Multiple-choice question generation with auto-generated distractors for computer-assisted educational assessment

被引:7
作者
Das, Bidyut [1 ]
Majumder, Mukta [2 ]
Phadikar, Santanu [3 ]
Sekh, Arif Ahmed [4 ]
机构
[1] Haldia Inst Technol, Haldia, India
[2] Univ North Bengal, Siliguri, India
[3] Maulana Abul Kalam Azad Univ Technol, Kolkata, W Bengal, India
[4] XIM Univ, Bhubaneswar, India
关键词
Computer-assisted learning; Multiple-choice question; Distractor generation; Unsupervised clustering; Computer-aided assessment; AUTOMATIC-GENERATION;
D O I
10.1007/s11042-021-11222-2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Multiple-choice questions (MCQs) are used as instrumental tool for assessment, not only in various competitive examinations but also in contemporary information and communications Technology (ICT)-based education, active learning, etc. Therefore, automatic generation of multiple-choice test items from text-based learning material is a truly demanding task in computer aided-assessment. A lot of systems were developed in the past two decades for this purpose, but the system generated questions have failed to satisfy the needs of computer-based automated assessment. As a consequence, this is still an open area of research in education technology and natural language processing. This article presents an automated system for generating multiple-choice test items with distractors. The system first selects informative sentences using the topic-words or keywords (one or more words). The best keyword from a selected sentence is chosen as an answer key. Next, the system eliminates the answer key from this sentence and transforms it into a question-sentence (stem). The wrong options or distractors are generated automatically using a feature-based clustering approach, without using any external information or knowledge-base. The result highlights the efficiency of the proposed system for generating MCQs with distractors.
引用
收藏
页码:31907 / 31925
页数:19
相关论文
共 57 条
[1]   Automatic generation of multiple choice questions using dependency-based semantic relations [J].
Afzal, Naveed ;
Mitkov, Ruslan .
SOFT COMPUTING, 2014, 18 (07) :1269-1281
[2]  
Agarwal M., 2011, Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications, P1
[3]  
Agarwal Manish., 2011, P 6 WORKSHOP INNOVAT, P56
[4]  
Aldabe I, 2010, LECT NOTES ARTIF INT, V6233, P27, DOI 10.1007/978-3-642-14770-8_5
[5]   Ontology-Based Multiple Choice Question Generation [J].
Alsubait, Tahani ;
Parsia, Bijan ;
Sattler, Ulrike .
KUNSTLICHE INTELLIGENZ, 2016, 30 (02) :183-188
[6]  
Andersen S, 2014, SENTENCE TYPES FUNCT
[7]  
[Anonymous], 2012, UNM ED
[8]  
Araki J., 2016, P COLING 2016 26 INT, P1125
[9]  
Becker L., 2012, P 2012 C N AM CHAPT, P742
[10]   SISR: System for integrating semantic relatedness and similarity measures [J].
Ben Aouicha, Mohamed ;
Taieb, Mohamed Ali Hadj ;
Ben Hamadou, Abdelmajid .
SOFT COMPUTING, 2018, 22 (06) :1855-1879