Teaching Science for
Motivation and Understanding
Discussion with Knowles Fellows
November, 2003
Andy Anderson and Gail Richmond
Issues You Would Like to Discuss
Watching Jim Minstrell Teach
about Newton’s Laws of Motion
• What do you notice that would cause you
to change your answers to some of the
questions about moving objects?
• What did you notice that would cause you
to change or add to your answers to
questions about problems of practice?
• What did Jim Minstrell need to know in
order to teach this way?
Problems of Practice
in Science Teaching
1. Science content: Goals and activities for
student learning
2. Students and assessment
3. Classroom learning environments and
teaching strategies
4. Professional resources and relationships
Niels Bohr on Scientific Reasoning
The task of science is both to extend our
experience and reduce it to order, and this task
represents various aspects, inseparably connected
with each other. Only by experience itself do we
come to recognize those laws which grant us a
comprehensive view of the diversity of
phenomena. As our knowledge becomes wider we
must always be prepared, therefore, to expect
alterations in the points of view best suited for the
ordering of our experience.
Extending Experience and
Reducing It to Order
Deepest scientific
theories
Sense Experience
Extending
experience
Reducing experience
to order
Pursuing the “turtles
all the way down”
Piaget on Children’s First Inquiry:
Developing a “Theory of Objects”
• Babies: Selective attention to faces,
motion, unusual stimuli; no continuing
interest when something disappears from
view
• Peek-a-boo: Experience with objects
disappearing and reappearing;
encouraging adults to play
• Hide and seek: Finding hidden objects
• Object permanence as eventual outcome
Piaget on Conservation of
Liquids
• Extending experience by pouring liquids
from one container to another
• Early focus on only width or depth: More
liquid in deeper container
• Later: coordinating thinking about width
and depth
• Final: conservation of liquids; volume is
always the same regardless of container
Experientially Real Objects,
Systems, and Phenomena
Edge of accepted
experience
Experiences taken for
granted as “real”
Explanations for
those experiences
Deepest scientific
theories
Sense Experience
Extending
experience
Reducing experience
to order
Development of Knowledge
• Extending experience: Adding to our stock of
“experientially real” objects, systems, and
phenomena
– Adding new sense experiences
– Adding vicarious sense experiences (e.g., pictures, video)
– Adding believable, experientially real data (e.g., measurements,
carefully recorded observations)
• Reducing experience to order: Developing new
and better models and theories
– Conceptual change: Replacing old theories with new ones that account
for more data
– Converting previous “theories” to taken-for-granted experientially real
objects, systems, phenomena (e.g., existence of objects, conservation
of liquid volume)
Why Extending Our
Experience and Reducing It to
Order Isn’t Always Science
Multiple Sense-making Strategies
What Counts as Experientially
Real?
• Everyday judgments: Seeing (or hearing,
touching, feeling) is believing
– Vividness and immediacy of experience
– Confirmation by peer group
• Scientific judgments: Creating data from
experience
–
–
–
–
Reproducibility
Precision
Provenance of records
Confirmation by skeptical observers
Times When Everyday and
Scientific Judgments Differ
•
•
•
•
Rumors
News, history
Religious experience
Data collected with complicated
instruments
• Data presented in difficult-to-understand
formats
What Counts as a
Good Model or Theory?
• Procedural display: Whatever it takes to get a
good grade
• Practical reasoning: Whatever it takes to get
practical results (including inventing things)
• Narrative/metaphorical reasoning: Stories or
metaphors that bring coherence to our
experiences (including news, history)
• Model-based reasoning: Models that account for
all relevant data in testable, parsimonious ways
– “Unbroken chain of connections” from data to models
– Consistency with other models and theories
Model-based Reasoning:
Scientific Inquiry and Application
Inquiry (constructing explanations from patterns in experience)
Experiences
(data,
phenomena,
systems,
objects,
evernts
Patterns
(generalizations,
laws)
Explanations
(hypotheses,
models,
theories)
Application (Using scientific patterns and theories to describe, explain, predict, design )
Content Example:
Newton’s Laws
What does it mean to
“understand” Newton’s Laws?
