Journal article
Combining Item Response Theory and Diagnostic Classification Models: A Psychometric Model for Scaling Ability and Diagnosing Misconceptions
Psychometrika, Vol.79(3), pp.403-425
07/01/2014
DOI: 10.1007/s11336-013-9350-4
PMID: 25205005
Abstract
Traditional testing procedures typically utilize unidimensional item response theory (IRT) models to provide a single, continuous estimate of a student's overall ability. Advances in psychometrics have focused on measuring multiple dimensions of ability to provide more detailed feedback for students, teachers, and other stakeholders. Diagnostic classification models (DCMs) provide multidimensional feedback by using categorical latent variables that represent distinct skills underlying a test that students may or may not have mastered. The Scaling Individuals and Classifying Misconceptions (SICM) model is presented as a combination of a unidimensional IRT model and a DCM where the categorical latent variables represent misconceptions instead of skills. In addition to an estimate of ability along a latent continuum, the SICM model provides multidimensional, diagnostic feedback in the form of statistical estimates of probabilities that students have certain misconceptions. Through an empirical data analysis, we show how this additional feedback can be used by stakeholders to tailor instruction for students' needs. We also provide results from a simulation study that demonstrate that the SICM MCMC estimation algorithm yields reasonably accurate estimates under large-scale testing conditions.
Details
- Title: Subtitle
- Combining Item Response Theory and Diagnostic Classification Models: A Psychometric Model for Scaling Ability and Diagnosing Misconceptions
- Creators
- Laine Bradshaw - University of GeorgiaJonathan Templin - Department of Educational Psychology, The University of Georgia, Athens, USA
- Resource Type
- Journal article
- Publication Details
- Psychometrika, Vol.79(3), pp.403-425
- Publisher
- Springer Nature
- DOI
- 10.1007/s11336-013-9350-4
- PMID
- 25205005
- ISSN
- 0033-3123
- eISSN
- 1860-0980
- Number of pages
- 23
- Grant note
- DRL-0822064; SES-0750859; SES-1030337 / National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 07/01/2014
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984371266302771
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