  The main objective of exams consists in performing an assessment of students'
expertise on a specific subject. Such expertise, also referred to as skill or
knowledge level, can then be leveraged in different ways (e.g., to assign a
grade to the students, to understand whether a student might need some support,
etc.). Similarly, the questions appearing in the exams have to be assessed in
some way before being used to evaluate students. Standard approaches to
questions' assessment are either subjective (e.g., assessment by human experts)
or introduce a long delay in the process of question generation (e.g.,
pretesting with real students). In this work we introduce R2DE (which is a
Regressor for Difficulty and Discrimination Estimation), a model capable of
assessing newly generated multiple-choice questions by looking at the text of
the question and the text of the possible choices. In particular, it can
estimate the difficulty and the discrimination of each question, as they are
defined in Item Response Theory. We also present the results of extensive
experiments we carried out on a real world large scale dataset coming from an
e-learning platform, showing that our model can be used to perform an initial
assessment of newly created questions and ease some of the problems that arise
in question generation.
