Imperfect answers in multiple choice questionnaires

Javier Diaz 1 Maria Rifqi 1 Bernadette Bouchon-Meunier 1 Sandra Jhean-Larose 2 Guy Denhière 2
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
2 LPC - Laboratoire de psychologie cognitive
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.
Type de document :
Communication dans un congrès
EC-TEL 2008 - 3rd European Conference on Technology Enhanced Learning, Sep 2008, Maastricht, Netherlands. Springer, 5192, pp.144-154, 2008, Lecture Notes in Computer Science. 〈10.1007/978-3-540-87605-2_17〉
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https://hal.inria.fr/hal-01072218
Contributeur : Maria Rifqi <>
Soumis le : mardi 7 octobre 2014 - 17:36:36
Dernière modification le : mercredi 21 mars 2018 - 18:58:10

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UNIV-AMU | LIP6 | UPMC | LPC

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Javier Diaz, Maria Rifqi, Bernadette Bouchon-Meunier, Sandra Jhean-Larose, Guy Denhière. Imperfect answers in multiple choice questionnaires. EC-TEL 2008 - 3rd European Conference on Technology Enhanced Learning, Sep 2008, Maastricht, Netherlands. Springer, 5192, pp.144-154, 2008, Lecture Notes in Computer Science. 〈10.1007/978-3-540-87605-2_17〉. 〈hal-01072218〉

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