A Proposal for Performance-Based Assessment of the Learning of Machine Learning Concepts and Practices in K-12

Autor/inn/en	Gresse Von Wangenheim, Christiane; Da Cruz Alves, Nathalia; Rauber, Marcelo F.; Hauck, Jean C. R.; Yeter, Ibrahim H.
Titel	A Proposal for Performance-Based Assessment of the Learning of Machine Learning Concepts and Practices in K-12
Quelle	In: Informatics in Education, 21 (2022) 3, S.479-500 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1648-5831
Schlagwörter	Performance Based Assessment; Artificial Intelligence; Learning Processes; Scoring Rubrics; Concept Formation; Classification; Student Evaluation; Intelligent Tutoring Systems; Research Reports; Teaching Methods; Educational Objectives; Middle School Students; High School Students; Interrater Reliability; Computer Science Education; Kindergarten; Elementary Secondary Education + Suchen Sie Ihr Suchwort? Leistungsermittlung; Künstliche Intelligenz; Learning process; Lernprozess; Scoring formulas; Auswertungsbogen; Concept learning; Begriffsbildung; Classification system; Klassifikation; Klassifikationssystem; Schulnote; Studentische Bewertung; Intelligentes Tutorsystem; Research report; Forschungsbericht; Teaching method; Lehrmethode; Unterrichtsmethode; Educational objective; Bildungsziel; Erziehungsziel; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; High school; High schools; Oberschule; Studentin; Interrater-Reliabilität; Computer science lessons; Informatikunterricht
Abstract	Although Machine Learning (ML) is used already in our daily lives, few are familiar with the technology. This poses new challenges for students to understand ML, its potential, and limitations as well as to empower them to become creators of intelligent solutions. To effectively guide the learning of ML, this article proposes a scoring rubric for the performance-based assessment of the learning of concepts and practices regarding image classification with artificial neural networks in K-12. The assessment is based on the examination of student-created artifacts as a part of open-ended applications on the use stage of the Use-Modify-Create cycle. An initial evaluation of the scoring rubric through an expert panel demonstrates its internal consistency as well as its correctness and relevance. Providing a first step for the assessment of concepts on image recognition, the results may support the progress of learning ML by providing feedback to students and teachers. (As Provided).
Anmerkungen	Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences. Akademjos str. 4, Vilnius LT 08663 Lithuania. Tel: +37-5-21-09300; Fax: +37-5-27-29209; e-mail: info@mii.vu.lt; Web site: https://infedu.vu.lt/journal/INFEDU
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01