Imperfect Answers in Multiple Choice Questionnaires

  • Authors:

  • Javier Diaz;Maria Rifqi;Bernadette Bouchon-Meunier;Sandra Jhean-Larose;Guy Denhiére

  • Affiliations:

  • Université Pierre et Marie Curie - Paris6, CNRS UMR 7606, DAPA, LIP6, Paris, France F-75016;Université Pierre et Marie Curie - Paris6, CNRS UMR 7606, DAPA, LIP6, Paris, France F-75016;Université Pierre et Marie Curie - Paris6, CNRS UMR 7606, DAPA, LIP6, Paris, France F-75016;Équipe CHArt: Cognition Humaine et Artificielle, EPHE-CNRS, Paris, France 75015;Équipe CHArt: Cognition Humaine et Artificielle, EPHE-CNRS, Paris, France 75015

  • Venue:
  • EC-TEL '08 Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts
  • Year:
  • 2008

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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.