Historical Sense-making
Strategies for Explaining Motion
• Aristotle, Ptolemy, and Aquinas
• Setting the stage for Galileo: Impetus
theorists and Copernicus
• Galileo
• Newton
Aristotle, Ptolemy, and Aquinas
• Practical reasoning: Moving objects with simple
machines, throwing,pushing, bows and arrows
• Narrative reasoning: Where and why do objects
move
– Animate objects (animals) move on their own
– Natural motion: Inanimate objects tend toward their own spheres (earth,
water, air, fire)
– Violent motion: Animals can impart motion to inanimate objects
– Heavenly objects are kept in motion by the Prime Mover
– Aquinas: Prime Mover is Christian God
• Model-based reasoning: Ptolemaic system
explains motions of sun, moon, planets
Setting the stage for Galileo
• Practical reasoning: Siege engines (catapults,
trebuchets, cannon); accuracy depends on
direction and speed (not just personal skill)
• Narrative reasoning: Protestant reformation
emphasizes personal God rather than distant
Prime Mover
• Model-based reasoning: Copernicus suggests
sun-centered model that fits observations better
Galileo
• Practical reasoning: Inventing better telescopes,
measuring speed and direction of rolling and
falling objects
• Narrative reasoning:
– Challenging Ptolemy and Aquinas: Copernicus’ model
is true, not just way to calculate positions
– Telescopic observations of corruptible heavens
• Model-based reasoning: Mathematical
predictions of speed of falling objects
Newton
• Practical reasoning: Mathematical predictions of
trajectories (models improve practical reasoning
rather than the other way around)
• Narrative reasoning:
– Anti-Trinitarian Biblical text criticism: God does not
intervene in everyday events
– Newton’s apple: The apple and the moon are
following the same laws
• Model-based reasoning: Newton’s Laws of
motion and universal gravitation
Newton’s First Law
Traditional wording
Contrasting Newton
and Aristotle
• Every object continues in
a state of rest, or of
motion in a straight line at
a constant speed, unless
it is compelled to change
that state by unbalanced
forces exerted on it.
• Motion doesn’t need to be
explained, only changes
in speed or direction
(velocity).
• Necessity: No forces or
balanced forces always
mean no change in speed
or direction, and vice
versa.
Newton’s Second Law
Traditional wording
• The acceleration
produced by a net force
on an object is directly
proportional to the
magnitude of the net
force, is in the same
direction as the net force,
and is inversely
proportional to the mass
of the object (F = ma).
Contrasting Newton
and Aristotle
• Forces do not cause
motion. Instead they
cause acceleration, or
change in speed or
direction (i.e., velocity).
Newton’s Third Law
Traditional wording
• For every action there
is an equal and
opposite reaction.
Contrasting Newton
and Aristotle
• Forces always come
in pairs. When A
exerts a force on B, B
exerts an equal and
opposite force on A.
This does not mean
that the forces on A or
B are balanced.
Aristotelian and Newtonian
Answers to Questions
1. Book on the table
•
•
Aristotelian: Table blocks book from falling
Newtonian: Table exerts an upward force
that balances the downward force of gravity
2. Coin in the air
•
•
Aristotelian: Coin’s upward flight is sustained
by a “force from the hand”
Newtonian: Continuing upward motion
doesn’t need to be explained. Unbalanced
force of gravity slows the coin down.
Aristotelian and Newtonian
Answers to Questions (cont)
3. Cart at constant velocity
•
•
Aristotelian: Continued motion requires
continued force
Newtonian: Net force is 0 for motion at
constant speed and direction
4. Accelerating cart
•
•
Aristotelian: Increasing motion requires
increasing force
Newtonian: F = ma. Constant acceleration
requires a constant net force
Problems of Practice
in Science Teaching
1. Science content: Goals and activities for
student learning
2. Students and assessment
3. Classroom learning environments and
teaching strategies
4. Professional resources and relationships
Purposes for Classroom
Assessment
• Understanding your students
• Helping your students to assess and
improve their own understanding
• Grading
Criteria for Assessments that
Help You Understand Students
• Connection to goals: The questions address
important objectives you have for student learning
• Interesting wrong answers: Even incorrect answers
reveal students' thinking
• Insight into students’ sense-making: The students’
answers help you understand how they make sense
of the world, not just where their knowledge of
science is weak.
• Starting a dialogue with students: The questions help
you to start discussions with students where they can
compare their ideas with scientific ideas.
Types of questions that
produce interesting wrong
answers
Backwards reasoning
• If --- is the answer, then what was the question?
• What question were scientists trying to answer:
– …when they discovered photosynthesis? (e.g., why
do plants need light?)
– …when they discovered atomic theory (e.g., why do
elements always combine in certain proportions?)
Familiar situations
• Getting students’ theories about familiar
examples.
– What are the forces on a coin flipped into the air?
– Are your eyes the same color as your mother’s? How
do you think that happened?
– What’s inside the bubbles of boiling water?
Connecting different
representations
• Seeing what happens when students represent
the same example in different ways.
– Draw a picture of what is happening to the atoms of
NaCl as solid salt dissolves in water.
– Show how the light rays travel that enable a person to
see a tree as she looks out the window.
