2010, odyssée des modèles de classification diagnostique (MCD)

Loye, Nathalie

doi:https://doi.org/10.7202/1024892ar

Browse the articles in this issue

Évolution des notes scolaires entre le collège et le lycée : interprétations et enjeux

Élaboration et validation de l’Échelle de motivation à bien réussir un test (ÉMRT)

Article outline

Abstract
Full text (PDF) Full text (PDF)
Références

Toolbox

Save Delete
PDFDownload
Cite this article
Share

Abstracts

Résumé

Cet article vise à définir les modèles de classification diagnostique (MCD) et à déterminer leur place relativement à d’autres modélisations existantes comme la TRI. Les modèles RSM, DINA et NC-RUM sont exposés plus en détail. Pour terminer, une analyse critique débouche sur des pistes de recherches théoriques et empiriques.

Mots-clés :

Modèles de classification diagnostique,
diagnostic,
modèles à classes latentes

Abstract

This paper gives a definition of diagnostic classification models (DCM) and purport to compare these models to models that may be more familiar like IRT. The RSM, DINA and NC-RUM models are examined in more depth. This paper is including a critical analysis and many theoretical and empirical research avenues.

Keywords:

Diagnostic classification models,
diagnostic,
latent class models

Resumo

Este artigo pretende definir os modelos de classificação diagnóstica (MCD) e determinar o seu lugar relativamente a outras modelizações existentes, como é o caso da TRI. Os modelos RSM, DINA e NC-RUM são apresentados mais em detalhe. O artigo termina com uma análise crítica que aponta pistas para investigações teóricas e empíricas.

Palavras chaves:

