High school students' data modeling practices and processes: from modeling unstructured data to evaluating automated decisions

• Published in: Journal of educational media : the journal of the Educational Television Association Vol. 48; no. 2; pp. 350 - 368
• Main Authors: Jiang, Shiyan, Tang, Hengtao, Tatar, Cansu, Rosé, Carolyn P., Chao, Jie
• Format: Journal Article
• Language: English
• Published: Basingstoke Routledge 03.04.2023; Taylor & Francis Ltd
• Subjects: Artificial Intelligence; Automation; Classification; Data Analysis; Data modeling; Data models; Decision Making; Decisions; Evaluation; High School Students; Literacy; Machine learning; model development processes; Modelling; Prediction models; Secondary school students; Student Attitudes; Students; Unstructured data
• ISSN: 1743-9884; 1743-9892
• DOI: 10.1080/17439884.2023.2189735

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.