Multidimensional Computerized Adaptive Testing in Recovering Reading and Mathematics Abilities

نویسندگان

  • Yuan H. Li
  • William D. Schafer
چکیده

Under a MIRT CAT’s (multidimensional computerized adaptive testing) testing scenario, an ability estimate in one dimension will provide clues for subsequently seeking a solution in other dimensions. This feature may enhance the efficiency of MIRT CAT’s item selection as well as its scoring algorithms compared with its counterpart, unidimensional CAT (UCAT). The shadow test approach ( van der Linden & Reese, 1998) was imposed on both MIRT CAT and UCAT to maintain content balance for the current research design which intended to assess how feasibly (or well) the MIRT CAT can be implemented in a real testing program under current circumstances and to address the issue of which of these two CAT algorithms would perform better. The present study used existing Reading and Math test data to generate simulated item parameters. A confirmatory item factor-analysis model was applied to the data using NOHARM (Fraser & McDonald, 1988) to produce interpretable MIRT item parameters. The item pool resulting from this analysis has two special features: (a) each Reading or Math item contributed only to its intended measure, and (b) since the simulated item discrimination parameters were intentionally defined for the Reading and Math dimensions, the MIRT CAT ability estimates should correspond to both measures, respectively, rather than to the abstract multidimensional abilities, θ. Results showed that MIRT CAT conditional on the constraints was quite capable of producing accurate estimates on both measures. Compared with UCAT, MIRT CAT increased the accuracy of both ability estimates, especially for the low or high abilities in both measures, and reduced the rate of unused items in the item pool.

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تاریخ انتشار 2003