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.