Using Information Retrieval to Detect Conflicting Questions

Abstract

Although E-learning has advanced considerably in the last decade, some of its aspects, such as E-testing, are still in the development phase. Authoring tools and test banks for E-tests are becoming an integral and indispensable part of E-learning platforms and with the implementation of E-learning standards, such as IMS QTI, E-testing material can be easily shared and
reused across various platforms. With the knowledge available for reuse and exam automation comes a new challenge: making sure that created exams are free of confl icts. A Confl ict exists in an exam if at least two questions within that exam are redundant in content, and/or if at least one question reveals the answer to another question within the same exam. In this paper we propose using Information Retrieval techniques to detect confl icts within an exam. Our solution, ICE (Identifi cation of Conflicts in Exams), is based on the vector space model relying on tf-idf weighing and the cosine function to calculate similarity. ICE also combines the hybrid recommendation techniques of the EQRS (Exam Question Recommender System) in order to propose replacements for confl icting questions.