Improving cognitive diagnostic computerized adaptive testing by balancing attribute coverage: The modified maximum global discrimination index method

Educational and Psychological Measurement

Volumn 70, Issue 6, 2010, Pages 902-913

  Cheng, Ying ^a  

^a UNIVERSITY OF NOTRE DAME   (United States)

Author keywords

balanced attribute coverage; cognitive diagnosis; cognitive profile state; computerized adaptive testing; recovery rate

Indexed keywords

EID: 78650506144     PISSN: 00131644     EISSN: 15523888     Source Type: Journal    
DOI: 10.1177/0013164410366693     Document Type: Article

References (27)

1. Chang, H., & Ying, Z. (1996). A global information approach to computerized adaptive testing. Applied Psychological Measurement, 20, 213-229.
2. Chen, S., & Ankenmann, R.D. (2004). Effects of practical constraints on item selection rules at the early stages of computerized adaptive testing. Journal of Educational Measurement, 41, 149-174.
3. Cheng, Y. (2009). When cognitive diagnosis meets computerized adaptive testing: CD-CAT. Psychometrika, 74, 619-632.
4. Cheng, Y., Chang, H., & Yi, Q. (2007). Two-phase item selection procedure for flexible content balancing in CAT. Applied Psychological Measurement, 31, 467-482.
5. Cover, T.M., & Thomas. J.A. (1991). Elements of information theory. New York, NY: John Wiley.
6. Doignon, J. P., & Falmagne, J. C. (Eds.). (1999). Knowledge spaces. New York, NY: Springer.
7. Haertel, E.H. (1984). An application of latent class models to assessment data. Applied Psychological Measurement, 8, 333-346.
8. Haertel, E.H. (1989). Using restricted latent class models to map the skill structure of achievement items. Journal of Educational Measurement, 26, 333-352.
9. Haertel, E.H., & Wiley, D.E. (1993). Presentations of ability structures: Implications for testing. In N. Frederiksen, R. J. Mislevey, & I. I. Bejar (Eds.), Test theory for a new generation of tests (pp. 359-384). Hillsdale, NJ: Erlbaum.
10. Hambleton, R.K. (2005). Preface. In W. J. van der Linden (Ed.), Linear models to optimal test design (p. xi). New York, NY: Springer.
11. Hartz, S. (2002). A Bayesian framework for the unified model for assessing cognitive abilities: Blending theory with practice (Unpublished doctoral thesis), University of Illinois at Urbana-Champaign, Champaign.
12. Hartz, S., Roussos, L., & Stout, W. (2002). Skill diagnosis: Theory and practice [Computer software user manual for Arpeggio software]. Princeton, NJ: Educational Testing Service.
13. 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.
14. Kingsbury, G.G., & Zara, A.R. (1991). A comparison of procedures for content-sensitive item selection in computerized adaptive tests. Applied Measurement in Education, 4, 241-261.
15. Lord, F.M. (1980). Applications of item response theory to practical testing problems. Mahwah, NJ: Erlbaum.
16. Macready, G.B., & Dayton, C.M. (1977). The use of probablistic models in the assessment of mastery. Journal of Educational Statistics, 33, 379-416.
17. Maris, E. (1999). Estimating multiple classification latent class models. Psychometrika, 64, 187-212.
18. McGlohen, M., & Chang, H. (2008). Combining computer adaptive testing technology with cognitively diagnostic assessment. Behavior Research Methods, 40, 808-821.
19. Mislevy, R.J., Almond, R.G., Yan, D., & Steinberg, L.S. (1999). Bayes nets in educational assessment: Where do the numbers come from? In K. B. Laskey & H. Prade (Eds.), Proceedings of the fifteenth conference on uncertainty in artificial intelligence (pp. 437-446). San Francisco, CA: Morgan Kaufmann.
20. No Child Left Behind Act of 2001, Pub. L. No. 1-7-110 (2001).
21. Tatsuoka, K.K. (1983). Rule space: An approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20, 345-354.
22. Tatsuoka, K.K. (1995). Architecture of knowledge structures and cognitive diagnosis: A statistical pattern recognition and classification approach. In R. D. Nichols, S. F. Chipman, & R. L. Brennan (Eds.), Cognitively diagnostic assessment (pp. 327-359). Hillsdale, NJ: Erlbaum.
23. Thissen, D., & Mislevy, R.J. (2000). Testing algorithms. In H. Wainer (Ed.), Computerized adaptive testing: A primer (pp. 101-133). Hillsdale, NJ: Erlbaum.
24. Weiss, D.J. (1982). Improving measurement quality and efficiency with adaptive testing. Applied Psychological Measurement, 6, 473-492.
25. Weissman, A. (2007). Mutual information item selection in adaptive classification testing. Educational and Psychological Measurement, 67, 41-58.
26. Xu, X., Chang, H., & Douglas, J. (2003). A simulation study to compare CAT strategies for cognitive diagnosis. Paper presented at the annual meeting of National Council on Measurement in Education, Montreal, Quebec, Canada.
27. Xu, X., & Douglas, J. (2006). Computerized adaptive testing under nonparametric IRT models. Psychometrika, 71, 121-137.