Transcript Class 3+ `isms and Philosophies

Science of Learning
Overview of Cognitive Science for
Teachers
Psychological/Cognitive
Revolution
• Freud (1890)
• Kohler’s monkeys(1908)
• 1900-1960: Two directions
– US: Behaviorism
– Europe: Gestalt
• 1960’s- Cognitive Science
– Formerly artificial intelligence
– Only Nobel prize for psychology
Behaviorism
• Pavlov, Watson, Thorndike & Skinner
• Create a science of behavior
– No mention of thinking
– Only visible factors considered
• Reinforcement of desired behaviors
– Treat all students the same
– Feedback is most important
• Teaching machines
Gestalt Psychology
• Kohler, Wertheimer, Piaget
• Focus on Perception
– Illusions suggest some of the inner processes
• Brain as an active processor
– Not simply taking in information
– Fish is fish
– http://faculty.uca.edu/~lglenn/gestaltimages.htm
• Research on understanding
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Constructivsm
• Jean Piaget (1940-70s)
• Knowledge must be constructed
– Assimilation
– Accomodation
– Ignore/segment knowledge
• Looked at students mistakes and developed theory of
development
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Sensorimotor stage
Preoperational stage
Concrete operational stage
Formal operations
Problematic Initial ideas
• Earth is flat
• When you throw a ball it naturally slows down
and stops
• It is much less likely to get ‘heads’ four times
in a row than to get h-t-h-t
• Astronaut Question
Astronaut question
The astronaut is walking on the
moon when he lets go of
his pen. Does the pen:
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B.
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Fall to the moon surface
Float up into the sky
Float in place
Misconceptions?
• All ideas are the product of some experience
• Students initial ideas are the building blocks
of future ideas
• If misconceptions are not addressed many
students will retain them after instruction
• How to you promote “conceptual change”
(aka accommodation that involves major
change of ideas)?
Cognitive Science
• Understanding thinking using many
perspectives:
– Psychology
– Computer Science
– Linguistics
– Anthropology
Artificial Intelligence
• Starts with Allan Newell and Herb Simon
(1956) General Problem Solver
• Computers can solve well structured
problems (e.g. geometry, chess)
– Identify the goal
– Search the “solution space”
– Hill climbing
• Most problems are not well structured
• Later efforts focus on gestalt type approaches
(neural nets)
Contemporary Learning
Theory
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Constructivism - Role of prior knowledge
Metacognition - Control over thinking process
Expert/Novice - How do experts think
Transfer - Evidence of understanding
Social Constructivism - role of peers, community
Sense Making - actively trying to understand
Mental Models - representations of phenomena
Constructionism - learning through building
Metacognition
• Literally - thinking about thinking
• Awareness of ones thinking and knowledge of
skills and limitations
– Planning
– Checking
– Reflection
• Epistemology: knowledge of ideas and their
origins
– General knowledge of ideas
– Specific to field of study eg Nature of Science
Experts vs Novices
• Experts do not
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Think at a faster rate
Use more of their brain
Instantly know the answer to everything
Learn without effort
• Experts do
– Organize knowledge by important factors
– Chunk information for easy use and retrieval
– Identify patterns and make inferences
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Transfer
• Best test of understanding
• Three key types
– Near transfer (parallelograms)
– Far transfer (fortress - tumor)
– Transfer to real world
• Keys for promoting transfer
– Abstraction
– Representations of knowledge
– Use of analogies
Lev Vygotsky (1896-1934) Social Constructivism
• “Every function in the child's
cultural development appears
twice: first, on the social level,
and later, on the individual level”
• Zone of Proximal Development (ZPD)
– The difference between what you
can do by yourself and what you
can do with the aid of a coach
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Problem Solving & Sense
Making
• Problem Solving
– Humans work differently than computers
– Students assume they should just know
– Experts spend more time planning and checking
• Sense Making
– Somewhat undefined: “I know it when I see it”
– Effort to try and understand/explain something
something
Mental Models
• A mental representation of a
phenomena or event
• Depictive simulations allow people to
see things in their minds eye
• Representation that is “run-able” so you
can simulate what will happen and
make predictions
• Similar to Schemas but bigger?
Constructionism
• Learning by creating new things
• From Paper (1979) Mindstorms
– Research on LOGO programing
– Computerized “Microworlds” where you
can build anything you want
• Extended to engineering type projects
– Instead of experimentation, build
something