470
Views
6
CrossRef citations to date
0
Altmetric
Articles

Diagnosing Competency Mastery in Science: An Application of GDM to TIMSS 2011 Data

, , , &

References

  • AAAS. (1993). Benchmarks for science literacy. Retrieved from http://www.project2061.org/tools/benchol/bolintro.htm
  • Birenbaum, M., Tatsuoka, C., & Xin, T. (2005). Large-scale diagnostic assessment: Comparison of eighth graders’ mathematics performance in the United States, Singapore and Israel. Assessment in Education: Principles, Policy & Practice, 12(2), 167–181. doi:10.1080/09695940500143852
  • Birenbaum, M., & Tatsuoka, K. K. (1993). Applying an IRT-based cognitive diagnostic model to diagnose students’ knowledge states in multiplication and division with exponents. Applied Measurement in Education, 6(4), 255–268. doi:10.1207/s15324818ame0604_1
  • Braun, H., Coley, R., Jia, Y., & Trapani, C. (2009). Exploring what works in science instruction: A look at the eighth-grade science classroom. Princeton, NJ: Educational Testing Service.
  • Chen, Y. H., Gorin, J. S., Thompson, M. S., & Tatsuoka, K. K. (2008). An alternative examination of Chinese Taipei mathematics achievement: Application of the rule-space method to TIMSS 1999 data. In M. Von Davier & D. Hastedt (Eds.), Issues and methodologies in large-scale assessments (Vol. 1, pp. 23–49). Hamburg, Germany/Princeton, NJ: IEA-ETS Research Institute.
  • Chiu, C. Y., & Seo, M. (2009). Cluster analysis for cognitive diagnosis: An application to the 2001 PIRLS reading assessment. In M. von Davier & D. Hastedt (Eds.), Issues and methodologies in large-scale assessments (Vol. 2, pp. 137–159). Hamburg, Germany/Princeton, NJ: IEA-ETS Research Institute.
  • de La Torre, J. (2009). A cognitive diagnosis model for cognitively based multiple-choice options. Applied Psychological Measurement, 33(3), 163–183. doi:10.1177/0146621608320523
  • de La Torre, J., & Douglas, J. A. (2008). Model evaluation and multiple strategies in cognitive diagnosis: An analysis of fraction subtraction data. Psychometrika, 73(4), 595–624. doi:10.1007/s11336-008-9063-2
  • DeCarlo, L. T. (2011). On the analysis of fraction subtraction data: The DINA model, classification, latent class sizes, and the Q-matrix. Applied Psychological Measurement, 35(1), 8–26. doi:10.1177/0146621610377081
  • DiBello, L. V., Roussos, L. A., & Stout, W. (2007). Review of cognitively diagnostic assessment and a summary of psychometric models. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics psychometrics (Vol. 26, pp. 979–1030). Amsterdam, The Netherlands: Elsevier Science Publishers.
  • 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. doi:10.1007/s10649-007-9099-8
  • Embretson, S., & Gorin, J. (2001). Improving construct validity with cognitive psychology principles. Journal of Educational Measurement, 38(4), 343–368. doi:10.1111/j.1745-3984.2001.tb01131.x
  • Gierl, M. J. (2007). Making diagnostic inferences about cognitive attributes using the rule-space model and attribute hierarchy method. Journal of Educational Measurement, 44(4), 325–340. doi:10.1111/j.1745-3984.2007.00042.x
  • Gierl, M. J., & Cui, Y. (2008). Defining characteristics of diagnostic classification models and the problem of retrofitting in cognitive diagnostic assessment. Measurement: Interdisciplinary Research and Perspectives, 6(4), 263–268. doi:10.1080/15366360802497762
  • Haberman, S. J., & Von Davier, M. (2007). Some notes on models for cognitively based skills diagnosis. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics (Vol. 26, pp. 1031–1038). Amesterdam, the Netherlands: Elsevier Science Publishers.
  • Im, S., & Park, H. J. (2010). A comparison of US and Korean students’ mathematics skills using a cognitive diagnostic testing method: Linkage to instruction. Educational Research and Evaluation, 16(3), 287–301. doi:10.1080/13803611.2010.523294
  • Jang, E. E. (2009). Cognitive diagnostic assessment of L2 reading comprehension ability: Validity arguments for Fusion Model application to LanguEdge assessment. Language Testing, 26(1), 031–073. doi:10.1177/0265532208097336
