1. Overview of Cognitive Science for Teachers
Science of Learning
Overview of Cognitive Science for Teachers

2. Psychological/Cognitive Revolution
Freud (1890)
Kohler’s monkeys(1908)
: Two directions
US: Behaviorism
Europe: Gestalt
1960’s- Cognitive Science
Formerly artificial intelligence
Only Nobel prize for psychology

3. 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

4. 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
Research on understanding

6. Constructivsm Jean Piaget (1940-70s) Knowledge must be constructed
Assimilation
Accomodation
Ignore/segment knowledge
Looked at students mistakes and developed theory of development
Sensorimotor stage
Preoperational stage
Concrete operational stage
Formal operations

7. 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

8. Astronaut question
The astronaut is walking on the moon when he lets go of his pen. Does the pen:
Fall to the moon surface
Float up into the sky
Float in place

9. 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)?

10. Cognitive Science Understanding thinking using many perspectives:
Psychology
Computer Science
Linguistics
Anthropology

11. 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)

12. Contemporary Learning Theory
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

13. 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

14. Experts vs Novices Experts do not Experts do 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

16. 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

17. 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

18. Problem Solving & Sense Making
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

19. 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?

20. 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