Modelos de classificação diagnóstica,
disgnóstico,
modelo de classes latentes

Download the article in PDF to read it. Download

Appendices

Références

Almond, R. (2007). Modeling diagnostic assessments with Bayesian networks. Journal of Educational Measurement, 44(4), 341-359.
10.1111/j.1745-3984.2007.00043.x Google Scholar Search this bibliographic reference on Google Scholar
Birenbaum, M., Kelly, A. E., & Tatsuoka, K. K. (1993). Diagnosing knowledge states in algebra using the rule-space model. Journal for Research in Mathematics Education, 24(5), 442-459.
10.2307/749153 Google Scholar Search this bibliographic reference on Google Scholar
Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: an application of the EM algorithm. Psychometnka, 46, 443-449.
10.1007/BF02293801 Google Scholar Search this bibliographic reference on Google Scholar
Bolt, D. (2007). The present and the future of IRT-based cognitive models (ICDMs) and related methods. Journal of Educational Measurement, 44(4), 377-383.
10.1111/j.1745-3984.2007.00045.x Google Scholar Search this bibliographic reference on Google Scholar
Buck, G., & Tatsuoka, K. K. (1998). Application of the rule space procedure to language testing: examining attributes of a free response listening test. Language Testing, 15(2), 119-157.
10.1111/j.1745-3984.2008.00069.x Google Scholar Search this bibliographic reference on Google Scholar
Buck, G., Tatsuoka, K. K., & Kostin, I. (1997). The subskills of reading: Rule-space analysis of a multiple choice test of second language reading comprehension. Language Testing, 47(3), 423-466.
10.3102/1076998607309474 Google Scholar Search this bibliographic reference on Google Scholar
de la Torre, J. (2008). An empirically-based method of Q-matrix validation for the DINA model: Development and applications. Journal of Educational Measurement, 45(4), 343-362.
10.1111/j.1745-3984.2008.00069.x Google Scholar Search this bibliographic reference on Google Scholar
de la Torre, J. (2009). DINA model and parameter estimation: A didactic. Journal of Educational and Behavioral Statistics, 34(1), 115-130.
10.3102/1076998607309474 Google Scholar Search this bibliographic reference on Google Scholar
de la Torre, J., & Douglas, J. A. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69(3), 333-353.
10.1007/BF02295640 Google Scholar Search this bibliographic reference on Google Scholar
de la Torre, J., & Douglas, J. A. (2005, april). Modeling multiple strategies in cognitive diagnosis. Article présenté au congrès annuel du National Council on Measurement in Education (NCME), Montréal, QC.
10.1111/j.1745-3984.2007.00042.x Google Scholar Search this bibliographic reference on Google Scholar
DiBello, L. V., Roussos, L., & Stout, W. (2007). Review of cognitively diagnostic assessment and a summary of psychometric models. In C. R. Rao & S. Sinharay (dir.), Handbook of Statistics (vol. 26, pp. 979-1030). Amsterdam: Elsevier.
10.1177/014662168400800311 Google Scholar Search this bibliographic reference on Google Scholar
DiBello, L. V., & Stout, W. (2007). Guest editors’ introduction and overview: IRT-based cognitive diagnostic models and related methods. Journal of Educational Measurement, 44(4), 285-291.
10.1111/j.1745-3984.2007.00039.x Google Scholar Search this bibliographic reference on Google Scholar
DiBello, L. V., Stout, W. F., & Roussos, L. A. (1995). Unified cognitive/psychometric diagnostic assessment likeklihood-based classification techniques. In P. D. Nichols, S. F. Chipman & R. L. Brennan (dir.), Cognitively diagnostic assessment (pp. 361-389). Hillsdale, NJ: Erlbaum.
10.1111/j.1745-3984.2007.00044.x Google Scholar Search this bibliographic reference on Google Scholar
Modèles de classification diagnostique 95
10.1007/s11336-008-9089-5 Google Scholar Search this bibliographic reference on Google Scholar
Dogan, E., & Tatsuoka, K. (2008). An international comparison using a diagnostic testing model: Turkish students’ profile of mathematical skills on TIMSS-R. Educational Studies in Mathematics, 68(3), 263-272.
10.1007/s10649-007-9099-8 Google Scholar Search this bibliographic reference on Google Scholar
Doornik, J. A. (2002). Object-oriented matrix programming using Ox (version 3.1) [Logiciel]. London: Timberlake Consultats Press.
10.1177/01466210122032064 Google Scholar Search this bibliographic reference on Google Scholar
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, N.J.: Lawrence Erlbaum Associates.
