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Automatic computer science domain multiple-choice questions generation based on informative sentences

Students require continuous feedback for effective learning. Multiple choice questions (MCQs) are extensively used among various assessment methods to provide such feedback. However, manual MCQ generation is a tedious task that requires significant effort, time, and domain knowledge. Therefore, a sy...

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Autores principales: Maheen, Farah, Asif, Muhammad, Ahmad, Haseeb, Ahmad, Shahbaz, Alturise, Fahad, Asiry, Othman, Ghadi, Yazeed Yasin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454961/
https://www.ncbi.nlm.nih.gov/pubmed/36091982
http://dx.doi.org/10.7717/peerj-cs.1010
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author Maheen, Farah
Asif, Muhammad
Ahmad, Haseeb
Ahmad, Shahbaz
Alturise, Fahad
Asiry, Othman
Ghadi, Yazeed Yasin
author_facet Maheen, Farah
Asif, Muhammad
Ahmad, Haseeb
Ahmad, Shahbaz
Alturise, Fahad
Asiry, Othman
Ghadi, Yazeed Yasin
author_sort Maheen, Farah
collection PubMed
description Students require continuous feedback for effective learning. Multiple choice questions (MCQs) are extensively used among various assessment methods to provide such feedback. However, manual MCQ generation is a tedious task that requires significant effort, time, and domain knowledge. Therefore, a system must be present that can automatically generate MCQs from the given text. The automatic generation of MCQs can be carried out by following three sequential steps: extracting informative sentences from the textual data, identifying the key, and determining distractors. The dataset comprising of various topics from the 9th and 11th-grade computer science course books are used in this work. Moreover, TF-IDF, Jaccard similarity, quality phrase mining, K-means, and bidirectional encoder representation from transformers techniques are utilized for automatic MCQs generation. Domain experts validated the generated MCQs with 83%, 77%, and 80% accuracy, key generation, and distractor generation, respectively. The overall MCQ generation achieved 80% accuracy through this system by the experts. Finally, a desktop app was developed that takes the contents in textual form as input, processes it at the backend, and visualizes the generated MCQs on the interface. The presented solution may help teachers, students, and other stakeholders with automatic MCQ generation.
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spelling pubmed-94549612022-09-09 Automatic computer science domain multiple-choice questions generation based on informative sentences Maheen, Farah Asif, Muhammad Ahmad, Haseeb Ahmad, Shahbaz Alturise, Fahad Asiry, Othman Ghadi, Yazeed Yasin PeerJ Comput Sci Adaptive and Self-Organizing Systems Students require continuous feedback for effective learning. Multiple choice questions (MCQs) are extensively used among various assessment methods to provide such feedback. However, manual MCQ generation is a tedious task that requires significant effort, time, and domain knowledge. Therefore, a system must be present that can automatically generate MCQs from the given text. The automatic generation of MCQs can be carried out by following three sequential steps: extracting informative sentences from the textual data, identifying the key, and determining distractors. The dataset comprising of various topics from the 9th and 11th-grade computer science course books are used in this work. Moreover, TF-IDF, Jaccard similarity, quality phrase mining, K-means, and bidirectional encoder representation from transformers techniques are utilized for automatic MCQs generation. Domain experts validated the generated MCQs with 83%, 77%, and 80% accuracy, key generation, and distractor generation, respectively. The overall MCQ generation achieved 80% accuracy through this system by the experts. Finally, a desktop app was developed that takes the contents in textual form as input, processes it at the backend, and visualizes the generated MCQs on the interface. The presented solution may help teachers, students, and other stakeholders with automatic MCQ generation. PeerJ Inc. 2022-08-16 /pmc/articles/PMC9454961/ /pubmed/36091982 http://dx.doi.org/10.7717/peerj-cs.1010 Text en © 2022 Maheen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Adaptive and Self-Organizing Systems
Maheen, Farah
Asif, Muhammad
Ahmad, Haseeb
Ahmad, Shahbaz
Alturise, Fahad
Asiry, Othman
Ghadi, Yazeed Yasin
Automatic computer science domain multiple-choice questions generation based on informative sentences
title Automatic computer science domain multiple-choice questions generation based on informative sentences
title_full Automatic computer science domain multiple-choice questions generation based on informative sentences
title_fullStr Automatic computer science domain multiple-choice questions generation based on informative sentences
title_full_unstemmed Automatic computer science domain multiple-choice questions generation based on informative sentences
title_short Automatic computer science domain multiple-choice questions generation based on informative sentences
title_sort automatic computer science domain multiple-choice questions generation based on informative sentences
topic Adaptive and Self-Organizing Systems
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454961/
https://www.ncbi.nlm.nih.gov/pubmed/36091982
http://dx.doi.org/10.7717/peerj-cs.1010
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