Multiple-choice questions are often used to assess the user's understanding. However, the system does not know if the user answered randomly, by chance, with confidence or without confidence. The user's confidence can be used as a feedback for teachers or smart interfaces that could understand that he is not sure of himself and then provide further assistance. We aim to create confidence aware interfaces such as pervasive display. As a first step, we propose an application in the context of multiple-choice question answering. By using an eye tracker, we analyze the reading and answering behavior of the user, extract some features and predict his confidence. We asked 11 participants to solve 80 multiple-choice questions about English understanding, and estimated whether they answered confidently or not. We obtained 90.1% average accuracy for this prediction.
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