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Creating Data Literate Students

Debbie Abilock; Susan D. Ballard; Tasha Bergson-Michelson; Jennifer Colby; Kristin Fontichiaro; Lynette Hoelter; Justin Joque; Jo Angela Oehrli; Jole Seroff; Susan Smith; Tierney Steelberg; Wendy Stephens; Martha Stuit; Connie Williams; Edited by Kristin Fontichiaro, Jo Angela Oehrli, and Amy Lennex

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DOI: http://dx.doi.org/10.3998/mpub.9873254

Published by: Ann Arbor, MI: Michigan Publishing, University of Michigan Library, 2017.

  

Permissions: This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Please contact mpub-help@umich.edu to use this work in a way not covered by the license.

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Table of Contents

• Introduction
  • Resources
• 1  Introduction to statistical literacy — Lynette Hoelter
  • Defining statistical literacy
  • The relationship between statistics and statistical literacy
  • Statistics review
  • Raising statistical literacy in the classroom
  • Resources
• 2  Statistical storytelling: The language of data — Tasha Bergson-Michelson
  • Experts and novices
  • Pure statistical storytelling
  • Emotionally evocative storytelling
  • Intensifiers and qualifiers
  • Teaching the nonfiction writer as narrator
  • Results required
  • Memory and (mis)information
  • Searching
  • Context terms
  • Associated search terms
  • Conclusion
  • Resources
  • Appendix A: Tips for integrating lessons on unpacking language into your research process
  • Appendix B: Causation and correlation worksheet
  • Appendix C: Does this sound like correlation, or is there a causal hint, too?
• 3  Using data in the research process — Jole Seroff
  • Building background knowledge with statistical benchmarking
  • As students research: Interrogating the statistic
  • Conclusion
  • Resources
  • Appendix A: Activities menu
• 4  Real world data fluency: How to use raw data — Wendy Stephens
  • Thinking computationally
  • Finding existing datasets
  • Representing data responsibly
  • Conclusion
  • Resources
• 5  Manipulating data in spreadsheets — Martha Stuit
  • Finding data
  • Downloading your data into a spreadsheet
  • Cleaning up the data in your spreadsheet
  • Sorting and filtering to discover patterns
  • Pivot tables
  • Drawing conclusions: Visualize data from a spreadsheet
  • Inserting your spreadsheet or graph into Word
  • Workarounds for Google Drive
  • Conclusion
  • Resources
  • Appendix A: Spreadsheet tools
• 6  Making sense of data visualization — Justin Joque
  • Graphs
  • Pie charts
  • Histograms
  • Layered visualizations
  • Categories, quantities, colors, and shapes
  • Representing quantitative and qualitative data
  • Reading data visualizations
  • Conclusion
  • Resources
• 7  Data presentation: Showcasing your data with charts and graphs — Tierney Steelberg
  • General rules of thumb
  • Useful charts & graphs
  • Conclusion
  • Resources
  • Appendix A
• 8  Deconstructing data visualizations: What every teen should know — Susan Smith
  • Think like a designer
  • Extending information literacy skills
  • A few basic rules of thumb
  • What does the data really mean?
  • Practice, practice, practice
  • Conclusion
  • Resources
  • Appendix A: Tips for integrating visualization analysis into your classroom
  • Appendix B: Practice evaluation with the 5 W’s
  • Appendix C: Unpacking visual decisions in an infographic
  • Appendix D: Choosing effective visualization types
• 9  Designing your infographic: Getting to design — Connie Williams
  • Poster vs infographic
  • Steps for teaching an infographic lesson
  • Conclusion
  • Resources
  • Appendix A: Question design matrix template
  • Appendix B: Question design matrix example
• 10  Using data visualizations in the content areas — Jennifer Colby
  • Why is data visualization instruction important in the classroom?
  • Data literacy skills can be the same as literacy skills
  • Addressing national standards
  • Data visualizations can do all that?
  • How does developing data literacy skills help students with standardized testing?
  • So, now are you ready to incorporate data literacy instruction into your content area?
  • Resources
  • Appendix A: Tips for integrating data visualizations into any content area
  • Appendix B: Integrating an infographic about Romeo and Juliet into the ELA classroom
  • Appendix C: Integrating an infographic about water into the science classroom
  • Appendix D: Integrating an infographic about the Olympics into the social studies classroom
  • Appendix E: Integrating an infographic about budgeting into a math classroom
  • Appendix F: Using a sample assessment question for an ELA classroom
  • Appendix G: Using a sample assessment question for a science classroom
  • Appendix H: Using a sample assessment question for a social studies classroom
  • Appendix I: Using a sample assessment question for a math classroom
• 11  Teaching data contexts: An instructional lens — Debbie Abilock
  • Scenario 1: Area: Why context matters
  • Scenario 2: Population: How data is constructed and contextualized
  • Scenario 3: Pandemics: The emotional context
  • Strategies for teaching context in the wild: Find a problem, build a rule
  • Conclusion
  • Resources
  • Appendix A: Mini-lessons
• 12  Diving lessons: Taking the data literacy plunge through action research — Susan D. Ballard
  • What is action research and evidence-based practice?
  • Why action research?
  • The action research proposal
  • Data collection
  • Sorting and analyzing data
  • Apply the action or intervention
  • Presenting the findings
  • Action research in action
  • Action research and the connection to data literacy
  • Conclusion: Time to get your feet wet
  • Resources
  • Appendix A: Data instrument comparison
  • Appendix B: Tips for integrating action research into your classroom
• Data literacy related standards
  • Visit the following web sites for additional information
• Data Literacy in the Real World: Conversations & Case Studies
• Glossary
• Contributors

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