LIVE INTERACTIVE LEARNING @ YOUR DESKTOP
Preparing for NGSS:
Analyzing and Interpreting Data
Presented by: Ann Rivet
October 23, 2012
6:30 p.m. – 8:00 p.m. Eastern time
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Developing the Standards
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Developing the Standards
Assessments
Curricula
Instruction
Teacher
Development
July 2011
2011-2013
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NGSS Development Process
In addition to a number of reviews by state teams
and critical stakeholders, the process includes two
public reviews.
1st Public Draft was in May 2012
2nd Public Draft will take place in the Fall of 2012
Final Release is expected in the Spring of 2013
IT’S NOT OUT YET!
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A Framework for K-12 Science Education
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Released in July 2011
Developed by the National Research Council at the
National Academies of Science
Prepared by a committee of Scientists (including Nobel
Laureates) and Science Educators
Three-Dimensions:
Scientific and Engineering Practices
Crosscutting Concepts
Disciplinary Core Ideas
Free PDF available from The National Academies Press (www.nap.edu)
Print Copies available from NSTA Press (www.nsta.org/store)
Scientific and Engineering Practices
1. Asking questions (for science)
and defining problems (for engineering)
2. Developing and using models
3. Planning and carrying out investigations
4. Analyzing and interpreting data
5. Using mathematics and computational thinking
6. Constructing explanations (for science)
and designing solutions (for engineering)
7. Engaging in argument from evidence
8. Obtaining, evaluating, and communicating information
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Crosscutting Concepts
1. Patterns
2. Cause and effect: Mechanism and explanation
3. Scale, proportion, and quantity
4. Systems and system models
5. Energy and matter: Flows, cycles, and conservation
6. Structure and function
7. Stability and change
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Disciplinary Core Ideas
Life Science
Physical Science
LS1: From Molecules to Organisms:
Structures and Processes
PS1: Matter and Its Interactions
LS2: Ecosystems: Interactions, Energy, and
Dynamics
LS3: Heredity: Inheritance and Variation of
Traits
PS2: Motion and Stability: Forces and
Interactions
PS3: Energy
PS4: Waves and Their Applications in
Technologies for Information Transfer
LS4: Biological Evolution: Unity and Diversity
Earth & Space Science
Engineering & Technology
ESS1: Earth’s Place in the Universe
ETS1: Engineering Design
ESS2: Earth’s Systems
ETS2: Links Among Engineering,
Technology, Science, and Society
ESS3: Earth and Human Activity
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Closer Look at a Performance Expectation
Construct and use models to explain that atoms combine to form new substances of varying complexity in terms of
the number of atoms and repeating subunits. [Clarification Statement: Examples of atoms combining can include Hydrogen
(H2) and Oxygen (O2) combining to form hydrogen peroxide (H2O2) or water(H2O). [Assessment Boundary: Restricted to
macroscopic interactions.]
Performance expectations combine practices, core ideas,
and crosscutting concepts into a single statement.
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Closer Look at a Performance Expectation
Construct and use models to explain that atoms combine to form new substances of varying complexity in terms of
the number of atoms and repeating subunits. [Clarification Statement: Examples of atoms combining can include Hydrogen
(H2) and Oxygen (O2) combining to form hydrogen peroxide (H2O2) or water(H2O). [Assessment Boundary: Restricted to
macroscopic interactions.]
Performance expectations combine practices, core ideas,
and crosscutting concepts into a single statement.
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Practices and the NGSS:
Analyzing and
Interpreting Data
Ann Rivet
Teachers College Columbia University
NSTA Webinar October 23, 2012
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Who Am I?
• Associate Professor of Science Education at Teachers College
Columbia University
• Background in science: physics and earth science
• Focus on the design of learning environments that support
students in understanding the Earth
• Connections between curriculum, instruction, and assessment
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Who Am I?
• Associate Professor of Science Education at Teachers College
Columbia University
• Background in science: physics and earth science
• Focus on the design of learning environments that support
students in understanding the Earth
• Connections between curriculum, instruction and assessment
Caveats
• Not part of the Framework development team
• Not an expert in engineering
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Overview
• What is the practice of analyzing and interpreting data?
• Why is analyzing and interpreting data important?
• Connections within the Framework
• Progression of practice
• Classroom examples
• Discussion
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Poll: What is Data?
• Which of the following do you NOT consider to be data?
A. Photos
B. Drawings
C.
Written Observations
D. Measurements
E.
