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Assessing Growth in a Diagnostic Classification Model Framework.

Matthew J Madison1, Laine P Bradshaw2

  • 1Department of Education and Human Development, Clemson University, 226 Holtzendorff Hall, Clemson, SC, 29634, USA. mjmadis@clemson.edu.

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Summary
This summary is machine-generated.

This study introduces the Transition Diagnostic Classification Model (TDCM) to measure changes in attribute mastery over time. The TDCM offers accurate, reliable growth analysis for educational assessments.

Keywords:
cognitive diagnosis modeldiagnostic classification modelgrowthitem parameter driftlatent transition analysismeasurement invariancepre-test/post-test design

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Cognitive Science

Background:

  • Single-group pre-test/post-test designs are common for measuring examinee growth.
  • Longitudinal Item Response Theory (IRT) models assess growth in examinee ability.
  • Diagnostic Classification Models (DCMs) interpret growth as changes in attribute mastery.

Purpose of the Study:

  • Introduce the Transition Diagnostic Classification Model (TDCM).
  • Provide a methodology for analyzing growth within the DCM framework.
  • Analyze pre-test/post-test data from a diagnostic mathematics assessment.

Main Methods:

  • Combined latent transition analysis with the log-linear cognitive diagnosis model.
  • Developed a novel methodology for growth analysis in DCM.
  • Utilized simulation studies to evaluate model performance.

Main Results:

  • The TDCM demonstrated flexibility and provided accurate, reliable classifications.
  • The model proved robust to violations of measurement invariance over time.
  • Successfully applied the TDCM to analyze diagnostic mathematics assessment data.

Conclusions:

  • The TDCM offers a robust and accurate method for analyzing growth in attribute mastery.
  • This model enhances criterion-referenced interpretations of examinee development.
  • The TDCM is a valuable tool for educational researchers and practitioners.