Working with Data:
A Learning Sciences Perspective
Daniel C. Edelson
WorldWatcher Project
School of Education & Social Policy and
Computer Science Department
Northwestern University
Supported by the National Science Foundation under grants no. REC9730377, REC-9720663, ESI-9720687, and REC-0087751
Collaborators
Faculty: Louis Gomez, Roy Pea, Brian Reiser, Bruce
Sherin
Research Scientists: Duane Griffin, Michael Taber, Susan
Marshall
Graduate students: Douglas Gordin, Matthew Brown,
Gabrielle Matese, Virginia Pitts
Curriculum development professionals: Kylene Chinsio,
Adam Tarnoff, Michael Lach, Kathleen Schwille
Programmers: Eric Russell, Peter Moore, Brian Clark
The Big Disconnects in Education
Current methods do not match our goals
Current education emphasizes
• Memorization of facts
• Passive reception of information (listening, reading)
• Practicing simple skills out of context
We want citizens that can
• Perform complex tasks
• Gather and synthesize information
• Communicate with others
The Big Disconnects in Education (2)
Current methods do not match what people do in the real
world:
For example, science is...
• Asking questions.
• Constructing explanations.
• Collecting evidence.
• Engaging in a dialogue (arguing, listening, asking).
• Applying knowledge to meaningful problems.
The Big Disconnects in Education (3)
Current methods do not match what we know about learning:
• Students must be engaged.
• Content (facts) and Process (skills) learning must be
integrated.
• Students are not blank slates. Prior knowledge and
outside influences must be accounted for.
• Things learned divorced from meaningful context does
not transfer to contexts where they are useful.
So, why do we want students to work with
data?
Provide an authentic experience
of science
Build skills (representative,
quantitative, analytical)
Allow them to explore content
phenomena in ways that…
• Supplement other
experiences of those
phenomena
• Allow them to explore
phenomena on scales too
large or too small to be
experienced directly
The challenges of enabling students to work
with data…
Availability (the easy one)
Accessibility for students and teachers
• of tools
• of data
Design of effective learning activities
QuickTime™ and a
Sorenson Video decompressor
are needed to see this picture.
There’s a learning sciences research and
development program here:
• Tool design
• Data library design
• Learning activity design
2:00
An approach to design of learning activities:
Learning-for-Use
The Goal
• help students to develop “useful” knowledge—knowledge that
will be retrieved and applied when relevant in the future
Model of Learning
• describes how useful knowledge can be developed.
• based on research from cognitive science
Design Framework
• provides guidelines for teachers and designers
• fosters useful understanding
Learning-for-Use Model (from JRST 2001)
Underlying principles from cognitive science research
(e.g., How People Learn):
1. Learning takes place through the construction and
modification of knowledge structures.
2. Knowledge construction is a goal-directed process that
is guided by a combination of conscious and
unconscious understanding goals.
3. The circumstances in which knowledge is constructed
and subsequently used determine its accessibility for
future use.
4. Knowledge must be constructed in a form that supports
use before it can be applied.
Three stages in Learning-for-Use
Motivate specific learning objectives
…based on perceived need for and usefulness of knowledge or
skills
Construct knowledge
…from experience and instruction
Organize knowledge for use
…for accessibility (retrieval) and usability (application)
Motivate
Step
Name
Motivate Create
task
demand
Elic it
curiosit y
Description
Desired Effect
Studen ts are
presented wit h a task
that requir es new
unde rstand ing.
Creates a
perceived need
for new
know ledge or
skills .
Studen ts are placed
in a situation that
elicit curiosity by
revea li ng an
unexpe cted gap in
their unde rstanding .
Exampl es
Project-based science
(Krajcik et al. ), goa lbased scena rios
(Schank et al.) ,
ancho red instruction
(CGTV), problembased learning
(Barrows et al. )
Studen t becomes Discrepant even ts,
aware of limi ts
expe ctation violations
of know ledge
(Schank) , bench mark
and need fo r
lessons (Minstrell)
new know ledge
to address those
limi ts.
Construct Understanding
Step
Name
Construct Dir ect
expe rienc e
Indirect
expe rienc e
Modeling
Instruction
Expl anation
Description
Desired Effect
Exampl es
Studen ts are
provided wit h
direct physical
experience or
observation of
pheno mena .
Studen ts hea r
about, view, or
read about
pheno mena .
Studen ts cons truct
know ledge structures
encod ing the attributes
and relation ships that
unde rli e the
pheno mena .
Hand s-on
activiti es
Video
Studen ts observe
ano ther person
performi ng a task.
Studen ts cons truct
know ledge structures
that encode th e
Studen t are told or elements of a process
read about how to
perform a task.
Studen ts are
provided wit h
exp lana tions of
pheno mena or
processes.
