Analyzing the Text Contents Produced by ChatGPT: Prompts, Feature-Components in Responses, and a Predictive Model.
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| Title: | Analyzing the Text Contents Produced by ChatGPT: Prompts, Feature-Components in Responses, and a Predictive Model. |
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| Authors: | Leping Liu1 liu@unr.edu |
| Source: | Journal of Educational Technology Development & Exchange. 2023, Vol. 16 Issue 1, p49-70. 22p. |
| Subject Terms: | ChatGPT, Language models, Prediction models, Logistic regression analysis, Research questions |
| Abstract: | ChatGPT is a large language model that uses deep learning to produce natural language and generate intelligent and relevant responses to user prompts. It comes to the field of education as an inevitable wave. Educators have to deal with it and figure out appropriate ways to use it and produce positive learning. This study explores the use of ChatGPT from the perspective of front-end users, focusing on the text-content that ChatGPT can produce for learners to learn new knowledge (e.g., a concept, a theory, or an application). The sample of this study consists of 253 ChatGPT text responses derived from three types of initial prompts/questions: general questions, specific questions, and questions with interactive prompts. Six feature components of text-information that can help learners to understand new knowledge are analyzed (concept and definition, procedure, example, comparison or contrast, deductive or inductive argument, summary). The results from Chi-square tests indicate that the presence of each feature component in the responses differs by the types of prompts. The results from a logistic regression analysis reveal that the presence of five (out of the six) feature components are significant to the probability that a response provides accurate and reliable information. The integration of using ChatGPT into learning is discussed. Further research questions are suggested. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Educational Technology Development & Exchange is the property of Society of International Chinese in Educational Technology (SICET) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 170733879 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Analyzing the Text Contents Produced by ChatGPT: Prompts, Feature-Components in Responses, and a Predictive Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Leping+Liu%22">Leping Liu</searchLink><relatesTo>1</relatesTo><i> liu@unr.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Technology+Development+%26+Exchange%22">Journal of Educational Technology Development & Exchange</searchLink>. 2023, Vol. 16 Issue 1, p49-70. 22p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22ChatGPT%22">ChatGPT</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Research+questions%22">Research questions</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: ChatGPT is a large language model that uses deep learning to produce natural language and generate intelligent and relevant responses to user prompts. It comes to the field of education as an inevitable wave. Educators have to deal with it and figure out appropriate ways to use it and produce positive learning. This study explores the use of ChatGPT from the perspective of front-end users, focusing on the text-content that ChatGPT can produce for learners to learn new knowledge (e.g., a concept, a theory, or an application). The sample of this study consists of 253 ChatGPT text responses derived from three types of initial prompts/questions: general questions, specific questions, and questions with interactive prompts. Six feature components of text-information that can help learners to understand new knowledge are analyzed (concept and definition, procedure, example, comparison or contrast, deductive or inductive argument, summary). The results from Chi-square tests indicate that the presence of each feature component in the responses differs by the types of prompts. The results from a logistic regression analysis reveal that the presence of five (out of the six) feature components are significant to the probability that a response provides accurate and reliable information. The integration of using ChatGPT into learning is discussed. Further research questions are suggested. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Educational Technology Development & Exchange is the property of Society of International Chinese in Educational Technology (SICET) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.18785/jetde.1601.03 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 49 Subjects: – SubjectFull: ChatGPT Type: general – SubjectFull: Language models Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Logistic regression analysis Type: general – SubjectFull: Research questions Type: general Titles: – TitleFull: Analyzing the Text Contents Produced by ChatGPT: Prompts, Feature-Components in Responses, and a Predictive Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Leping Liu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 19418027 Numbering: – Type: volume Value: 16 – Type: issue Value: 1 Titles: – TitleFull: Journal of Educational Technology Development & Exchange Type: main |
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