Résumés
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
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Parties annexes
Références
- Almond, R. (2007). Modeling diagnostic assessments with Bayesian networks. Journal of Educational Measurement, 44(4), 341-359.
- 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.
- Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: an application of the EM algorithm. Psychometnka, 46, 443-449.
- 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.
- 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.
- 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.
- 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.
- de la Torre, J. (2009). DINA model and parameter estimation: A didactic. Journal of Educational and Behavioral Statistics, 34(1), 115-130.
- de la Torre, J., & Douglas, J. A. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69(3), 333-353.
- 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.
- 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.
- 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.
- 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.
- Modèles de classification diagnostique 95
- 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.
- Doornik, J. A. (2002). Object-oriented matrix programming using Ox (version 3.1) [Logiciel]. London: Timberlake Consultats Press.
- Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, N.J.: Lawrence Erlbaum Associates.
- 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.
- 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.
- 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.
- 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.
- Gorin, J. S. (2009). Diagnostic classification models: Are they necessary? Commentary on Rupp and Templin (2008). Measurement: Interdisciplinary Research & Perspective, 7(1), 30-33.
- 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.
- Haertel, E. H. (1984). An application of latent class models to assessment data. Applied Psychological Measurement, 8, 333-346.
- Haertel, E. H. (1990). Continuous and discrete latent class structure models of item response data. Psychometnka, 55, 477-494.
- Hancock, G. R. (2009). Diagnostic classification modeling: opportunity for identity. Measurement: Interdisciplinary Research & Perspective, 7(1), 62-64.
- 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.
- Henson, R. (2009). Diagnostic classification models: throughts future directions. Measurement: Interdisciplinary Research & Perspective, 7(1), 34-36.
- 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.
- 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.
- 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.
- Jiao, H. (2009). Diagnostic classification models: Which one should I use? Measurement: Interdisciplinary Research & Perspective, 7(1), 65-67.
- 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.
- Karelitz, T. (2008). How binary skills obscure the transition from non-mastery to mastery. Measurement: Interdisciplinary Research & Perspective, 6(4), 268-272.
- 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.
- Leighton, J. P., & Gierl, M. J. (2007). Cognitive diagnostic assessment for education: Theory and applications. Cambridge: Cambridge University Press.
- 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.
- Levy, R. (2009). Evidentiary reasonning in diagnostic classification models. Measurement: Interdisciplinary Research & Perspective, 7(1), 36-41.
- Loye, N. (2005). Quelques modèles de mesure. Mesure et évaluation en éducation, 28(3), 51-68.
- 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.
- 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.
- 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.
- Macready, G. B., & Dayton, C. M. (1977). The use of probabilistic models in the assessment of mastery. Journal of Educational Statistics, 2, 99-120.
- Maris, E. (1999). Estimating multiple classification latent class models. Psychometrika, 64, 187-212.
- Maris, G., & Bechger, T. (2009). Equivalent diagnostic classification models. Measurement: Interdisciplinary Research & Perspective, 7(1), 41-46.
- Nichols, P. D., Chipman, S. F., & Brennan, R. L. (1995). Cognitively diagnostic assessment. Hillsdale, NJ: Erlbaum.
- Rao, C. R., & Sinharay, S. (dir.). (2007). Handbook of statistics (vol. 26). Amsterdam: Elsevier.
- Roussos, L., Templin, J., & Henson, R. (2007). Skills diagnosis using IRT-based latent class models. Journal of Educational Measurement, 44(4), 293-311.
- 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/].
- 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.
- Rupp, A. A., Templin, J., & Henson, R. J. (2010). Diagnostic measurement: Theory, methods, and applications. New York: The Guilford Press.
- Sinharay, S., & Haberman, S. J. (2009). How much can we reliably know about what examinees know? Measurement: Interdisciplinary Research & Perspective, 7(1), 46-49.
- Stout, W. (2007). Skills diagnosis using IRT-based continuous latent trait models. Journal of Educational Measurement, 44(4), 313-324.
- Tatsuoka, C. (2009). Diagnostic models as partially ordered sets. Measurement: Interdisciplinary Research & Perspective, 7(1), 49-53.
- Tatsuoka, K. K. (1983). Rule-space: an approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20, 345-354.
- Tatsuoka, K. K. (1984). Caution indices based on item response thery. Psychometrika, 49(1), 95-110.
- 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.
- Tatsuoka, K. K. (2009). Cognitive assessment: An introduction to the rule space method. New York: Routledge Taylor & Francis Group.
- 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.
- 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.
- Templin, J., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11, 287-305.
- von Davier, M. (2005). A general diagnostic model applied to language testing data (Research Report No. RR-05-16). Princeton, NJ: Educational Testing Service.
- 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.
- Whitely, S. E. (1980). Multicomponent latent trait models for ability tests. Psychometrika, 45, 479-494.
- 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.
- Yan, D., Almond, R., & Mislevy, R. (2003). Empirical comparisons of cognitive diagnostic models. Princeton, NJ: Educational Testing Service.
- 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.
- 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).