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All of the above can be data
What Does “Analyzing and
Interpreting Data” Mean?
• Data: Facts, statistics, or items of information
• Analyze: To study or determine the nature and relationship of
the parts
• Interpret: To explain the meaning of
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What Does “Analyzing and
Interpreting Data” Mean?
• Data: Facts, statistics, or items of information
• Analyze: To study or determine the nature and relationship of
the parts
• Interpret: To explain the meaning of
• The process of assigning meaning to collected information and
determining conclusions, significance, and implications
• A function of both the type of information and the question asked
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The Practice of Analyzing and
Interpreting Data
• Practices: Ways of thinking about and working with science
concepts to address problems and answer questions
• The goal in science is to connect information (in the form of
data) to some sort of claim or explanation
• In the process of doing so, the information needs to be put in
a form where the meaning of the data can be recognized and
extracted.
• This is the practice of analysis and interpretation
• Guiding questions: “What do the data we collected mean?”
“How do these data help me answer my question?”
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Why Focus on Analyzing and
Interpreting Data?
• Key piece of both the “doing” and “thinking” of science that is
often overlooked or skimmed over
• Central to connecting abstract ideas and concrete examples
• Uses multiple tools and strategies
• Engages a wide array of thinking and reasoning skills
• In engineering, iterative cycles are not just trial and error.
They are about figuring out HOW it worked in a particular way
and WHY.
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Practices and the Framework
1. Ask questions and
defining problems
2. Developing and using
models
3. Planning and carrying
out investigations
4. Analyzing and
interpreting data
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5. Using mathematics
and computational
thinking
6. Developing
explanations and
designing solutions
7. Engaging in argument
from evidence
8. Obtaining, evaluating,
and communicating
information
How Relates to Other Practices
• Analyzing and interpreting data is the process of connecting
information gathered in investigations to explanations, models
and arguments through the transformation of data into
evidence.
• Obtaining evidence is the central purpose underlying data
analysis and interpretation
• Connected to all the other practices
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Guided by Questions and Investigations
1. Ask questions and
defining problems
2. Developing and using
models
3. Planning and carrying
out investigations
4. Analyzing and
interpreting data
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5. Using mathematics
and computational
thinking
6. Developing
explanations and
designing solutions
7. Engaging in argument
from evidence
8. Obtaining, evaluating,
and communicating
information
Informed By Models
1. Ask questions and
defining problems
2. Developing and using
models
3. Planning and carrying
out investigations
4. Analyzing and
interpreting data
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5. Using mathematics
and computational
thinking
6. Developing
explanations and
designing solutions
7. Engaging in argument
from evidence
8. Obtaining, evaluating,
and communicating
information
Uses Mathematics Tools
1. Ask questions and
defining problems
2. Developing and using
models
3. Planning and carrying
out investigations
4. Analyzing and
interpreting data
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5. Using mathematics
and computational
thinking
6. Developing
explanations and
designing solutions
7. Engaging in argument
from evidence
8. Obtaining, evaluating,
and communicating
information
Informs Explanations, Arguments,
and Communication
1. Ask questions and
defining problems
2. Developing and using
models
3. Planning and carrying
out investigations
4. Analyzing and
interpreting data
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5. Using mathematics
and computational
thinking
6. Developing
explanations and
designing solutions
7. Engaging in argument
from evidence
8. Obtaining, evaluating,
and communicating
information
A Central Practice!
1. Ask questions and
defining problems
2. Developing and using
models
3. Planning and carrying
out investigations
4. Analyzing and
interpreting data
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5. Using mathematics
and computational
thinking
6. Developing
explanations and
designing solutions
7. Engaging in argument
from evidence
8. Obtaining, evaluating,
and communicating
information
Questions?
Submit your questions and ideas via the chat.
REMINDERS
• To turn off notifications of other participants arriving go to:
Edit -> Preferences -> General -> Visual notifications
• You can minimize OR detach and expand chat panel
• Continue the discussion in the Community Forums
http://learningcenter.nsta.org/discuss
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Tools for Analysis
• Tables
• Permit major features of data to be summarized in accessible
form
• Graphs
• Visually summarize the data
• Mathematics
• Expressing relationships between different variables in the
dataset
• Computer-based “visualization” tools
• Allow data to be displayed in a variety of forms
• Standard statistical techniques
• Help to reduce the effect of error
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What Scientists Look For in Data
• Patterns
• Significant features
• Relationships
• Trends
• Anomalies
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Poll: How Much Do Students
Work With Data?
