One of the most common and computably tractable ways of evaluating the knowledge of a student is through the use of questionnaires with multiple choice questions (MCQ), where students must express a precise choice to answer a question, without leaving room for ambiguities or doubts. The problem is that sometimes the student doesn't really know the answer or cannot decide between the possible choices, even if he is able to discard some of them. We propose an alternative MCQ that, using belief function theory, allows the student to state his answer in an imprecise way, indicating to which degree each possible choice represents the correct answer. This way we get to model the ignorance and uncertainties of the learners, allowing an Intelligent Tutoring System (ITS) to gather a richer student model.