Overview of Cognitive Science for Teachers 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

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

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: 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 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 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

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

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

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