• How often do students work with data in your classroom?
A. At least once a week
B. Once a month or so
C.
Several times a year
D. A few times a year
E.
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Never
Progression Across Grades
• Increased sophistication and fluency of the practice of
analyzing and interpreting data, and the relationship to other
practices, as students move through k-12 science
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Analyze and Interpret Data:
Grades K-2
• Focus on collecting, recording, and sharing observations
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Analyze and Interpret Data:
Grades K-2
• Focus on collecting, recording, and sharing observations
• Share observations
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Analyze and Interpret Data:
Grades K-2
• Focus on collecting, recording, and sharing observations
• Share observations
• Make measurements
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Analyze and Interpret Data:
Grades K-2
• Focus on collecting, recording, and sharing observations
• Share observations
• Make measurements
• Note patterns and relationships
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Analyze and Interpret Data:
Grades K-2
• Focus on collecting, recording, and sharing observations
• Share observations
• Make measurements
• Note patterns and relationships
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Analyze and Interpret Data:
Grades 3-5
• Emphasize more quantitative approaches, and multiple trials
of qualitative data
• Display data in tables and graphs
• Compare data across different groups
• Evaluate claims of cause and effect
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Example: Investigating Advantage
of Machines
• Question: How can machines move things that I can’t?
Lesson Sequence
1. Incline plane – each group takes one measurement of force
and distance for each set-up (one trial). Group
measurements are pooled into one class data table. Class
discussion of consistency and identification of outliers.
Teacher models how to average the data, and how to create
comparative bar graphs. Through discussion, class generates
interpretations of the analysis by developing initial “class
rule” for the relationship.
?
?
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Example: Investigating Advantage
of Machines
2. Lever – Each group conducts one trial of each set-up,
gathering both force and distance data. The data from each
group is again compiled into class data tables, with a second
conversation about consistency and identification of
outliers. However, then groups work individually to average
and graph the data. These are shared and compared, and
the class writes interpretations. They revise their initial
“class rule” of the relationship.
?
?
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Example: Investigating Advantage
of Machines
3. Pulley – Groups now conduct three trials of each set-up and
average their own data. Groups then create comparative
bar graphs of both force and distance that include data from
the three different configurations. The graphs are compared
and discussed. Student groups write their own
interpretations of the graphs. The class as a whole revises
the “class rule” to include the importance of changing the
direction of applied force.
Distance
?
?
Force (N)
Distance (m)
?
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Force
Without
Pulley
With
Fixed
Pulley
With
Free
Pulley
Without
Pulley
With
Fixed
Pulley
With
Free
Pulley
Analyze and Interpret Data:
Grades 6-8
• Increased quantitative analyses in investigation, distinguishing
causation vs. correlation, basic statistical techniques
• Use mean, median, mode and variability to describe data
• Identify linear and non-linear relationships using graphs
• Consider limitations (e.g., measurement error) and ways to
increase precision (e.g., multiple trials)
• Use graphical displays of large data sets to analyze temporal and
spatial relationships
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Example: Investigating Motion
• Question: Why do I need to wear a helmet when I ride my
bike?
• Concepts: Relationships between mass,
velocity, acceleration, force
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Distance-Time Graphs
• Moving away, fast and slow
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• Moving toward, fast and
slow
Velocity-Time Graphs
• Accelerating down the ramp,
higher and higher
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• Investigating acceleration as
mass is increased
Example: Investigating Earth Systems
Question: Where
is all the water?
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Example: Investigating Earth Systems
Amount of
precipitation
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Mean
population
density
Description of locations
All values
31.6
-
Greater than
300 cm/year
47.4
Near the Equator – Indonesia, West
Africa, Brazil, and Central America
Less than 10
cm/yr
9.6
Sahara Desert in Africa, Andes
Mountains in South America,
Himalaya Mountains in Asia
Question: Where
is all the water?
• Usefulness of the
tool for looking
at patterns of
data across the
Earth
Analyze and Interpret Data:
Grades 9-12
• More detailed statistical analyses and use of computational
models to generate and analyze data
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•
Use tools such as computational or mathematical models to generate
and analyze data for scientific claims or optimal design solutions
•
Consider limitations to analysis (e.g., sample size, measurement error)
•
Determine function fits to data (slope, intercept, and correlation
coefficient)
•
Triangulation across types of data sets to examine consistency of
measurement and observation
Example: Water Quality Investigation
• Visual images and
water quality tests
(pH, DO, turbidity)
• Analyze through
tables, graphs, and
comparison of
images and
archival data
• Triangulate
interpretations
across multiple
data sources
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Computational Models
EdGCM
Educational Global Climate Model
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Computational Models
EdGCM
Educational Global Climate Model
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Example: Engineering Energy Efficiency
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• Question: How do we design
an efficient solar house?
