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Reliable Knowledge-Based Adaptive Tests by Credal Networks

  • Francesca Mangili
  • Claudio Bonesana
  • Alessandro Antonucci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)

Abstract

An adaptive test is a computer-based testing technique which adjusts the sequence of questions on the basis of the estimated ability level of the test taker. We suggest the use of credal networks, a generalization of Bayesian networks based on sets of probability mass functions, to implement adaptive tests exploiting the knowledge of the test developer instead of training on databases of answers. Compared to Bayesian networks, these models might offer higher expressiveness and hence a more reliable modeling of the qualitative expert knowledge. The counterpart is a less straightforward identification of the information-theoretic measure controlling the question-selection and the test-stopping criteria. We elaborate on these issues and propose a sound and computationally feasible procedure. Validation against a Bayesian-network approach on a benchmark about German language proficiency assessments suggests that credal networks can be reliable in assessing the student level and effective in reducing the number of questions required to do it.

Keywords

Bayesian Network Conditional Entropy Probability Mass Function Computer Adaptive Testing Interval Constraint 
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 International Publishing AG 2017

Authors and Affiliations

  • Francesca Mangili
    • 1
  • Claudio Bonesana
    • 1
  • Alessandro Antonucci
    • 1
  1. 1.Istituto Dalle Molle di Studi sull’Intelligenza ArtificialeLuganoSwitzerland

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