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Liza Bondurant; Stephanie Somersille – Mathematics Teacher: Learning and Teaching PK-12, 2024
This article describes an activity and resource from The New York Times that can be used to help learners cultivate critical statistical literacy. Critical statistical literacy involves understanding, interpreting, and questioning statistical information to make informed decisions (Casey et al., 2023; Franklin et al., 2015; Weiland, 2017). It is a…
Descriptors: Statistics Education, Teaching Methods, Newspapers, Decision Making
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Ann M. Brearley; Kollin W. Rott; Laura J. Le – Journal of Statistics and Data Science Education, 2023
We present a unique and innovative course, Biostatistical Literacy, developed at the University of Minnesota. The course is aimed at public health graduate students and health sciences professionals. Its goal is to develop students' ability to read and interpret statistical results in the medical and public health literature. The content spans the…
Descriptors: Statistics Education, Data Interpretation, Teaching Methods, Biology
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Lee, Victor R.; Drake, Joel; Williamson, Kylie – TechTrends: Linking Research and Practice to Improve Learning, 2015
Accessibility to wearable technology has exploded in the last decade. As such, this technology has potential to be used in classrooms in uniquely interactive and personally meaningful ways. Seeing this as a possible future for schools, we have been exploring approaches for designing activities to incorporate wearable physical activity data…
Descriptors: Physical Education, Handheld Devices, Technology Uses in Education, Data Collection
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Smith, Amy; Molinaro, Marco; Lee, Alisa; Guzman-Alvarez, Alberto – Science Teacher, 2014
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…
Descriptors: STEM Education, Statistics, Statistical Analysis, Learning Modules
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Taylor, Judith V. – Teaching Children Mathematics, 1997
Presents activities having to do with data generation, organization, interpretation, representation, drawing conclusions, and making predictions on the basis of data which students have collected. (ASK)
Descriptors: Data Analysis, Data Collection, Data Interpretation, Elementary Education
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Hitch, Chris; Armstrong, Georganna – Arithmetic Teacher, 1994
Presents four sets of activities to develop the concepts of data analysis and graphing. Students estimate sample populations using beans, examine graphs from newspapers and magazines, predict the most popular color of cars, and simulate quality control in a manufacturing process. (MDH)
Descriptors: Concept Formation, Data Analysis, Data Collection, Data Interpretation
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Young, Sharon L. – Arithmetic Teacher, 1991
Presents a series of activities using data collection and interpretation techniques designed for levels 1-8, 1-5, 3-6, and 4-8 that integrate mathematics and social studies through the common theme of television viewing. Includes an activity sheet for parents to use with their children, three class-activity sheets, and a data sheet. (MDH)
Descriptors: Data Analysis, Data Collection, Data Interpretation, Elementary Education
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Stevens, Jill – Mathematics Teacher, 1993
Presents activities in which students develop and analyze scatterplots on graphing calculators to model corn growth, decay, a box of maximum volume, and weather prediction. Provides reproducible worksheets. (MDH)
Descriptors: Cooperative Learning, Data Analysis, Data Interpretation, Functions (Mathematics)
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Dixon, Juli K.; Falba, Christy J. – Mathematics Teaching in the Middle School, 1997
Describes five activities using the World Wide Web that teach students to experience searching, locating, and organizing data. Students learn to summarize statistics, analyze data, make conjectures, and communicate information. They interpret or create bar graphs, line graphs, histograms, and circle graphs. (PVD)
Descriptors: Computer Assisted Instruction, Computer Uses in Education, Critical Thinking, Data Interpretation