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多项选择题中自动干扰项生成:一项系统文献综述

Automatic distractor generation in multiple-choice questions: a systematic literature review.

作者信息

Awalurahman Halim Wildan, Budi Indra

机构信息

Faculty of Computer Science, Universitas Indonesia, Depok, West Java, Indonesia.

出版信息

PeerJ Comput Sci. 2024 Nov 13;10:e2441. doi: 10.7717/peerj-cs.2441. eCollection 2024.

DOI:10.7717/peerj-cs.2441
PMID:39650367
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11623049/
Abstract

BACKGROUND

Multiple-choice questions (MCQs) are one of the most used assessment formats. However, creating MCQs is a challenging task, particularly when formulating the distractor. Numerous studies have proposed automatic distractor generation. However, there has been no literature review to summarize and present the current state of research in this field. This study aims to perform a systematic literature review to identify trends and the state of the art of automatic distractor generation studies.

METHODOLOGY

We conducted a systematic literature following the Kitchenham framework. The relevant literature was retrieved from the ACM Digital Library, IEEE Xplore, Science Direct, and Scopus databases.

RESULTS

A total of 60 relevant studies from 2009 to 2024 were identified and extracted to answer three research questions regarding the data sources, methods, types of questions, evaluation, languages, and domains used in the automatic distractor generation research. The results of the study indicated that automatic distractor generation has been growing with improvement and expansion in many aspects. Furthermore, trends and the state of the art in this topic were observed.

CONCLUSIONS

Nevertheless, we identified potential research gaps, including the need to explore further data sources, methods, languages, and domains. This study can serve as a reference for future studies proposing research within the field of automatic distractor generation.

摘要

背景

多项选择题(MCQs)是最常用的评估形式之一。然而,创建多项选择题是一项具有挑战性的任务,尤其是在编写干扰项时。许多研究都提出了自动生成干扰项的方法。然而,目前还没有文献综述来总结和呈现该领域的研究现状。本研究旨在进行系统的文献综述,以确定自动生成干扰项研究的趋势和最新技术水平。

方法

我们按照基奇纳姆框架进行了系统的文献综述。相关文献从美国计算机协会数字图书馆、电气和电子工程师协会(IEEE)Xplore、科学Direct和Scopus数据库中检索。

结果

共识别并提取了2009年至2024年的60项相关研究,以回答关于自动生成干扰项研究中使用的数据来源、方法、问题类型、评估、语言和领域的三个研究问题。研究结果表明,自动生成干扰项在许多方面都随着改进和扩展而不断发展。此外,还观察到了该主题的趋势和最新技术水平。

结论

尽管如此,我们发现了潜在的研究空白,包括需要进一步探索数据来源、方法、语言和领域。本研究可为未来在自动生成干扰项领域提出研究的研究提供参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bab8/11623049/976b0e7a9174/peerj-cs-10-2441-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bab8/11623049/a8d918637af7/peerj-cs-10-2441-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bab8/11623049/a3e64ea38e55/peerj-cs-10-2441-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bab8/11623049/6eab5b7b5272/peerj-cs-10-2441-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bab8/11623049/976b0e7a9174/peerj-cs-10-2441-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bab8/11623049/a8d918637af7/peerj-cs-10-2441-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bab8/11623049/a3e64ea38e55/peerj-cs-10-2441-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bab8/11623049/6eab5b7b5272/peerj-cs-10-2441-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bab8/11623049/976b0e7a9174/peerj-cs-10-2441-g004.jpg

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本文引用的文献

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Eur J Clin Pharmacol. 2024 May;80(5):729-735. doi: 10.1007/s00228-024-03649-x. Epub 2024 Feb 14.
2
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PeerJ Comput Sci. 2022 Aug 16;8:e1010. doi: 10.7717/peerj-cs.1010. eCollection 2022.
3
A Natural-Language-Processing-Based Procedure for Generating Distractors for Multiple-Choice Questions.
一种基于自然语言处理的多选题干扰项生成方法。
Eval Health Prof. 2022 Dec;45(4):327-340. doi: 10.1177/01632787211046981. Epub 2021 Nov 9.
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Multiple-Choice Item Distractor Development Using Topic Modeling Approaches.使用主题建模方法开发多项选择题干扰项
Front Psychol. 2019 Apr 25;10:825. doi: 10.3389/fpsyg.2019.00825. eCollection 2019.
5
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6
Ontology-based multiple choice question generation.基于本体的多项选择题生成。
ScientificWorldJournal. 2014;2014:274949. doi: 10.1155/2014/274949. Epub 2014 Mar 26.