Types of Representations
Most concr ete
Observations of actual phenomena
 Close to expe rience s, obse rvations , data
 Mis concep tions (matching inco rrect
Ana logous pheno mena (e.g., conden sation
models to the expe rience ) are a key
on glass compared to cloud formation)
problem
Phys ical models
Simulation s
Drawings , diagrams
Data tables
Graphs show ing data or relationsh ips
Most abstract
 Close to models or theo ries
 Empty symbol manipul ation is a key
problem
Verbal exp lana tions
Formulas and equa tions
Short answer + explanation
• Ask students to make a choice or draw
arrows, then explain their reasoning.
– Does food normally move up or down a
plant’s stem? Explain your reasoning.
– Will the “ashes” left after magnesium burns
weigh more or less than the original metal?
Explain your reasoning.
Use misconceptions research
• Ask questions that will reveal common
misconceptions (as reported in research).
– What question would reveal a belief that liquids
disappear when they evaporate?
– What questions would reveal a belief that plants get
their food from the soil?
– What question would reveal a belief that the phases
of the moon are caused by the earth’s shadow?
Comparing examples or
concepts
• Ask students to compare and contrast
different real world examples or familiar
terms
– heat vs. temperature
– force vs. momentum
– Current vs.voltage
– Green plants vs. fungi
– Volcanoes vs. other mountains
Critique of suggested responses
• Ask students whether they agree or disagree
with responses that reveal misconceptions, and
why.
– My friend says that sunlight is food for plants. Do you
agree? Why or why not?
– My friend says that when water evaporates, the water
vapor weighs just as much as the liquid water. Do
you agree? Why or why not?
Teaching Newton’s Laws to
(Aristotelian) High School Students
• Describing motion: Focusing on speed and direction
rather than destination and reason for motion
• Negotiating standards for what counts as evidence
(“experientially real”)
• Extending experience: Collecting data in situations
where Aristotle’s rules break down
• Questioning students’ narrative and practical knowledge
• Model-based reasoning: Finding consistent,
parsimonious explanations that fit all the data
• Quantitative rigor: Using models to make precise,
quantitative predictions
Watching Jim Minstrell Teach
Again
How does he address each of the
challenges to his students’
learning with understanding?
Problems of Practice
in Science Teaching
1. Science content: Goals and activities for
student learning
2. Students and assessment
3. Classroom learning environments and
teaching strategies
4. Professional resources and relationships
Approaches to Teaching Science (from Mark Olson)
Carol: Science Curriculum
as a Progression of Models
Jennifer: Science Curriculum
as “Chapters in the Story”
Inquiry (constructing explanations from patterns in experience)
Experiences
(data,
phenomena,
systems,
objects,
evernts
Patterns
(generalizations,
laws)
Explanations
(hypotheses,
models,
theories)
Application (Using scientific patterns and theories to describe, explain, predict, design )
Bar magnets
Example
Electromagnets
Example
Magnetic Induction
Example
Ferromagnetism
Example
Magnetic Domains
Example
Inquiry (constructing explanations from patterns in experience)
Experiences
(data,
phenomena,
systems,
objects,
evernts
Patterns
(generalizations,
laws)
Explanations
(hypotheses,
models,
theories)
Application (Using scientific patterns and theories to describe, explain, predict, design )
Inquiry (constructing explanations from patterns in experience)
Experiences
(data,
phenomena,
systems,
objects,
evernts
Patterns
(generalizations,
laws)
Explanations
(hypotheses,
models,
theories)
Application (Using scientific patterns and theories to describe, explain, predict, design )
Problems with Carol’s approach:
Problems with Jennifer’s Approach:
oversimplified models
leaves out the good part
not part of Kuhn’s normal science
students can only tell story
no model-based reasoning
students forget what they studied
General Teaching Strategies
• Covering content: Telling the story with
examples and expecting students to tell it back
(Jennifer’s approach)
– Leads to narrative understanding or procedural
display
• Learning cycles focusing on application of
model-based reasoning (Carol’s approach)
• Inquiry cycles focusing on developing new
models through reasoning about data (Minstrell’s
approach)
Classroom Environments for
Learning and Inquiry Cycles
• Personal and emotional safety for
students, including moderate levels of risk
and ambiguity
• Motivating students to learn: Expectancy
times value
• Social norms for participation and
communication
Model-based Reasoning:
Scientific Inquiry and Application
Inquiry (constructing explanations from patterns in experience)
Experiences
(data,
phenomena,
systems,
objects,
evernts
Patterns
(generalizations,
laws)
Explanations
(hypotheses,
models,
theories)
Application (Using scientific patterns and theories to describe, explain, predict, design )
Learning Cycles
Transfer of Responsibility in the Learning Cycle
Stages in the Learning Cycle
• Establishing the problem: Connecting with prior
knowledge and establishing motivation to learn
• Modeling: Exposing learners to comprehensible
models of good practice
• Coaching: Providing opportunities for practice
with scaffolding or support
• Fading: Gradually removing support until
learners engage in the practice independently
• Maintenance: Continuing practice after initial
learning is over
Prerequisites for Successful
Learning Cycles (Focused on
Application)
•
•
•
Model or theory that you want students
to be able to apply
Set of real-world examples
Pattern for students to follow in applying
theory to examples
Important Points about Learning Cycles
• Assessing student thinking. Include embedded
assessment that will help you and your students
understand their ideas and practices—both
correct and incorrect.
