Volume 65 | Issue 12 | Year 2019 | Article Id. IJMTT-V65I12P517 | DOI : https://doi.org/10.14445/22315373/IJMTT-V65I12P517
Mohammad Nasim Wafa, "Assessing School Students' Mathematic Ability Using DINA and DINO Models," International Journal of Mathematics Trends and Technology (IJMTT), vol. 65, no. 12, pp. 153-165, 2019. Crossref, https://doi.org/10.14445/22315373/IJMTT-V65I12P517
[1] Baghaei, P., &Ravand, H. (2015). A cognitive processing model of reading comprehension in English as a foreign language using the linear logistic test model. Learning and Individual Differences, 43, 100-105.
[2] Birenbaum, M., Tatsuoka, C., & Yamada, T. (2004). Diagnostic assessment in TIMSS-R: Between-countries and within-country comparisons of eighth graders‟ mathematics performance. Studies in Educational Evaluation, 30(2), 151–173.
[3] Chen, J., & de la Torre, J. (2013). A general cognitive diagnosis model for expert-definedpolytomousattributes. Applied Psychological Measurement, 37(6), 419-437.
[4] Chung, M. (2014). Estimating the Q-matrix for cognitive diagnosis models in a Bayesian framework. Teachers College.
[5] Corter, J. E., &Tatsuoka, K. K. (2002). Cognitive and measurement foundations of diagnostic assessments in mathematics. The College Board: Technical Report.
[6] Corter, J. E., &Tatsuoka, K. K. (2002). Diagnostic Assessments for Mathematics Tests Grades 6-12.
[7] 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.
[8] De La Torre, J. (2009). A cognitive diagnosis model for cognitively based multiple-choice options. Applied Psychological Measurement, 33(3), 163–183.
[9] De La Torre, J. (2009). DINA model and parameter estimation: A didactic. Journal of Educational and Behavioral Statistics, 34(1), 115–130.
[10] De La Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199.
[11] De La Torre, J., & Douglas, J. A. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69(3), 333–353.
[12] De la Torre, J., & Lee, Y.-S. (2013). Evaluating the Wald test for item-level comparison of saturated and reduced models in cognitive diagnosis. Journal of Educational Measurement, 50(4), 355–373.
[13] Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–22.
[14] George, A. C., Robitzsch, A., Kiefer, T., Groß, J., &Ünlü, A. (2016). The R package CDM for cognitive diagnosis models. Journal of Statistical Software, 74(2), 1–24.
[15] Gierl, M. J., Zhou, J., & Alves, C. (2008). Developing a taxonomy of item model types to promote assessment engineering. The Journal of Technology, Learning and Assessment, 7(2).
[16] Grabe, W., & Stoller, F. (2002). Teaching and research reading. Harlow, UK: Longman.
[17] Haertel, E. H. (1989). Using restricted latent class models to map the skill structure of achievement items. Journal of Educational Measurement, 26(4), 301–321.
[18] Halimi, N. (2013). Mathematics Education in Secondary School in Afghanistan: Teachers‟ View and Practices on Teaching Problem Solving.
[19] Hartz, S. M. (2002). A Bayesian framework for the unified model for assessing cognitive abilities: Blending theory with practicality. ProQuest Information & Learning.
[20] Henson, R., & Douglas, J. (2005). Test construction for cognitive diagnosis. Applied Psychological Measurement, 29(4), 262-277.
[21] Huebner, A., & Wang, C. (2011). A note on comparing examinee classification methods for cognitive diagnosis models. Educational and Psychological Measurement, 71(2), 407–419.
[22] Junker, B. W., &Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25(3), 258–272.
[23] Kaya, Y., &Leite, W. L. (2017). Assessing change in latent skills across time with longitudinal cognitive diagnosis modeling: An evaluation of model performance. Educational and psychological measurement, 77(3), 369-388.
[24] Kunina‐Habenicht, O., Rupp, A. A., & Wilhelm, O. (2012). The impact of model misspecification on parameter estimation and item‐fit assessment in log‐linear diagnostic classification models. Journal of Educational Measurement, 49(1), 59-81.
[25] Lee, Y.-W., &Sawaki, Y. (2009). Application of three cognitive diagnosis models to ESL reading and listening assessments. Language Assessment Quarterly, 6(3), 239–263
[26] Lee, Y.-W., &Sawaki, Y. (2009). Cognitive diagnosis and Q-matrices in language assessment. Taylor & Francis.
[27] Lee, Y.-W., &Sawaki, Y. (2009). Cognitive diagnosis approaches to language assessment: An overview. Language Assessment Quarterly, 6(3), 172–189.
[28] 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(3), 205–237.
[29] Leighton, J., &Gierl, M. (2007). Cognitive diagnostic assessment for education: Theory and applications. Cambridge University Press.
[30] Mansory, A. M. (2010). Do Children Learn in Afghan Schools? Assessment of Math and Language Achievements of Students at the End of Grades 3 and 6 in SCA Supported Schools. Swedish Committee for Afghanistan (SCA).
[31] Mullis, I. V. S., Martin, M. O., Smith, T. A., & for the Evaluation of Educational Achievement, I. A. (2003). TIMSS: Assessment frameworks and specifications 2003. International Study Center, Lynch School of Education, Boston College~….
[32] Ravand, H. (2016). Application of a cognitive diagnostic model to a high-stakes reading comprehension test. Journal of Psychoeducational Assessment, 34(8), 782-799.
[33] Ravand, H., Barati, H., &Widhiarso, W. (2012). Exploring diagnostic capacity of a high stakes reading comprehension test: A pedagogical demonstration. Iranian Journal of Language Testing, 3(1), 12-37.
[34] Rupp, A. A., & Templin, J. (2008a). The effects of Q-matrix misspecification on parameter estimates and classification accuracy in the DINA model. Educational and Psychological Measurement, 68(1), 78–96.
[35] Rupp, A. A., & Templin, J. L. (2008b). Unique characteristics of diagnostic classification models: A comprehensive review of the current state-of-the-art. Measurement, 6(4), 219–262.
[36] Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic assessment: Theory, methods, and applications. New York: Guilford.
[37] Tatsuoka, K. K. (1983). Rule space: An approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20(4), 345–354.
[38] Tatsuoka, K. K. (1995). Architecture of knowledge structures and cognitive diagnosis: A statistical pattern recognition and classification approach. Cognitively Diagnostic Assessment, 327–359
[39] Templin, J., & Henson, R. A. (2010). Diagnostic measurement: Theory, methods, and applications. Guilford Press.
[40] Xu, X., & von Davier, M. (2008). Fitting the structured general diagnostic model to NAEP data. ETS Research Report Series, 2008(1), i--18.