eprintid: 19419
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/94/19
datestamp: 2024-06-04 14:11:52
lastmod: 2024-06-04 14:11:52
status_changed: 2024-06-04 14:05:41
type: article
metadata_visibility: show
creators_name: Gilal, A.R.
creators_name: Waqas, A.
creators_name: Talpur, B.A.
creators_name: Abro, R.A.
creators_name: Jaafar, J.
creators_name: Amur, Z.H.
title: Question Guru: An Automated Multiple-Choice Question Generation System
ispublished: pub
note: cited By 2; Conference of 2nd International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2022 ; Conference Date: 2 September 2022 Through 3 September 2022; Conference Code:287819
abstract: 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.
date: 2023
publisher: Springer Science and Business Media Deutschland GmbH
official_url: 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
id_number: 10.1007/978-3-031-20429-6₄₆
full_text_status: none
publication: Lecture Notes in Networks and Systems
volume: 573 LN
pagerange: 501-514
refereed: TRUE
isbn: 9783031204289
issn: 23673370
citation:   Gilal, A.R. and Waqas, A. and Talpur, B.A. and Abro, R.A. and Jaafar, J. and Amur, Z.H.  (2023) Question Guru: An Automated Multiple-Choice Question Generation System.  Lecture Notes in Networks and Systems, 573 LN.  pp. 501-514.  ISSN 23673370