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A question–answer generation system for an asynchronous distance learning platform
Distance learning frees the learning process from spatial constraints. Each mode of distance learning, including synchronous and asynchronous learning, has disadvantages. In synchronous learning, students have network bandwidth and noise concerns, but in asynchronous learning, they have fewer opport...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984765/ https://www.ncbi.nlm.nih.gov/pubmed/37361731 http://dx.doi.org/10.1007/s10639-023-11675-y |
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author | Wang, Hei-Chia Maslim, Martinus Kan, Chia-Hao |
author_facet | Wang, Hei-Chia Maslim, Martinus Kan, Chia-Hao |
author_sort | Wang, Hei-Chia |
collection | PubMed |
description | Distance learning frees the learning process from spatial constraints. Each mode of distance learning, including synchronous and asynchronous learning, has disadvantages. In synchronous learning, students have network bandwidth and noise concerns, but in asynchronous learning, they have fewer opportunities for engagement, such as asking questions. The difficulties associated with asynchronous learning make it difficult for teachers to determine whether students comprehend the course material. Motivated students will consistently participate in a course and prepare for classroom activities if teachers ask questions and communicate with them during class. As an aid to distance education, we want to automatically generate a sequence of questions based on asynchronous learning content. In this study, we will also generate multiple-choice questions for students to answer and teachers to easily correct. The asynchronous distance teaching-question generation (ADT-QG) model, which includes Sentences-BERT (SBERT) in the model architecture to generate questions from sentences with a higher degree of similarity, is proposed in this work. With the Wiki corpus generation option, it is anticipated that the Transfer Text-to-Text Transformer (T5) model will generate more fluent questions and be more aligned with the instructional topic. The results indicate that the questions created by the ADT-QG model suggested in this work have good fluency and clarity indicators, showing that the questions generated by the ADT-QG model are of a certain quality and relevant to the curriculum. |
format | Online Article Text |
id | pubmed-9984765 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-99847652023-03-06 A question–answer generation system for an asynchronous distance learning platform Wang, Hei-Chia Maslim, Martinus Kan, Chia-Hao Educ Inf Technol (Dordr) Article Distance learning frees the learning process from spatial constraints. Each mode of distance learning, including synchronous and asynchronous learning, has disadvantages. In synchronous learning, students have network bandwidth and noise concerns, but in asynchronous learning, they have fewer opportunities for engagement, such as asking questions. The difficulties associated with asynchronous learning make it difficult for teachers to determine whether students comprehend the course material. Motivated students will consistently participate in a course and prepare for classroom activities if teachers ask questions and communicate with them during class. As an aid to distance education, we want to automatically generate a sequence of questions based on asynchronous learning content. In this study, we will also generate multiple-choice questions for students to answer and teachers to easily correct. The asynchronous distance teaching-question generation (ADT-QG) model, which includes Sentences-BERT (SBERT) in the model architecture to generate questions from sentences with a higher degree of similarity, is proposed in this work. With the Wiki corpus generation option, it is anticipated that the Transfer Text-to-Text Transformer (T5) model will generate more fluent questions and be more aligned with the instructional topic. The results indicate that the questions created by the ADT-QG model suggested in this work have good fluency and clarity indicators, showing that the questions generated by the ADT-QG model are of a certain quality and relevant to the curriculum. Springer US 2023-03-04 /pmc/articles/PMC9984765/ /pubmed/37361731 http://dx.doi.org/10.1007/s10639-023-11675-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Wang, Hei-Chia Maslim, Martinus Kan, Chia-Hao A question–answer generation system for an asynchronous distance learning platform |
title | A question–answer generation system for an asynchronous distance learning platform |
title_full | A question–answer generation system for an asynchronous distance learning platform |
title_fullStr | A question–answer generation system for an asynchronous distance learning platform |
title_full_unstemmed | A question–answer generation system for an asynchronous distance learning platform |
title_short | A question–answer generation system for an asynchronous distance learning platform |
title_sort | question–answer generation system for an asynchronous distance learning platform |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984765/ https://www.ncbi.nlm.nih.gov/pubmed/37361731 http://dx.doi.org/10.1007/s10639-023-11675-y |
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