the second dimension, called process propensity (PP), is used to classify struggling students based on their pattern of incorrect responses. In the simulation studies, CAT item selection rules and stopping rules were varied to evaluate their effect on measurement accuracy along dimension RC and classification accuracy along dimension PP. For dimension RC, methods that improved accuracy tended to increase test length. For dimension PP, however, item selection and stopping rules increased classification accuracy without materially increasing test length. A small live-testing pilot study confirmed some of the findings of the simulation studies. Development of the assessment has been guided by psychometric theory, Monte Carlo simulation results, and a theory of instruction and diagnosis.

Suggested Citation

  • Mark L. Davison & David J. Weiss & Joseph N. DeWeese & Ozge Ersan & Gina Biancarosa & Patrick C. Kennedy, 2023. "A Diagnostic Tree Model for Adaptive Assessment of Complex Cognitive Processes Using Multidimensional Response Options," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 914-941, December.
    • Handle: RePEc:sae:jedbes:v:48:y:2023:i:6:p:914-941
    DOI: 10.3102/10769986231158301
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    References listed on IDEAS

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    1. Laine Bradshaw & Jonathan Templin, 2014. "Combining Item Response Theory and Diagnostic Classification Models: A Psychometric Model for Scaling Ability and Diagnosing Misconceptions," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 403-425, July.
    2. De Boeck, Paul & Partchev, Ivailo, 2012. "IRTrees: Tree-Based Item Response Models of the GLMM Family," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(c01).
    3. Timothy R. Johnson & Daniel M. Bolt, 2010. "On the Use of Factor-Analytic Multinomial Logit Item Response Models to Account for Individual Differences in Response Style," Journal of Educational and Behavioral Statistics, , vol. 35(1), pages 92-114, February.
    4. Thomas Warm, 1989. "Weighted likelihood estimation of ability in item response theory," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 427-450, September.
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