The Cloud Academy training platform offers a Skill Assessment tool capable of providing useful insights to learners and their managers. The assessment of skills is based both on the capability of users in solving practical exercises (hands-on labs) and on their proficiency in answering theoretical multiple-choice questions (quizzes and exams). The effectiveness of such a tool is based upon an accurate calibration of the hands-on labs and the exams the user is required to take.

In a previous post, we presented some of the ways in which Cloud Academy uses Natural Language Processing (NLP). In this article, we will show how NLP can be used for calibrating the multiple-choice questions (MCQs) of quizzes and exams as soon as they are created, which consists of estimating some properties (referred to as latent traits) that are required for scoring students under assessment. The immediate consequence of this solution will be an increased capability of assessing the users’ scores also with brand new questions, that typically require data (and time) before being usable.
In this article, we will describe a novel approach for estimating the latent traits of MCQs from textual information. This approach, named R2DE (Regression for Difficulty and Discrimination Estimation), is the result of an ongoing collaboration between Cloud Academy and Politecnico di Milano and will be presented in the research paper “R2DE: a NLP approach to estimating IRT parameters of newly generated questions” at the forthcoming International Conference on Learning Analytics and Knowledge (LAK ‘20). The paper is already available online.
What are latent traits and why are they important?
Latent traits are numerical values describing some properties of a question, and they can be of different kinds. In this post, we will focus on two latent traits that are used for assessing learners’ skills: the difficulty and the discrimination. Assuming the student’s knowledge

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