• Keeping the objective whole. Students work
through several examples where they see or do
the whole task.
• Learning as transfer of responsibility. Students
take more responsibility for doing the task.
• Scaffolding is temporary. Learning cycles are
complete when students can accomplish the
objective on their own.
Inquiry Cycles
Essential Features of
Classroom Inquiry
1. Questions. Students engage in scientifically oriented
questions.
2. Evidence: Data and patterns. Students respond to
questions by looking for patterns in data—their own
experiences and/or data supplied by the teacher.
3. Students’ explanations. Students formulate
explanations from evidence.
4. Scientific theories or models. Students compare
their explanations to explanations based on canonical
scientific models or theories
5. Communication: Argumentation and justification.
Students explain and justify their methods, results, and
conclusions.
Prerequisites for Successful
Inquiry Cycles
• Experiences: “Experientially real” data
from students’ personal experience,
classroom observations, simulations, or
archived data sets
• Pattern(s) in experience that students will
be able to see
• Theory or model that explains patterns
Lab Activities that Are Not Inquiry
1. Confirmation labs: students follow directions. Purposes:
 Practicing lab techniques
 Confirming accuracy of laws and theories
2. “Consumer reports” labs: students compare products or practices to
find the best one. Purposes:
 Reasoning about and developing experimental techniques
 Practicing evidence-based argumentation and decision making
3. Explanation labs: students observe phenomena, then use models
and theories to explain what they saw. Purposes:
 Connecting representations at different levels of abstraction
 Practicing detailed explanations of real-world examples
4. Design labs (engineering inquiry): students use scientific principles to
design systems that accomplish specific purposes (e.g., egg drop
lab, building bridges, maximizing crop yield). Purposes:
 Applying scientific theories to practical design problem.
 Building engineering skills
Inquiry Activities
1. Naturalistic or field inquiry: students look for patterns in observations
that they make. Examples:
 Geological or ecological field work
 Astronomical observations, such as sun and moon
2. Experimental inquiry: students create new experience in the lab,
often with planned variation. Examples:
 Systematically observing products of different reactions
 Comparing plant growth under different conditions
3. Data analysis: students look for and explain patterns in
“experientially real” data sets that are given to them. Examples:
 Looking for patterns in weather or geographic data
 Explaining reported results of dangerous experiments
4. Simulations: students look for patterns and explain results in “virtual
worlds” that imitate reality. Examples:
 Models of moving objects or electrical circuits
 Ecosystem models
Learning and Inquiry Cycles
• Prerequisites
•
o “Experientially real” data
o Pattern(s) that students
will be able to see
o Theory or model that
explains patterns
o Model or theory
o Set of real-world examples
o Pattern for students to
follow in applying theory to
examples
• Stages or activities
o
o
o
o
o
Establishing the problem
Modeling
Coaching
Fading
Maintenance
Prerequisites
•
Activities
o Questions
o Evidence: Data and
patterns
o Students’ explanations
o Scientific explanations
o Communication
Jim Minstrell’s Teaching Strategies
• Classroom environment
• Learning cycles
• Inquiry cycles
Problems of Practice
in Science Teaching
1. Science content: Goals and activities for
student learning
2. Students and assessment
3. Classroom learning environments and
teaching strategies
4. Professional resources and relationships
Key Issues in Professional
Resources and Relationships
• Finding, using, and adapting resources
• Learning from imperfect mentors
• Using assessment to learn from
experience
• Explaining yourself and your practice to
colleagues, administrators, parents
Revisiting Questions
What else do we need to discuss?
Contact Us
• Andy Anderson
– andya@msu.edu
– 319A Erickson Hall, Michigan State University, East
Lansing, MI 48824
– 517-432-4648
• Gail Richmond
– gailr@msu.edu
– 319 Erickson Hall, Michigan State University, East
Lansing, MI 48824
– 517-432-4854
• Website:
http://SciRes.educ.msu.edu/TEScience/index.htm