High School Students' Data Modeling Practices and Processes: From Modeling Unstructured Data to Evaluating Automated Decisions

Autor/inn/en	Jiang, Shiyan; Tang, Hengtao; Tatar, Cansu; Rosé, Carolyn P.; Chao, Jie
Titel	High School Students' Data Modeling Practices and Processes: From Modeling Unstructured Data to Evaluating Automated Decisions
Quelle	In: Learning, Media and Technology, 48 (2023) 2, S.350-368 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Jiang, Shiyan) ORCID (Tang, Hengtao) ORCID (Chao, Jie)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1743-9884
DOI	10.1080/17439884.2023.2189735
Schlagwörter	Artificial Intelligence; High School Students; Models; Classification; Data Analysis; Automation; Decision Making; Evaluation; Student Attitudes + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Analogiemodell; Classification system; Klassifikation; Klassifikationssystem; Auswertung; Decision-making; Entscheidungsfindung; Evaluierung; Schülerverhalten
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01