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Determining targets for multi-stage adaptive tests using integer programming

Author

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  • Armstrong, Ronald D.
  • Kung, Mabel T.
  • Roussos, Louis A.

Abstract

This paper considers a multi-stage adaptive test (MST) where the testlets at each stage are determined prior to the administration. The assembly of a MST requires target information and target response functions for the MST design. The targets are chosen to create tests with accurate scoring and high utilization of items in an operational pool. Forcing all MSTs to have information and response function plots to be within an interval about the targets will yield parallel MSTs, in the sense that standardized paper-and-pencil tests are considered parallel. The objective of this paper is to present a method to determine targets for the MST design based on an item pool and an assumed distribution of examinee ability. The approach is applied to a Skills Readiness Inventory test designed to identify logical reasoning deficiencies of examinees. This method can be applied to obtain item response theory targets for a linear test as this is a special case of a MST.

Suggested Citation

  • Armstrong, Ronald D. & Kung, Mabel T. & Roussos, Louis A., 2010. "Determining targets for multi-stage adaptive tests using integer programming," European Journal of Operational Research, Elsevier, vol. 205(3), pages 709-718, September.
    • Handle: RePEc:eee:ejores:v:205:y:2010:i:3:p:709-718
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    References listed on IDEAS

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    1. Lu, Chin-Shan & Lai, Kee-hung & Cheng, T.C.E., 2007. "Application of structural equation modeling to evaluate the intention of shippers to use Internet services in liner shipping," European Journal of Operational Research, Elsevier, vol. 180(2), pages 845-867, July.
    2. Adema, Jos J. & Boekkooi-Timminga, Ellen & van der Linden, Wim J., 1991. "Achievement test construction using 0-1 linear programming," European Journal of Operational Research, Elsevier, vol. 55(1), pages 103-111, November.
    3. Ronald Armstrong & Dmitry Belov & Alexander Weissman, 2005. "Developing and Assembling the Law School Admission Test," Interfaces, INFORMS, vol. 35(2), pages 140-151, April.
    4. Csondes, Tibor & Kotnyek, Balazs & Zoltan Szabo, Janos, 2002. "Application of heuristic methods for conformance test selection," European Journal of Operational Research, Elsevier, vol. 142(1), pages 203-218, October.
    5. Mavrommatis, George, 2008. "Learning objects and objectives towards automatic learning construction," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1449-1458, June.
    6. Gupta, Sumeet & Kim, Hee W., 2008. "Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities," European Journal of Operational Research, Elsevier, vol. 190(3), pages 818-833, November.
    7. Anderson, Ronald D. & Vastag, Gyula, 2004. "Causal modeling alternatives in operations research: Overview and application," European Journal of Operational Research, Elsevier, vol. 156(1), pages 92-109, July.
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