A systematic review of teaching and learning machine learning in K-12 education.

Autor/inn/en	Sanusi, Ismaila Temitayo; Oyelere, Solomon Sunday; Vartiainen, Henriikka; Suhonen, Jarkko; Tukiainen, Markku
Titel	A systematic review of teaching and learning machine learning in K-12 education.
Quelle	In: Education and information technologies, 2023 (2023) 28, S. 5967-5997Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei (1); PDF als Volltext kostenfreie Datei (2); PDF als Volltext kostenfreie Datei (3) Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	1360-2357; 1573-7608
DOI	10.1007/s10639-022-11416-7
Schlagwörter	Systematic Review; Künstliche Intelligenz; Informatikunterricht; Kindergarten; Künstliche Intelligenz; Lernen; Schule; Kindergarten; Schule; Lernen; Literaturanalyse; Informatikunterricht
Abstract	"The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field." [Abstract: Author's information].
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2024/1