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Data in The Classroom:  It's Not Just for Nerds Anymore!
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Data in The Classroom: It's Not Just for Nerds Anymore!

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These slides provide resources for real, interactive, and fun data faculty can bring into the classroom for great discussions and paper assignments designed to get students thinking critically. You …

These slides provide resources for real, interactive, and fun data faculty can bring into the classroom for great discussions and paper assignments designed to get students thinking critically. You don't need to be a numbers guru to do it! These slides also emphasize the value of data and numbers to students in getting great jobs and in understanding the world around them.

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  • 1. Data in the Classroom:It’s not just for nerdsanymore! May 8, 2012 Linda Detterman Lynette Hoelter ICPSR - Institute for Social Research
  • 2. Session Outline• Defining “quantitative literacy (QL)” and “data”• Why the emphasis on quantitative literacy?• “But, I teach English… – …. I don‟t „do‟ data” – …. my students don‟t „do‟ data” – …. what does quantitative literacy mean for me?”• Tools for incorporating data in the classroom• Evidence of effectiveness from social sciences
  • 3. Defining QuantitativeLiteracy/Reasoning, Numeracy “„Statistical literacy, quantitative literacy, numeracy – under the hood, it is what do we want people to be able to do: Read tables and graphs and understand English statements that have numbers in them. That‟s a good start,‟ said Milo Schield, a professor of statistics at Augsburg College and a vice president of the National Numeracy Network. Shield was dismayed to find that, in a survey of his new students, 44 percent could not read a simple 100 percent row table and about a quarter could not accurately interpret a scatter plot of adult heights and weights.” Chandler, Michael Alison. What is Quantitative Literacy?, Washington Post, Feb. 5, 2009
  • 4. • Skills learned & used within a context• Skills: – Reading and interpreting tables or graphs and to calculating percentages and the like – Working within a scientific model (variables, hypotheses, etc.) – Understanding and critically evaluating numbers presented in everyday lives – Evaluating arguments based on data – Knowing what kinds of data might be useful in answering particular questions• A straightforward example of a definition/skill list: Samford University
  • 5. What do we mean by “data”?• Definitions differ by context. For example: – Citing another author who supports your point – Analysis of newspaper articles, blogs, Twitter feeds, commercials, etc. looking for themes – The result of an in-depth interview or observation – Information from medical tests, experiments, and other scientific exercises• For this presentation, “data” refers to summary information presented numerically in graphs, charts, or tables and the underlying survey results.
  • 6. Sources of Data• Administrative records (e.g., human resource files, police records)• Census and other government data collections• Individuals responding to a survey – Highly standardized – Recorded (“coded”) as numbers and these numbers can be used in combination to say something about the group of people who responded
  • 7. Why is QL Important?• Critical for a democratic society (Steen 2001) – Informed citizenry – must be able to make sense of information coming from multiple sources. – Use of evidence in making decisions and evaluating arguments.
  • 8. Importance (Con’t)• Job skills
  • 9. Why QL Across the Curriculum?• “Quantitative literacy largely determines an individual‟s capacity to control his or her quality of life and to participate effectively in social decision-making.• Educational policy and practice have fallen behind the rapidly changing data-oriented needs of modern society, and undergraduate education is the appropriate locus of leadership for making the necessary changes• QL is not about „basic skills‟ but rather, like reading and writing, is a demanding college-level learning expectation that cuts across the entire undergraduate curriculum• The current calculus-driven high school curriculum is unlikely to produce a quantitatively literate student population” (MAA 2004:xii)
  • 10. QL Outside of Math/Statistics• Other disciplines provide context for numbers, giving them meaning• More repetition of skills, better learning• Inclusion in multiple settings reduces student anxiety – helps fight culture of “quantitative dumbing down”• Teacher anxiety can be reduced with tools (pre-made exercises, interpretations given)
  • 11. YOU don’t need to be a statisticsguru to use data in class• Start class with a data-based news article• Have students interpret charts/graphs from popular media and critique news articles• Require empirical evidence to support claims in essays• Question banks and exercises allow students to work with surveys and the resulting data• Have students collect data – even in-class polls!• Engage students by having them find maps, graphs, or other data that provide examples of course content
  • 12. Tools for Faculty• Data archives – Public opinion – Topic-specific archives• Quantitative news blogs• Pre-made exercises, pedagogical examples• Collections of resources
  • 13. Public Opinion Data• Roper Center for Public Opinion Research http://www.ropercenter.uconn.edu• Gallup: http://www.gallup.com• NORC reports & data: www.norc.org/Research/DataFindings• Pew Social & Demographic Trends: http://www.pewsocialtrends.org/
  • 14. Topic-specific Archives• Association of Religion Data Archives(www.thearda.com)• Sociometrics (family, AIDS, maternal drug abuse, etc.)
  • 15. News Blogs & Quick Facts• TeachingWithData.org – Data in the News• U.S. Census Newsroom• Other government sources; organizations – beware of credibility• Data360• Press releases from own university Use of terms “Soda,” “Pop,” and “Coke” from The Invisible Borders that Define American Culture, Data360
  • 16. Collections of Resources• Science Education Resource Center (Carleton College – pedagogical materials)• TeachingWithData.org• ICPSR – Online Learning Center – Modules – Tools (SSVD, Bibliography, SDA)
  • 17. Arguments and Evidence fromSocial Sciences• Increased learning – Makes course content relevant to students – Emphasizes substantive points – Higher student engagement• Better sense of field – Less disconnect between substantive and technical courses – Learn how social scientists actually work
  • 18. More Arguments/Evidence• Provides students with marketable skills – ASA survey – statistical knowledge most widely represented on resumes – Enhances writing and critical thinking
  • 19. Data can be FUN!