Thinking with Data

Autor/inn/en	Smith, Amy; Molinaro, Marco; Lee, Alisa; Guzman-Alvarez, Alberto
Titel	Thinking with Data
Quelle	In: Science Teacher, 81 (2014) 8, S.58-63 (6 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0036-8555
Schlagwörter	STEM Education; Statistics; Statistical Analysis; Learning Modules; Data Analysis; Data Collection; Data Interpretation; Science Process Skills; Skill Development; Learning Activities; Thinking Skills; Case Studies; Evidence; Secondary School Curriculum; High Schools; Teaching Methods + Suchen Sie Ihr Suchwort? STEM; Statistik; Statistische Analyse; Learning module; Lernmodul; Auswertung; Data capture; Datensammlung; Data evaluation; Datenauswertung; Kompetenzentwicklung; Qualifikationsentwicklung; Lernaktivität; Denkfähigkeit; Case study; Fallstudie; Case Study; Evidenz; High school; Oberschule; Teaching method; Lehrmethode; Unterrichtsmethode
Abstract	For students to be successful in STEM, they need "statistical literacy," the ability to interpret, evaluate, and communicate statistical information (Gal 2002). The science and engineering practices dimension of the "Next Generation Science Standards" ("NGSS") highlights these skills, emphasizing the importance of students' ability to analyze data and form evidence-based conclusions in response to complex questions. Recent research suggests that most high school students can create a graph and calculate a mean, but cannot describe what data represent, reason about data, or use data to generate evidence-based conclusions (Bakker and Gravemeijer 2004; Garfield and Ben-Zvi 2007; Sampson, Enderle, and Grooms 2013). This research recommends shifting instructional focus toward using more intuitive notions to make sense of data, detect patterns, and use data to confirm or refute scientific hypotheses before moving to more precise statistical definitions and calculations. Correspondingly, the Science, Biostatistics, and Cancer Education (SBCE) Distributions Module was developed by a team including education researchers (the authors of this article), a statistician, software engineers, and high school teacher leaders to provide students with the tools to informally reason about distributions of science-related data to form evidence-based arguments while building a bridge to formal statistical instruction. The module first introduces students to data, discussing what data are and why data are needed to make accurate conclusions. Then it introduces distributions of data displayed in stacked dotplots and the statistical terminology used to describe distributions. Finally, the module teaches basic resampling procedures, a more transparent approach for assessing statistical differences between two distributions of data (Cobb 2007). (ERIC).
Anmerkungen	National Science Teachers Association. 1840 Wilson Boulevard, Arlington, VA 22201-3000. Tel: 800-722-6782; Fax: 703-243-3924; e-mail: membership@nsta.org; Web site: http://www.nsta.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10