10.1017/CBO9780511611186 Google Scholar Search this bibliographic reference on Google Scholar
Frey, A., & Carstensen, C. H. (2009). Diagnostic classification models and multidimensional adaptive testing: A commentary on Rupp and Templin. Measurement: Interdisciplinary Research & Perspective, 7(1), 58-61.
10.1111/j.1745-3984.2004.tb01163.x Google Scholar Search this bibliographic reference on Google Scholar
Gierl, M. (2007). Making diagnostic inferences about cognitive attributes using the Rule- Space Model and Attribute Hierarchy Method. Journal of Educational Measurement, 44(4), 325-340.
10.1111/j.1745-3984.2007.00042.x Google Scholar Search this bibliographic reference on Google Scholar
Gierl, M. (2008). Defining characteristics of diagnostic classification models and the problem of retrofitting in cognitive diagnostic assessment. Measurement: Inter-disciplinary Research & Perspective, 6(4), 263-268.
10.1007/BF02294535 Google Scholar Search this bibliographic reference on Google Scholar
Gitomer, D. H., & Rock, D. (1993). Adressing process variables in test analysis. In N. Fredericksen, R. J. Mislevy & I. I. Bejar (dir.), Test theory for a new generation of tests (pp. 125-150). Hillsdale, NJ: Erlbaum.
10.1111/j.1745-3984.2007.00040.x Google Scholar Search this bibliographic reference on Google Scholar
Gorin, J. S. (2009). Diagnostic classification models: Are they necessary? Commentary on Rupp and Templin (2008). Measurement: Interdisciplinary Research & Perspective, 7(1), 30-33.
10.1080/15366360802715486 Google Scholar Search this bibliographic reference on Google Scholar
Haberman, S. J., & von Davier, M. (2007). Some notes on models for cognitively based skills diagnosis. In C. R. Rao & S. Sinharay (dir.), Handbook of Statistics (vol. 26, pp. 1031-1039). Amsterdam: Elsevier.
10.1111/j.1745-3984.2007.00041.x Google Scholar Search this bibliographic reference on Google Scholar
Haertel, E. H. (1984). An application of latent class models to assessment data. Applied Psychological Measurement, 8, 333-346.
10.1177/014662168400800311 Google Scholar Search this bibliographic reference on Google Scholar
Haertel, E. H. (1990). Continuous and discrete latent class structure models of item response data. Psychometnka, 55, 477-494.
10.1007/BF02294762 Google Scholar Search this bibliographic reference on Google Scholar
Hancock, G. R. (2009). Diagnostic classification modeling: opportunity for identity. Measurement: Interdisciplinary Research & Perspective, 7(1), 62-64.
10.3102/00028312041004901 Google Scholar Search this bibliographic reference on Google Scholar
Hartz, S. M. (2002). A Bayesian framework for the unified model for assessing cognitive abilities: Blending theory with practicality. Dissertation doctorale non publié, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL.
10.1037/1082-989X.11.3.287 Google Scholar Search this bibliographic reference on Google Scholar
Henson, R. (2009). Diagnostic classification models: throughts future directions. Measurement: Interdisciplinary Research & Perspective, 7(1), 34-36.
10.1007/BF02293610 Google Scholar Search this bibliographic reference on Google Scholar
Henson, R., Templin, J., & Douglas, J. (2007). Using efficient model based sum-scores for conducting skills diagnoses. Journal of Educational Measurement, 44(4), 361-376.
10.1111/j.1745-3984.2007.00044.x Google Scholar Search this bibliographic reference on Google Scholar
Henson, R., Templin, J., & Willse, J. (2009). Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika, 74(2), 191-210.
10.1007/s11336-008-9089-5 Google Scholar Search this bibliographic reference on Google Scholar
Jang, E. E. (2005). Avalidity narrative: Effects of reading skills diagnosis on teaching and learning in the context of NG TOEFL. Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL.
Google Scholar Search this bibliographic reference on Google Scholar
Jiao, H. (2009). Diagnostic classification models: Which one should I use? Measurement: Interdisciplinary Research & Perspective, 7(1), 65-67.
10.1080/15366360902799869 Google Scholar Search this bibliographic reference on Google Scholar
Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25, 258-272.
10.1177/01466210122032064 Google Scholar Search this bibliographic reference on Google Scholar
Karelitz, T. (2008). How binary skills obscure the transition from non-mastery to mastery. Measurement: Interdisciplinary Research & Perspective, 6(4), 268-272.
