Students' Informal Statistical Inferences through Data Modeling with a Large Multivariate Dataset

Autor/inn/en	Kazak, Sibel; Fujita, Taro; Turmo, Manoli Pifarre
Titel	Students' Informal Statistical Inferences through Data Modeling with a Large Multivariate Dataset
Quelle	In: Mathematical Thinking and Learning: An International Journal, 25 (2023) 1, S.23-43 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kazak, Sibel)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1098-6065
DOI	10.1080/10986065.2021.1922857
Schlagwörter	Statistical Inference; Mathematics Skills; Mathematics Instruction; Secondary School Students; Computer Software; Multivariate Analysis; Visual Aids; Data Analysis; Statistics Education; Trend Analysis; Reports; Prediction; Assignments; Task Analysis; Information Technology; Information Science Education; Employment Qualifications; Higher Education; Learning Management Systems; Foreign Countries; United Kingdom (England) + Suchen Sie Ihr Suchwort? Inferential statistics; Schließende Statistik; Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Mathematics lessons; Mathematikunterricht; Sekundarschüler; Multivariate Analyse; Anschauungsmaterial; Auswertung; Trendanalyse; Abschlussbericht; Berichten; Vorhersage; Assignment; Auftrag; Zuweisung; Aufgabenanalyse; Informationstechnologie; Informationstechnologische Bildung; Employment qualification; Vocational qualification; Vocational qualifications; Berufliche Qualifikation; Hochschulbildung; Hochschulsystem; Hochschulwesen; Ausland
Abstract	In today's age of information, the use of data is very powerful in making informed decisions. Data analytics is a field that is interested in identifying and interpreting trends and patterns within big data to make data-driven decisions. We focus on informal statistical inference and data modeling as a means of developing students' data analytics skills in school. In this study, we examine how students apply the data modeling process to draw informal inferences when exploring trends, patterns and relationships in a real dataset using technological tools, such as CODAP and Excel. We analyzed 17-18-year-old students' written reports on their explorations of data supplied by third parties. Students used a variety of statistical measures and visualizations to account for variability in analyzing data. They tended to make statements with certainty in their inferences and predictions beyond the data. When the pattern in the data was uncertain, they were inclined to use contextual knowledge to remain certain in their claims. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01