sue boudreau, science teacher, orinda intermediate school [email protected] catherine...
TRANSCRIPT
SUE BOUDREAU, SCIENCE TEACHER, ORINDA INTERMEDIATE SCHOOL [email protected] .CA.US
CATHERINE SALDUTTI , PRESIDENT EDUCHANGE
Stories in Statistics: Data Interpretation for Middle School Students
Who are you?
Cluster by subject area category. Walk to where you are with using data with
students, from rarely to often. Share a story about using data with students
good or bad.
Cool graph 1
What’s the story? Is it true?
Cool Graph 1 + additional info.
What do you think now? Could you use it?
Cool Graph 2
What’s the story? Is it true?
Add obituary/graveyard surveys to bring death statistics to life…
Cool Graph 3 Teaser…
If you want to save $ on your energy bill AND reduce your carbon emissions, what would be the most effective way to save? Clothes line instead of dryer… or what? Discuss with
your neighbor.
Cool Graph 3So were you right? What else do you
need to know before making changes?
Graphic from EnergyStar
Great Places to find hot data
Your local newspaperNY Times Tuesday Science
SectionNew Scientist Scientific American “By the
Numbers” sectionScientific American MindConsumer ReportsCook’s MagazineDiscover MagazineTime, Newsweek
magazinesMother JonesUN Millennium
Development Goals
Great places to find hot data
Use kids as your search engines – “Hot Data in Science” assignment….
Silly sources of data to seduce students with statistics
The Bathroom Readers
www.graphjam.com.
Students can practice observing, finding trends, making inferences, and asking ‘is it true?’
And more serious reasons students need to understand data
“Who Cares About Data?” student sheet linking data to their lives, health and future.
Presentation Focus: Data Interpretation
• Data Collection– In the lab, school or community– Associated with specific techniques or methods
• Data Organization– Aligning appropriate organizers to data– Organizer creation & notation
• Data Interpretation/Analysis –Our Focus today– Identifying Bias– Finding trends & patterns– Understanding the limitations of the interpretation– Problems students have
Data Work is Inquiry Work
The National Science Education Standards define inquiry as the following:
“Inquiry is a multifaceted activity that involves making observations; posing questions…using tools to gather, analyze, and interpret data, proposing answers; explanations and predictions…Inquiry requires identification of assumptions, use of critical and logical thinking, consideration of alternative explanations (p. 23).”
Why Teach Data Skills?
The National Research Council’s text on Inquiry states:
“Student understanding of inquiry does not, and cannot, develop in isolation from science subject matter (p. 36).”
Making data work PART AND PARCEL of content-based science instruction is the way to go—teaching one unit on science inquiry and then moving to content is not desirable for authentic science learning.
Data in the CA Frameworks
Grade 1: “Record observations and data with pictures, numbers or written statements… [and] on a bar graph”
Grade 4: “Differentiate observation from inference (interpretation) and know scientists’ explanations come partly from what they observe and partly from how they interpret their observations.”
Grade 7: “Select and use appropriate tools and technology…to perform tests, collect data and display data.”
Grades 9-12: “Identify possible reasons for inconsistent results, such as sources of error or uncontrolled conditions.”
Teaching Data Interpretation
• Identifying compelling data sources• Their lab data, from them, about them, their environment, news
data, surprising, accessible, topical data.
• “Hooking” students —methods to get them to care about these data• Guess and check, find the story, Hot issues in Data.
• Querying Data How it’s gathered, graphed and where it comes from all matter.
• Problems students have • Steps to “stories” with statistics
• Prediction, understanding, observing, trends, inferences and surprises.
• From statistical stories to science• Identify bias, tie data to conservative conclusions, triangulate
information, evaluate citations.
Identifying Compelling Data Sources
• Their own data from experiments and surveys.• Data they are part of. Family data, attendance, Ca Healthy
Kids…• Data relating to current unit in the news.• Data they want to know for project work or for their own
decisions. • Health data in the news they relate to – obesity, asthma,
diabetes, teen pregnancy, US eating & exercise habits etc.• Environment data in the news – especially local/state and
good news OR data that shows a way to do something positive.
• Data that is fairly easy to understand
• Surprising data – good news where they thought it was terrible, insights ex. happiness vs income.
Hooking students with their own survey:Blind taste-testing organic vs ordinary foods
Hooking students with data they want to know: Take Action Project
Hooking students with family stories – Will the diseases grandparents knew be
different to now?Try the disease scavenger hunt with the
people next to you. See “The Hunt is On” from SEPUP.
Look over the disease survey.Analyze family survey for cause of disease.