Google Scholar Search this bibliographic reference on Google Scholar
Leighton, J. (2008). Where’s the psychology? A commentary on unique characteristics of diagnostic classification models: A comprehensive review of the current state-of- the-art. Measurement: Interdisciplinary Research & Perspective, 6(4), 272-275.
Google Scholar Search this bibliographic reference on Google Scholar
Leighton, J. P., & Gierl, M. J. (2007). Cognitive diagnostic assessment for education: Theory and applications. Cambridge: Cambridge University Press.
10.1017/CBO9780511611186 Google Scholar Search this bibliographic reference on Google Scholar
Leighton, J. P., Gierl, M. J., & Hunka, S. M. (2004). The attribute hierarchy method for cognitive assessment: A variation on Tatsuoka’s rule-space approach. Journal of Educational Measurement, 41, 205-237.
10.1111/j.1745-3984.2004.tb01163.x Google Scholar Search this bibliographic reference on Google Scholar
Levy, R. (2009). Evidentiary reasonning in diagnostic classification models. Measurement: Interdisciplinary Research & Perspective, 7(1), 36-41.
10.1080/15366360802497762 Google Scholar Search this bibliographic reference on Google Scholar
Loye, N. (2005). Quelques modèles de mesure. Mesure et évaluation en éducation, 28(3), 51-68.
Google Scholar Search this bibliographic reference on Google Scholar
Loye, N. (2008). Conditions d'élaboration de la Matrice Q des modèles cognitifs et impact sur sa validité et sa fidélité. Thèse de doctorat non publiée, Université d’Ottawa, Ottawa.
10.1080/15366360802715387 Google Scholar Search this bibliographic reference on Google Scholar
Loye, N. (2009). Les modèles cognitifs. In J.-G. Blais (dir.), Évaluation des apprentissages et technologies de l'information et de la communication: Enjeux, applications et modèles de mesure. Québec: PUL.
10.1016/S0169-7161(06)26040-1 Google Scholar Search this bibliographic reference on Google Scholar
Loye, N., Caron, F., Pineault, J., Tessier-Baillargeon, M., Burney-Vincent, C., & Gagnon, M. (sous presse). La validité du diagnostic issu d’un mariage entre didactique et mesure sur un test existant. In G. Raîche, K. Paquette-Côté & D. Magis (dir.), Des mécanismes pour assurer la validité de l'interprétation de la mesure en éducation (vol. 1). Sainte- Foy, Québec: Presses de l’Université du Québec.
10.1080/15366360902799836 Google Scholar Search this bibliographic reference on Google Scholar
Macready, G. B., & Dayton, C. M. (1977). The use of probabilistic models in the assessment of mastery. Journal of Educational Statistics, 2, 99-120.
10.2307/1164802 Google Scholar Search this bibliographic reference on Google Scholar
Maris, E. (1999). Estimating multiple classification latent class models. Psychometrika, 64, 187-212.
10.1007/BF02294535 Google Scholar Search this bibliographic reference on Google Scholar
Maris, G., & Bechger, T. (2009). Equivalent diagnostic classification models. Measurement: Interdisciplinary Research & Perspective, 7(1), 41-46.
Google Scholar Search this bibliographic reference on Google Scholar
Nichols, P. D., Chipman, S. F., & Brennan, R. L. (1995). Cognitively diagnostic assessment. Hillsdale, NJ: Erlbaum.
10.1080/15366360802502322 Google Scholar Search this bibliographic reference on Google Scholar
Rao, C. R., & Sinharay, S. (dir.). (2007). Handbook of statistics (vol. 26). Amsterdam: Elsevier.
10.1080/15366360802497804 Google Scholar Search this bibliographic reference on Google Scholar
Roussos, L., Templin, J., & Henson, R. (2007). Skills diagnosis using IRT-based latent class models. Journal of Educational Measurement, 44(4), 293-311.
10.1111/j.1745-3984.2007.00040.x Google Scholar Search this bibliographic reference on Google Scholar
Rupp, A. A. (2009, avril). Software for calibrating Diagnostic Classification Models. Symposium conduit lors de l’American Educational Research Association de San Diego, CA. Documentation disponible à [http://www.education.umd.edu/EDMS/fac/Rupp/].
Google Scholar Search this bibliographic reference on Google Scholar
Rupp, A. A., & Templin, J. (2008). Unique characteristics of diagnostic classification models: A comprehensive review of the current state-of-the-art. Measurement: Interdisciplinary Research & Perspective, 6(4), 219-262.
Google Scholar Search this bibliographic reference on Google Scholar
Rupp, A. A., Templin, J., & Henson, R. J. (2010). Diagnostic measurement: Theory, methods, and applications. New York: The Guilford Press.
