Question Guru: An Automated Multiple-Choice Question Generation System

• Main Authors: Gilal, A.R., Waqas, A., Talpur, B.A., Abro, R.A., Jaafar, J., Amur, Z.H.
• Format: Article
• Published: Springer Science and Business Media Deutschland GmbH 2023
• Online Access: http://scholars.utp.edu.my/id/eprint/34247/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144991527&doi=10.1007%2f978-3-031-20429-6_46&partnerID=40&md5=1d9f6e6313e00f9a1e23f781ee291241

During the last two decades, natural language processing (NLP) puts a tremendous impact on automated text generation. There are various important libraries in NLP that aid in the development of advanced applications in a variety of sectors, most notably education, with a focus on learning and assessment. In the learning environment, objective evaluation is a common approach to assessing student performance. Multiple-choice questions (MCQs) are a popular form of evaluation and self-assessment in both traditional and electronic learning contexts. A system that generates multiple-choice questions automatically would be extremely beneficial to teachers. The objective of this study is to develop an NLP based system, Quru (Question Guru), to produce questions automatically from text content. The Quru is broken into three basic steps to construct an automated MCQs generation system: Stem Extraction (Important Sentences Selection), Keyword Extraction, and Distractor Generation. Furthermore, the system's performance is validated by university lecturers. As per the findings, the MCQs generated are more than 80 accurate. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.