Cognitive psychology and the design of technology-supported learning experiences

1. Cognitive psychology and the design of technology-supported learning experiences Presented at the eighth annual University of Colorado Teaching with Technology Conference Boulder, CO, August 12, 2003 Copyright 2003 Douglas D. Mann, Ph.D. Ohio University College of Osteopathic Medicine mannd@ohio.edu

5. 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) “active learning,” “constructivism,” “learning styles,” etc?

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

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

8. Web-streamedlecture with synchronizedslides High-tech Low-tech Traditional instruction Application-driven learning

9. Web-streamedlecture with synchronizedslides High-tech PBL based onpaper cases Low-tech Traditional instruction Application-driven learning

10. Technology as a tool… • Chickering, A.W., and Gamson, Z. F. (1987). Seven principles for good practice in undergraduate education. AAHE Bulletin.http://www.aahebulletin.com/public/archive/sevenprinciples1987.asp . • Chickering, A. W., and Ehrmann, S. C. (1996). Implementing the seven principles: technology as lever. AAHE Bulletin.http://www.tltgroup.org/programs/seven.html.

11. Chickering and Ehrmann, 1996 • “Any given instructional strategy can be supported by a number of contrasting technologies (old and new), just as any given technology might support different instructional strategies.”

12. Assumption #2 • Pedagogy (instructional strategy) is more important than technology

13. “Here we are, standing in front of the Eiffel Tower.”

14. “Here we are, standing in front of the Eiffel Tower.”

15. Indications of pedagogical problems • Poor student motivation and low attendance • Passive, disengaged student behavior in class • Poor student performance on assignments and tests; high dropout and failure rates • Poor transfer of learning to subsequent courses in the same discipline

16. Technology as a tool… • Example: CMS (course management systems) • Carmean, C., and Haefner, J. (2002). Mind over matter: Transforming course management systems into effective learning environments. Educause Review, 37:6, pp. 26- 34.

17. Carmean & Haefner, 2002 • Table 1: (Five) Deeper Learning Principles • 1. SOCIAL (five criteria) • 2. ACTIVE (six criteria) • 3. CONTEXTUAL (eight criteria) • 4. ENGAGING (four criteria) • 5. STUDENT-OWNED (six criteria) • Five principles, 29 criteria!

18. Carmean & Haefner, 2002 • Table 1: (Five) Deeper Learning Principles • 1. Learning is SOCIAL when: • It involves cognitive apprenticeship. • It promotes reciprocity and cooperation among students. • It offers prompt feedback. • It encourages contact between students and faculty. • It emphasizes rich, timely feedback.

19. Technology as a tool… • Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development (50:3).http://www.id2.usu.edu/Papers/5FirstPrinciples.PDF • “first principles for instruction do exist…one or more of these first principles can be found in most instructional design theories and models.”

20. M. D. Merrill, 2002 • Learning is facilitated when: • Learners are engaged in solving real-world problems. • Existing knowledge is activated as a foundation for new knowledge. • New knowledge is demonstrated to the learner. • New knowledge is applied by the learner. • New knowledge is integrated into the learner’s world.

21. M. D. Merrill, 2002 Problem Integration Activation Application Demonstration

22. First principles for learning • Marchese, T. J. (1997). The new conversations about learning: Insights from neuroscience and anthropology, cognitive science and workplace studies. In Assessing Impact: Evidence and Action. Washington, D.C.: American Association for Higher Education, pp. 79-95.http://www.aahe.org/pubs/TM-essay.htm, accessed 5-23-2003.

23. Collaborative learning Cooperative learning Problem-based learning Service-learning Case-method teaching Peer-based methods Undergraduate research Senior capstones Portfolios Journals Muticultural learning Leadership training Marchese, 1997: “powerful pedagogies”

24. Marchese, 1997: common features to promote “deep learning” • Learner independence and choice • intrinsic motivators and natural curiosity • rich, timely, usable feedback • coupled with occasions for reflection and… • active involvement in real-world tasks • emphasizing higher-order abilities • done with other people • in high-challenge, low-threat environments • that provide for practice and reinforcement.

25. Imagine an ideal learning experience…

26. Model of optimal learning

27. An abbreviated history of psychology as applied to learning • 1. Associationism/Behaviorism • “You have a brain, but we don’t care what it’s doing. We care about observable behavior.”

28. Traditional Instructional Design • Bloom’s taxonomy of cognitive objectives (1956):EvaluationSynthesisAnalysisApplicationComprehensionKnowledge (recall) • Behavioral objectives: conditions to perform a behavior to certain standards • Mastery learning

29. Traditional Instructional Design LearningActivities Objectives Assessment

30. Traditional Instructional Design

31. An abbreviated history of psychology as applied to learning • 2. Early cognitive psychology • “Your brain is important, and it works like a digital computer.” • The information-processing paradigm

32. An abbreviated history of psychology as applied to learning • 3. 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.”

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

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

35. Neural network learning

36. Neural network recall

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

39. Memory • APA Board of Educational Affairs, Nov. 1997http://www.apa.org/ed/lcp.html • “…unless new knowledge becomes integrated with the learner’s prior knowledge and understanding, this new knowledge remains isolated, cannot be used most effectively in new tasks, and does not transfer readily to new situations.”

40. 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 has some disadvantages

41. 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

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

43. Findings and principles - 1 • 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

44. Findings and principles - 2 • 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; project-based MBA programs

46. Nine projects over a two-year period • Short, intensive residencies • Three-month periods of technologically-supported team collaboration at a distance

47. Findings and principles - 3 • 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 • EXAMPLE: exercises to teach students to classify problems using the basic underlying concepts used by experts. Also see http://www.criticalthinking.org.

48. Findings and principles - 3 Foundation for Critical Thinking http://www.criticalthinking.org. Dr. Richard Paul and Dr. Linda Elder

49. Findings and principles - 4 • 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

50. Findings and principles - 5 • 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