Google Scholar Search this bibliographic reference on Google Scholar
Sinharay, S., & Haberman, S. J. (2009). How much can we reliably know about what examinees know? Measurement: Interdisciplinary Research & Perspective, 7(1), 46-49.
10.1080/15366360802715486 Google Scholar Search this bibliographic reference on Google Scholar
Stout, W. (2007). Skills diagnosis using IRT-based continuous latent trait models. Journal of Educational Measurement, 44(4), 313-324.
10.1111/j.1745-3984.2007.00041.x Google Scholar Search this bibliographic reference on Google Scholar
Tatsuoka, C. (2009). Diagnostic models as partially ordered sets. Measurement: Interdisciplinary Research & Perspective, 7(1), 49-53.
Google Scholar Search this bibliographic reference on Google Scholar
Tatsuoka, K. K. (1983). Rule-space: an approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20, 345-354.
10.1111/j.1745-3984.1983.tb00212.x Google Scholar Search this bibliographic reference on Google Scholar
Tatsuoka, K. K. (1984). Caution indices based on item response thery. Psychometrika, 49(1), 95-110.
10.1007/BF02294208 Google Scholar Search this bibliographic reference on Google Scholar
Tatsuoka, K. K. (1995). Architecture of knowledge structures and cognitive diagnosis: A statistical pattern recognition and classification approach. In P. D. Nichols, S. F. Chipman & R. L. Brennan (dir.), Cognitively diagnostic assessment (pp. 327-360). Hillsdale, NJ: Erlbaum.
10.1080/15366360802490866 Google Scholar Search this bibliographic reference on Google Scholar
Tatsuoka, K. K. (2009). Cognitive assessment: An introduction to the rule space method. New York: Routledge Taylor & Francis Group.
Google Scholar Search this bibliographic reference on Google Scholar
Tatsuoka, K. K., Corter, J. E., & Tatsuoka, C. (2004). Patterns of diagnosed mathematical content and process skills in TIMSS-R across a sample of 20 countries. American Educational Research Journal, 41(4), 901-926.
10.3102/00028312041004901 Google Scholar Search this bibliographic reference on Google Scholar
Templin, J., & Henson, R. A. (2005). The random effects reparametrized unified model: A model for joint estimation of discrete skills and continuous ability. Princeton, NJ: Educational testing service external research group technical report.
Google Scholar Search this bibliographic reference on Google Scholar
Templin, J., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11, 287-305.
10.1037/1082-989X.11.3.287 Google Scholar Search this bibliographic reference on Google Scholar
von Davier, M. (2005). A general diagnostic model applied to language testing data (Research Report No. RR-05-16). Princeton, NJ: Educational Testing Service.
Google Scholar Search this bibliographic reference on Google Scholar
von Davier, M. (2009). Some notes on the reinvention of latent structure models as diagnostic classification models. Measurement: Interdisciplinary Research & Perspective, 7(1), 67-74.
10.1002/j.2333-8504.2005.tb01993.x Google Scholar Search this bibliographic reference on Google Scholar
Whitely, S. E. (1980). Multicomponent latent trait models for ability tests. Psychometrika, 45, 479-494.
10.1007/BF02293610 Google Scholar Search this bibliographic reference on Google Scholar
Wilhelm, O., & Robitzsch, A. (2009). Have cognitive diagnostic models delivered their goods? Some substantial and methodological concerns. Measurement: Interdisciplinary Research & Perspective, 7(1), 53-57.
10.1080/15366360802504799 Google Scholar Search this bibliographic reference on Google Scholar
Yan, D., Almond, R., & Mislevy, R. (2003). Empirical comparisons of cognitive diagnostic models. Princeton, NJ: Educational Testing Service.
Google Scholar Search this bibliographic reference on Google Scholar
Yan, D., Mislevy, R. J., & Almond, R. G. (2003). Design and analysis in a cognitive assessment (Research Report No. RR-03-32)). Princeton, NJ: Educational Testing Service.
10.1002/j.2333-8504.2003.tb01924.x Google Scholar Search this bibliographic reference on Google Scholar
Yepes-Baraya, M. (1998). Application of the rule-space methodology to the 1996 NAEP science assessment: grade 4 preliminary results. Washington, DC: Office of Educational Research and Improvement (ED).
Google Scholar Search this bibliographic reference on Google Scholar

Abstracts

Résumé

Abstract

Resumo

Appendices

Références

Citation Tools

Cite this article

Export the record for this article