• Design and test model houses
to improve energy efficiency,
using sensors, CAD tools, and
infrared imaging probes
• Power, energy, heat transfer,
thermal equilibrium, specific
heat, conduction, convection,
radiation, heat capacity, solar
energy…
Questions?
Submit your questions and ideas via the chat.
REMINDERS
• To turn off notifications of other participants arriving go to:
Edit -> Preferences -> General -> Visual notifications
• You can minimize OR detach and expand chat panel
• Continue the discussion in the Community Forums
http://learningcenter.nsta.org/discuss
53
Common Challenges
• Under-analyzing the data
• Connect claim to question irrespective of the data
• Over-analyzing the data
• One data point does not make a claim
• Awareness and accountability of errors
• Precision of measurement
• Selection of appropriate tool(s) and procedure(s)
• Usually more than one analysis needed to address the question
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Assessment
Goal: Awareness of relationship between data, questions,
analysis tools, and concepts
• Ask students to explain their process
• How did they get to that interpretation?
• Ask students to provide a rationale for the analysis tool and
approach they used
• Why did you choose to use that kind of graph?
• Why did you average?
• What other ways could you have looked at it? Why did you select
this way?
• Ask students about other considerations during analysis
• What could be sources of error in your investigation?
• How confident are you in these findings? What things make you
unsure?
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Conclusion
• Kids MUST work with data!
• The focus is on the connection between the question, data,
and claim
• Statistics, graphs, and other mathematics are TOOLS for analyzing
and interpreting data, NOT the goal
• The practice is complex and must build over multiple
supported experiences across years
• Lots of potential applications across the science curriculum
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Thank You!
Contact information:
Professor Ann Rivet
Program in Science Education
Teachers College, Columbia University
rivet@tc.columbia.edu
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Questions?
Submit your questions and ideas via the chat.
REMINDERS
• To turn off notifications of other participants arriving go to:
Edit -> Preferences -> General -> Visual notifications
• You can minimize OR detach and expand chat panel
• Continue the discussion in the Community Forums
http://learningcenter.nsta.org/discuss
58
NSTA Website (nsta.org/ngss)
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Upcoming Web Seminars on Practices
Date
Topic
Speaker
1
9/11
Asking Questions and Defining Problems
Brian Reiser
2
9/25
Developing and Using Models
Christina Schwarz and
CindyPassmore
3
10/9
Planning and Carrying Out Investigations
Rick Duschl
4
10/23
Analyzing and Interpreting Data
Ann Rivet
5
11/6
Using Mathematics and Computational Thinking
Robert Mayes and
Bryan Shader
6
11/20
Constructing Explanations and Designing
Solutions
Katherine McNeill and
Leema Berland
7
12/4
Engaging in Argument from Evidence
Joe Krajcik
8
12/18
Obtaining, Evaluating and Communicating
Information
Philip Bell, Leah Bricker, and
Katie Van Horne
All take place on Tuesdays from 6:30-8:00 pm ET
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Next Web Seminar
November 6 (two weeks from today)
Using Mathematics and Computational Thinking
Teachers will learn more about:
the importance of mathematics and computation as
fundamental tools for representing physical variables and
their relationships;
how tools are used for a range of tasks, including
constructing simulations; statistically analyzing data; and
recognizing, expressing, and applying quantitative
relationships;
mathematical and computational approaches that enable
Presenters:
scientists and engineers to predict the behavior of
Robert Mayes &
systems and test the validity of such predictions
Bryan Shader
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Participants must either:
Attend the live presentation, complete the survey at the end of
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NSTA, or
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question for that particular webinar.
In addition, all participants must complete a 500 word reflection essay.
The total cost is $165.
For information on the course requirements, as well as registration and
payment information visit www.ship.edu/extended/NSTA
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Community Forums
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NSTA Area Conferences
These conferences will include a number of sessions
about the K–12 Framework and the highly anticipated
Next Generation Science Standards.
Among the sessions will be an NSTA sponsored session
focusing on the Scientific and Engineering Practices.
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to the Framework
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about the Framework
and the Standards
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Conferences and Programs
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