Educational Data Mining: The Analysis of the Factors Affecting Science Instruction by Clustering Analysis

Autor/inn/en	Eser, Mehmet Taha; Çobanoglu Aktan, Derya
Titel	Educational Data Mining: The Analysis of the Factors Affecting Science Instruction by Clustering Analysis
Quelle	In: International Journal of Educational Methodology, 7 (2021) 3, S.487-500 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Eser, Mehmet Taha) ORCID (Çobanoglu Aktan, Derya)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2469-9632
Schlagwörter	Data Analysis; Learning Analytics; Science Education; Scores; Scientific Literacy; Achievement Tests; International Assessment; Foreign Countries; Secondary School Students; Science Tests; Teaching Methods; Science Teachers; Feedback (Response); Inquiry; Active Learning; Cross Cultural Studies; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Auswertung; Naturwissenschaftliche Bildung; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; Sekundarschüler; Teaching method; Lehrmethode; Unterrichtsmethode; Science; Teacher; Teachers; Science teacher; Wissenschaft; Lehrer; Lehrerin; Lehrende; Aktives Lernen; Cultural comparison; Kulturvergleich
Abstract	Science literacy, which is included in Programme for International Student Assessment (PISA) as an assessment area, is an important research and discussion area of science education literature with all its dimensions. In this study, the clustering results of the students from 34 Organization for Economic Cooperation and Development (OECD) countries participating in the PISA 2015 test and sampled by systematic sampling method are obtained by K-Means Clustering and Two-Step Cluster Analysis using the factor scores and PISA science literacy average scores. It is thought that the study is of great importance in terms of dividing individuals into clusters according to science instruction methods and the mean of plausible values and having an idea about how each cluster is defined. As a result of the K-means cluster analysis, it was determined that the input variable with the highest level of importance in the formation of the first and third clusters in which the students with the highest scores were included was teacher-directed science instruction, and after this variable, the input variable with the highest level of importance was the perceived feedback from science teachers. Within the scope of the Two-Step Clustering Analysis, it was determined that teacher-directed science instruction has the most importance in terms of the decomposition of clusters, followed by adaptive instruction in science lessons in terms of importance level. (As Provided).
Anmerkungen	Eurasian Society of Educational Research. 7321 Parkway Drive South, Hanover, MD 21076. e-mail: publisher@ijem.com; Web site: https://www.ijem.com/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01