Educause 2002 presentation ( , Atlanta, Georgia): Cognitive Psychology Principles for Educational Technology Douglas D. Mann, Ph.D., Ohio University Copyright Doug Mann, This work is the intellectual property of the author. Permission is granted for this material to be shared for non-commercial, educational purposes, provided that this copyright statement appears on the reproduced materials and notice is given that the copying is by permission of the author. To disseminate otherwise or to republish requires written permission from the author.

Cognitive Psychology Principles for Educational Technology Educause 2002 (Atlanta, ) Douglas D. Mann, Ph.D. Associate Provost for Information Technology Ohio University

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Challenging Questions What scientific knowledge can be used to inform the design of technology-supported learning experiences? What is the scientific basis for (pick your favorite buzzword) “social constructivism,” etc?

Assumption #1 Pedagogy and technology are entirely independent of each other

High-tech Low-tech Traditional instruction Application-driven learning

High-tech Low-tech Traditional instruction Application-driven learning Web-streamed lecture with synchronized slides

High-tech Low-tech Traditional instruction Application-driven learning Web-streamed lecture with synchronized slides PBL based on paper cases

Assumption #2 Pedagogy is more important than technology

What scientific principles should drive the design of learning experiences?

An abbreviated history of psychology as applied to learning Associationism/Behaviorism “You have a brain, but we don’t care what it’s doing. We care about observable behavior.”

An abbreviated history of psychology as applied to learning Early cognitive psychology “Your brain is important, and it works like a digital computer.”

An abbreviated history of psychology as applied to learning Current cognitive psychology “Your brain is a complex product of evolution, and its strengths and weaknesses are the opposite of those of a digital computer.”

An abbreviated history of psychology as applied to learning Social constructivism “Each person’s knowledge is uniquely constructed on a foundation of prior knowledge and experience, and validated through participation in a community of learner-practitioners.”

Areas within cognitive psychology Cognitive neuroscience Attention, perception Memory Problem solving Judgment and decision making Creativity

Cognitive neuroscience The brain is a highly interconnected neural network; knowledge is stored in patterns of connection strengths among neurons

Neural network learning

Neural network recall

Memory Short-term (working) memory is small Long-term memory is unlimited Astounding visual pattern memory Partial retrieval of knowledge is common Activation of prior knowledge enhances encoding and retrieval of new information “Encoding specificity”: similarity of context at learning and at recall increases retrieval

Problem solving Expert-novice differences in categorizing and solving problems Poor transfer of learning to different types of problems Prior misconceptions of novices hinder new learning Most real-world problems are “ill-defined.” Expertise and “automaticity” have some disadvantages

Judgment and decision making JDM: making decisions under uncertainty, or based on personal preferences Shortcuts, heuristics, “satisficing” Modest “metacognition:” the ability to monitor, control, and evaluate the quality of one’s own judgments; overconfidence

Other research findings “flow” experiences (challenging situation, immediate feedback, high engagement) are highly satisfying  motivation ->  time on task ->  achievement

Findings and principles FINDING: Activation of prior knowledge enhances encoding and retrieval of new information Prior misconceptions of novices hinder new learning PRINCIPLE: Engage learners in reviewing what they already know before new information is introduced; probe for misconceptions. EXAMPLES; questions about prior knowledge; problems requiring prior knowledge

Findings and principles FINDING: “Encoding specificity”: similarity of context at learning and at recall increases retrieval PRINCIPLE: Organize the content of learning experiences around application themes EXAMPLE: “clinical presentation” curricula in medical education

Findings and principles FINDING: Expert-novice differences in categorizing and solving problems PRINCIPLE: Provide students with early exposure to expert approaches to problems; design learning experiences to foster expert- like thinking

Findings and principles FINDING: Poor transfer of learning to different types of problems Most real-world problems are “ill-defined.” PRINCIPLE: Provide many problems and “mini- cases” to promote generalization and transfer. EXAMPLE: “what if one variable changed” questions; applications of “cognitive flexibility theory” (Rand Spiro) to film analysis, medicine

Findings and principles FINDING: Modest “metacognition”: the ability to monitor, control, and evaluate the quality of one’s own judgments; overconfidence (in learning or judgment). PRINCIPLE: Build self-assessment into learning, along with expert feedback EXAMPLE: in-class “voting” on answers to problems; confidence-weighted test questions; self-assessments in learning portfolios

Findings and principles FINDING: “flow” experiences are highly satisfying  motivation ->  time on task ->  achievement PRINCIPLE: Use authentic, engaging simulations/cases/problems to drive learning COROLLARY: Don’t assume that high-fidelity simulations are required (e.g., paper-based PBL)

Model of optimal learning

Challenging Questions What scientific knowledge can be used to inform the design of technology-supported learning experiences? What is the scientific basis for (pick your favorite buzzword) “social constructivism,” etc?

Selected References Bruning, R. H., G. J. Schraw, et al. (1999). Cognitive Psychology and Instruction. Upper Saddle River, NJ, Prentice-Hall, Inc. Csikszentmihalyi, M. (1990). FLOW: The Psychology of Optimal Experience. New York, HarperCollins Publishers. Nix, D. and R. Spiro, Eds. (1990). Cognition, Education, and Multimedia: Exploring Ideas in High Technology. Hillsdale, NJ, Lawrence Erlbaum Associates.

Selected References cont. Norman, G. R. and H. G. Schmidt (1992). "The psychological basis of problem-based learning: A review of the evidence." Academic Medicine 67: Regehr, G. and G. R. Norman (1996). "Issues in cognitive psychology: Implications for professional education." Academic Medicine 71(9): Schank, R. C. and C. Cleary (1995). Engines for Education. Hillsdale, NJ, Lawrence Erlbaum Associates, Publishers.

Cognitive Psychology Principles for Educational Technology Educause 2002 (Atlanta, ) Douglas D. Mann, Ph.D. Associate Provost for Information Technology Ohio University