See the “Patterns of Disease” analysis sheet.Find trends by cause. Make inferences to explain. These questions
guide the unit.
Hooking students with family storiesData collection
Hooking students with family stories:Analysis
Hooking students with surprising data in Science and Life Issues: Medicine Testing
Students discover the placebo effect
Hooking students with data they are a part of:Has there been an epidemic at school this fall?
What trends in attendance do they expect?
See the activity sheet and data. Students graph their attendance
data for fall this year and last.
Hooking students: Setting up a unit on microbes –
Has there been an epidemic at school this fall?
• What surprises them?• Was there an epidemic?• What questions do they have
for the upcoming unit?• What questions about the
data – gaps? Causes of absence etc.
Querying Data – How data is gathered matters
Dr. Stupid finds that women have 52% faster reaction times than men. Is it true?
Watch and note the errors.
Querying Data – What was wrong with Dr. Stupid’s
data collection and analysis?
Mistakes include bias, poor measurement, not repeated, over-interpretation of data (gaps and silences).
Querying Data - How the data is graphed matters
Mortality statistics data graphed in 4 ways.
Work in groups of 4.Each does one graph plus the questions.Share what students might learn from
each of your graphs within your group.
But be careful not to leave the wrong way as the last thing they learn. End with an example of good practice.
Querying Data: How
it’s graphed matters.
Querying Data- Where it comes from matters
Precise statistics sound accurate, but are they? - The “Oh yeah?” attitude pays in science - Always have students consider sources of error and
the resulting significance of their data.
Which is accurate?“You only use 10% of your brain”
“Drink 8 glasses of water a day”Neither. Both are examples of incestuous reporting. See www.snopes.com
Querying Data- Subtle Bias and Information Literacy
Teach students how to be website detectiveS: Masthead, ‘about us’, sponsors and mission statement. “Follow the money” – is it commercial, and how will that
bias the information? Ex. Airborne, WebMd Does it have political or religious bias? Ex.
http://www.viewzone.com/endtime.html, www.peta.com Google the organization and sponsors. How was the data collected? TRIANGULATE data – “Extraordinary claims require
extraordinary evidence” – B.Nye
Problems Students Have
Plotting data on a consistent scale.Observing the data – understanding axes. Separating
observation from inference. Direct instruction, ‘bad graph’ activity, practice and leveraging with
math.Finding trends and patterns, especially the unexpected.
Direct instruction with examples ex. epidemic curves. Class culture that it’s fine to modify your hypothesis, mistakes are for learning.
Finding, evaluating and CITING SOURCES. Fairness, respect, makes your work look good. Grade it hard.
The bad data can be so entertaining that kids remember that instead of the right way to do it.
Use bad practice to highlight good practice the next day. Spend more time on good practice.
Steps to a “story” with statistics
Prediction is the story ‘hook’Relevance to their lives is the settingUnderstanding the axes, scale, observing
the data points is the premise. The trends & patterns is the narrative
and the surprise twist in the plotInferences are the possible conclusions
From Statistical Story to Science
Scientists are skeptical of extraordinary claims and surprises.
They identify bias. They tie data to conservative conclusions.
Triangulate data and information, including some primary sources.
Find how the data was collected.
Surprising patterns in reliable data motivate further research, leading to testable hypotheses and new discoveries – a very happy end to a story in statistics!
From Statistical Story to ScienceThe End
Reliable and Cool Data Sources
National Center for Health Statistics at Center for Disease Control and Prevention: http://www.cdc.gov/nchs/ FluView: http://www.cdc.gov/flu/weekly/
Nationmaster Statistics http://www.nationmaster.com/statistics World Health Organization Statistics Information Source:
http://www.who.int/whosis/en/ Guttmacher Institute advancing sexual and reproductive health world wide –
table maker. http://www.guttmacher.org/tablemaker/index.mhtml
www.snopes.com to check out possible internet urban legends. Your local newspaper NY Times Tuesday Science Section New Scientist Scientific American “By the Numbers” section Scientific American Mind Consumer Reports Cook’s Magazine Discover Magazine Time, Newsweek magazines Mother Jones UN Millennium Development Goals http://www.un.org/millenniumgoals/
Contact Information
Sue Boudreau, Orinda Intermediate School [email protected]
Catherine Saldutti, EduChange [email protected]
Link/site to download content: www.orindaschools.org, go to OIS, go to Teachers, go to Boudreau. See “Stories in Statistics” www.educhange.com/the_news.htm.
How are all these factors related?Using data to start the Take Action Projects
From New Scientist Feb.08