Studen ts cons truct
know ledge structures
that encode caus al
information behind the
relationsh ips among
pheno mena or
elements of a process.
Demonstration
Organize for Use
Step
Name
Orga nize
for U se
Description
Desired Effect
Exampl es
Practice Studen ts use
componen ts of ne w
unde rstand ing
outside of
motivating con text.
Studen ts cons truct procedura l
representations from
declarative representations ,
reinfo rce unde rstand ing,
expo se limit ations and n eed
for further know le dge
construc tion
Proble m
set
Apply
Studen ts apply
unde rstand ing in
context.
Project
Reflect
Studen ts artic ulate
wha t they hav e
learned and wh at the
bounda ries of that
unde rstand ing are.
Studen ts deve lop ind ices fo r
retrieval, cons truct
procedural representation s
from declarative, reinfo rce
unde rstand ing, expos e
limi tation s and need for
further know ledge
construc tion
Knowledge is re-index ed for
retrieval, expose lim itations
and need fo r further
know ledge construction
Reflective
discuss ion
What does LfU look like for each learning
objective?
Motivate
Create Demand
or
Elicit Curiosity
Reflect
Construct
Organize
Balance of direct experience, indirect experience,
modeling, instruction, and explanation
Reflect
Practice
Apply
Reflect
LfU for related learning objectives
Aggregate
objective
Motivate
Construct
Motivate
Construct
Learning
Objective 1
Organize
•
•
Motivate
Organize
Construct
Organize
Unit level
Activity level
Learning
Objective N
LfU for related learning objectives
Learning
Objective 1
Learning
Objective 1(a)
Motivate
Construct
=
Learning
Objective 1(b)
Motivate
Motivate
Construct
=
Organize
Organize
Construct
Organize
Where does working with data fit in?
Motivate
• Elicit curiosity: observe surprising patterns in data
(discrepant event)
Construct
• Experience: Learn about phenomenon by seeing
patterns in data
Organize
• Apply: Use what has been learned to explain or predict
patterns in data
Scenario-based inquiry learning
A specific form of Learning-for-Use
A scenario (or project) provides the context that:
1. Creates demand
2. Provides opportunity to apply new knowledge
A substantial portion of knowledge construction occurs
through inquiry.
An example: Planetary Forecaster
6-8 week middle school unit
Content objectives: relationship between physical geography
and temperature
• Latitude (curvature)
• Time of year (tilt)
• Land/Water (specific heat)
• Elevation (pressure/density)
Process objectives:
• data visualization and analysis
• hypothesis formation and revision
• argument from evidence
Create Demand: A Letter from the
International Space Agency
Scientists have discovered a new
planet that is very similar to Earth.
We want to plan a mission to
colonize it…
Which portions of it would be
habitable?
Students study relationship
between physical geography and
climate on Earth to forecast
climate for Planet Y.
Structure of the project
Develop list of initial hypotheses from activity that elicits students’ prior
conceptions.
Of those, investigate three factors:
• Latitude
• Land/Water
• Elevation
Repeating sequence of:
• Find pattern in Earth data
• Investigate pattern in hands-on lab
• Quantify pattern in Earth data
• Apply it to “planetary forecast”
Curvature
Study Earth:
• Average surface
temperature
• Incoming solar energy
Lab:
• Penlight area on paper as
angle changes
Tilt
Study Earth:
• Seasonal temperatures
• Seasonal insolation
Lab:
• Penlights on tilted globes
Land/Water
Investigate Earth:
• Land averages
• Water averages
…at same latitudes
Lab:
Soil vs. H2O heating under
shop lamp and cooling
Elevation
Investigating Earth:
• Elevation and Temperature
Lab:
• Rapid expansion (e.g.,
aerosol can)
Final Planetary Forecast
July
8:00
Planetary Forecaster and Learning-for-Use
Motivate
• Create demand based on planetary forecast scenario
Construct
• Direct experience through inquiry activities
• Explanation through instruction
Organize
• Reflection through frequent discussions
• Application in the context of planetary forecast
Use of data: To support inquiry activities and to support application task.
Take away
Learning Sciences research indicates need for complete
learning process.
The current system tends to focus on knowledge construction
to the detriment of other two steps.
Earth and Environmental Science problems can provide
meaningful context for learning-for-use through scenariobased inquiry learning.
Activities that use data can contribute to all three stages of
learning.
More info
The WorldWatcher Project — http://www.worldwatcher.org
Supported in part by the National Science Foundation under
grants no. RED-9453715 , ESI-9720687, DGE-9714534.
Affiliated with:
The Center for Learning Technologies in Urban Schools (LeTUS) —
http://www.letus.org
The Center for Instructional Materials in Science (CIMS)
http://www.sciencematerialscenter.org
Planetary Forecaster: http://www.letus.northwestern.edu/projects/pf