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Imperfect Answers in Multiple Choice Questionnaires

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 5192)

Abstract

Multiple choice questions (MCQs) are the most common and computably tractable ways of assessing the knowledge of a student, but they restrain the students to express a precise answer that doesn’t really represent what they know, leaving no room for ambiguities or doubts. We propose Ev-MCQs (Evidential MCQs), an application of belief function theory for the management of the uncertainty and imprecision of MCQ answers. Intelligent Tutoring Systems (ITS) and e-Learning applications could exploit the richness of the information gathered through the acquisition of imperfect answers through Ev-MCQs in order to obtain a richer student model, closer to the real state of the student, considering their degree of knowledge acquisition and misconception.

Keywords

  • Belief Function
  • Intelligent Tutor System
  • Focal Element
  • Multiple Choice Questionnaire
  • Basic Belief Assignment

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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  • DOI: 10.1007/978-3-540-87605-2_17
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© 2008 Springer-Verlag Berlin Heidelberg

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Diaz, J., Rifqi, M., Bouchon-Meunier, B., Jhean-Larose, S., Denhiére, G. (2008). Imperfect Answers in Multiple Choice Questionnaires. In: Dillenbourg, P., Specht, M. (eds) Times of Convergence. Technologies Across Learning Contexts. EC-TEL 2008. Lecture Notes in Computer Science, vol 5192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87605-2_17

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  • DOI: https://doi.org/10.1007/978-3-540-87605-2_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87604-5

  • Online ISBN: 978-3-540-87605-2

  • eBook Packages: Computer ScienceComputer Science (R0)