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The main objective of exams consists in performing an assessment of students'expertise on a specific subject. Such expertise, also referred to as skill orknowledge level, can then be leveraged in different ways (e.g., to assign agrade to the students, to understand whether a student might need some support,etc.). Similarly, the questions appearing in the exams have to be assessed insome way before being used to evaluate students. Standard approaches toquestions' 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 aRegressor for Difficulty and Discrimination Estimation), a model capable ofassessing newly generated multiple-choice questions by looking at the text ofthe question and the text of the possible choices. In particular, it canestimate the difficulty and the discrimination of each question, as they aredefined in Item Response Theory. We also present the results of extensiveexperiments we carried out on a real world large scale dataset coming from ane-learning platform, showing that our model can be used to perform an initialassessment of newly created questions and ease some of the problems that arisein question generation.

R2DE: a NLP approach to estimating IRT parameters of newly generated questions