An Empirical Analysis of High School Students' Practices of Modelling with Unstructured Data

Autor/inn/en	Jiang, Shiyan; Nocera, Amato; Tatar, Cansu; Yoder, Michael Miller; Chao, Jie; Wiedemann, Kenia; Finzer, William; Rosé, Carolyn P.
Titel	An Empirical Analysis of High School Students' Practices of Modelling with Unstructured Data
Quelle	In: British Journal of Educational Technology, 53 (2022) 5, S.1114-1133 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Jiang, Shiyan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.13253
Schlagwörter	High School Students; Data; Artificial Intelligence; Mathematical Models; Advanced Placement; Computer Science Education; Classification; Prediction; Error Patterns; Decision Making + Suchen Sie Ihr Suchwort? High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Daten; Künstliche Intelligenz; Mathematical model; Mathematisches Modell; Computer science lessons; Informatikunterricht; Classification system; Klassifikation; Klassifikationssystem; Vorhersage; Fehlertyp; Decision-making; Entscheidungsfindung
Abstract	To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K-12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K-12 AI education by examining student learning of modelling real-world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in-depth understandings of how text classification models--a type of AI application--are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in-depth reasoning about model decision making. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01