Academic Journal
High school students' data modeling practices and processes: from modeling unstructured data to evaluating automated decisions.
| Τίτλος: | High school students' data modeling practices and processes: from modeling unstructured data to evaluating automated decisions. |
|---|---|
| Συγγραφείς: | Jiang, Shiyan, Tang, Hengtao, Tatar, Cansu, Rosé, Carolyn P., Chao, Jie |
| Πηγή: | Learning, Media & Technology; Jun2023, Vol. 48 Issue 2, p350-368, 19p |
| Θεματικοί όροι: | High school students, Data modeling, Decision making, Prediction models, Machine learning |
| Περίληψη: | It's critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students' data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students' processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies. [ABSTRACT FROM AUTHOR] |
| Copyright of Learning, Media & Technology is the property of Taylor & Francis Ltd 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.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: High school students' data modeling practices and processes: from modeling unstructured data to evaluating automated decisions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jiang%2C+Shiyan%22">Jiang, Shiyan</searchLink><br /><searchLink fieldCode="AR" term="%22Tang%2C+Hengtao%22">Tang, Hengtao</searchLink><br /><searchLink fieldCode="AR" term="%22Tatar%2C+Cansu%22">Tatar, Cansu</searchLink><br /><searchLink fieldCode="AR" term="%22Rosé%2C+Carolyn+P%2E%22">Rosé, Carolyn P.</searchLink><br /><searchLink fieldCode="AR" term="%22Chao%2C+Jie%22">Chao, Jie</searchLink> – Name: TitleSource Label: Source Group: Src Data: Learning, Media & Technology; Jun2023, Vol. 48 Issue 2, p350-368, 19p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22High+school+students%22">High school students</searchLink><br /><searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: It's critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students' data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students' processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Learning, Media & Technology is the property of Taylor & Francis Ltd 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: 10.1080/17439884.2023.2189735 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 350 Subjects: – SubjectFull: High school students Type: general – SubjectFull: Data modeling Type: general – SubjectFull: Decision making Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: High school students' data modeling practices and processes: from modeling unstructured data to evaluating automated decisions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jiang, Shiyan – PersonEntity: Name: NameFull: Tang, Hengtao – PersonEntity: Name: NameFull: Tatar, Cansu – PersonEntity: Name: NameFull: Rosé, Carolyn P. – PersonEntity: Name: NameFull: Chao, Jie IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 17439884 Numbering: – Type: volume Value: 48 – Type: issue Value: 2 Titles: – TitleFull: Learning, Media & Technology Type: main |
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