  • Joncas, M. (2012). TIMSS 2011 sample design. In M. O. Martin & I. V. S. Mullis (Eds.), Methods and procedures in TIMSS and PIRLS 2011 (pp. 77–92). Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
  • Kim, Y. H. (2011). Diagnosing EAP writing ability using the reduced reparameterized unified model. Language Testing, 28(4), 509–541. doi:10.1177/0265532211400860
  • Lee, Y. S., Park, Y. S., & Taylan, D. (2011). A cognitive diagnostic modeling of attribute mastery in Massachusetts, Minnesota, and the US national sample using the TIMSS 2007. International Journal of Testing, 11(2), 144–177. doi:10.1080/15305058.2010.534571
  • Leighton, J. P., & Gierl, M. J. (2011). The learning sciences in educational assessment: The role of cognitive models. New York, NY: Cambridge University Press.
  • Martin, M. O., Mullis, I. V., Foy, P., & Stanco, G. M. (2012). TIMSS 2011 international results in science. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
  • Mullis, I. V. S., Martin, M. O., Ruddock, G. J., O’Sullivan, C. Y., & Preuschoff, C. (2009). TIMSS 2011 assessment frameworks. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
  • NAGB. (2010). Science framework for the 2011 national assessment of educational progress. Washington, DC: National Assessment Governing Board.
  • NRC. (1996). National science education standards. Washington, DC: National Academies Press.
  • NRC. (2000). Inquiry and the national science education standards: A guide for teaching and learning. Washington, DC: National Academies Press.
  • OECD. (2006). Assessing scientific, reading and mathematical literacy: A framework for PISA 2006. Paris, France: OECD.
  • OECD. (2009). PISA 2009 assessment framework: Key competencies in reading, mathematics and science. Paris, France: OECD.
  • Park, Y. S., & Lee, Y. S. (2011). Diagnostic cluster analysis of mathematics skills. In M. Von Davier & D. Hastedt (Eds.), Issues and methodologies in large-scale assessments (Vol. 4, pp. 75–107). Hamburg, Germany/Princeton, NJ: IEA-ETS Research Institute.
  • Roussos, L. A., Templin, J. L., & Henson, R. A. (2007). Skills diagnosis using IRT based latent class models. Journal of Educational Measurement, 44(4), 293–311. doi:10.1111/j.1745-3984.2007.00040.x
  • Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic measurement: Theory, methods, and applications. New York, NY: The Guilford Press.
  • Rupp, A. A., & Templin, J. L. (2008). Unique characteristics of diagnostic classification models: A comprehensive review of the current state-of-the-art. Measurement, 6(4), 219–262. doi:10.1080/15366360802490866
  • Sinharay, S., & Almond, R. G. (2007). Assessing fit of cognitive diagnostic models: A case study. Educational and Psychological Measurement, 67(2), 239–257. doi:10.1177/0013164406292025
  • 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. doi:10.3102/00028312041004901
  • von Davier, M. (2007a). Hierarchical general diagnostic models (No. RR-07-19). Princeton, NJ: Educational Testing Service.
  • von Davier, M. (2007b). Mixture distribution diagnostic models (No. RR-07-32). Princeton, NJ: Educational Testing Service.
  • von Davier, M. (2008). A general diagnostic model applied to language testing data. British Journal of Mathematical and Statistical Psychology, 61(2), 287–307. doi:10.1348/000711007X193957
  • von Davier, M. (2009). Using the general diagnostic model to measure learning and change in a longitudinal large-scale assessment (No. RR-09-28). Princeton, NJ: Educational Testing Service.
  • von Davier, M. (2011). Equivalency of the DINA model and a constrained general diagnostic model (No. RR-11-37). Princeton, NJ: Educational Testing Service.
  • von Davier, M., & Xu, X. (2009). Estimating latent structure models (including diagnostic classification models) with mdltm—A software for multidimensional discrete latent traits models. In A. A. Rupp (Ed.), Software for calibrating diagnostic classification models: An overview of the current state-of-the-art. College Park: University of Maryland.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.