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.
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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
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