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An Empirical Study About Calibration of Adaptive Hints in Web-Based Adaptive Testing Environments

  • Ricardo Conejo
  • Eduardo Guzmán
  • José-Luis Pérez de-la Cruz
  • Eva Millán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4018)

Abstract

In this paper we present a proposal for introducing hint adaptive selection in an adaptive web-based testing environment. To this end, a discussion of some aspects concerning the adaptive selection mechanism for hints is presented, which results in the statement of two axioms that such hints must fulfil. Then, an empirical study with real students is presented, whose goal is to evaluate a tentative bank of items with their associated hints to determine the usefulness of such hints for different knowledge levels and to calibrate both test items and hints.

Keywords

Item Response Theory Latent Trait Knowledge Level Item Bank Computerize Adaptive Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ricardo Conejo
    • 1
  • Eduardo Guzmán
    • 1
  • José-Luis Pérez de-la Cruz
    • 1
  • Eva Millán
    • 1
  1. 1.Departamento de Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaMálagaSpain

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