Managing Large Multiple-choice Test Items Repositories

Abstract

Knowledge assessment in online platforms is widely based on multiple-choice questions (MCQs). In this paper we describe our proposal for a NLP-based system designed to support the management of large repositories of MCQs. Indeed, within large repositories of MCQs, it is common to have similar if not almost duplicated questions, and coping with them is a time consuming and error prone task. We propose an approach, based on Natural Language Processing (NLP), that i) computes the similarity between the items and ii) checks the similarity between the questions and, if available, the areas of the syllabus. The results of the analysis are also displayed in a graph (i.e. network) based view, providing a clear picture to the user.