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Lesson Goals:

Lesson Goals:. Students will gain an understanding of andragogy and computational modeling. Students will be able to dance the Hokey Pokey. Lesson Agenda. Andragogy Overview Activity: Andragogical Hokey Pokey Computational Models Overview

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Lesson Goals:

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  1. Lesson Goals: • Students will gain an understanding of andragogy and computational modeling. • Students will be able to dance the Hokey Pokey.

  2. Lesson Agenda • Andragogy Overview • Activity: Andragogical Hokey Pokey • Computational Models Overview • Demonstration: Computational Model of the Hokey Pokey • Summative Evaluation • Discussion / Q&A

  3. Andragogy Overview • What is andragogy? • Andragogical principles

  4. What is andragogy? • It is an adult learning theory. • Focuses on discovery and control. • Adults are to be motivated to learn about what will help them overcome their daily challenges. • There will not be any learner resistance if participants are learning what they believed they needed. • The instructor approaches training as a facilitator or guide instead of a dictator.

  5. Andragogical Principles • Adults need to be involved in the planning and evaluation of their instruction. • Experience (including mistakes) provides the basis for learning activities. • Adults are most interested in learning subjects that have immediate relevance to their job or personal life. • Adult learning is problem-centered rather than content oriented.(Kearsley, 2004)

  6. Activity: Andragogical Hokey Pokey Goal: Discover steps to dance by recall and learning from others • Students break into groups of two or three. • Recall/teach each other the dance steps. • Music will be provided. • Jeremy and Lori will provide assistance as needed. • Everyone will dance the Hokey Pokey.

  7. Computational Models ofCognitive Development Overview • Origins: Pre-computational Models (Piagetian assimilation, accommodation, equilibration) • Computational models • Use formulae to simulate learning and development. • Primarily computer-based

  8. Computational Models of Cognitive Development • Example Computational Model: • Symbolic “Production Systems” • Series of IF - THEN statements: "productions" applied to input stimuli • Self-modification capability • Applied to Piagetian tasks

  9. Initialize Subroutine Call Subroutine Shake It All About Right Arm Out (2) Right Arm In Turn Yourself Around (2) Right Arm Out (1) Right Arm In Store Program Demonstration: Computational Model of the Hokey Pokey

  10. Computational Modeling: Benefits & Challenges Benefits • Requires deep and detailed analysis of task • Produces testable output • Provides direct access to mechanisms • Provides insight not apparent by static analysis Challenges • Authenticity? • Assumptions open models to invalidation • Self-fulfilling prophecy - creating models to fit preconceived notions • Social context • Technical limitations

  11. Summative Evaluation Everybody dance!

  12. Sources • Anonymous (1998). Malcolm Shepherd Knowles: 1913-1997. Training & Development, 52(2), 11. • Bell, C. R. (1989). Malcolm. Training and Development Journal, 43(10), 38-43. • Dastoor, B. (1993). Training 101: The Psychology of Learning. Training & Development, 47(6), 17-20. • Geber, B. (1988). The Problem With Andragogy. Training, 25(12), 8. • Kearsley, G. (n.d.). Explorations in Learning & Instruction: The Theory Into Practice Database. Retrieved October 12, 2004, from http://tip.psychology.org/knowles.html • Knowles, M. (1984). Adult Learner: A Neglected Species (3rd ed.). Houston, Texas: Gulf Publishing Company. • Knowles, M. S. (1962). A History of the Adult Education Movement in the United States (Rev. ed.). Huntington, New York: Robert E. Krieger Publishing Company. • Knowles, M. S. (1986). Using Learning Contracts: Practical Approaches to Individualizing and Structuring Learning. San Francisco: Jossey-Bass Publishers. • Lee, C. (1998). The adult learner: neglected no more. Training, 35(3), 47-52. • Reed, J. (1993). Training 101: Learner-Centered Learning. Training & Development, 47(6), 20-21.

  13. Sources • Elman, J.L., Bates, E.A., Johnson, M.H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1996). Rethinking innateness: A connectionist perspective on development. Cambridge, MA: MIT Press. • Jones, G., Ritter, F.E., & Wood, D.J. (2000). Using a cognitive architecture to examine what develops. Psychological Science, 11(2), 93-100. • Klahr, D. (1995). Computational models of cognitive change: The state of the art. In T.J. Simon & G.S. Halford (Eds.), Developing cognitive competence: New approaches to process modeling (pp. 355-375). Hillsdale, NJ: Erlbaum. • Klahr, D. & Wallace, J.G. (1976). Cognitive development: An information-processing view. Hillsdale, NJ: Erlbaum. • Mareschal, D. (2003). Computational models of cognitive development. In L. Nadel (Ed.), Encyclopaedia of cognitive science (pp. 533-39). London, UK: Macmillan. • Mareschal, D., & Shultz, T.R. (1996). Generative connectionist networks and constructivist cognitive development. Cognitive Development, 11, 571-603. • Neches, R., Langley, P. & Klahr, D. (1987). Learning, development, and production systems. In D. Klahr, P. Langley, & R. Neches (Eds.), Production systems models of learning and development (pp. 1-53). Cambridge, MA: MIT Press. • Schlesinger, M., & Parisi, D. (2001). The agent-based approach: A new direction for computational models of development. Developmental Review, 21, 121-146. • Simon, T.J., & Halford, G.S. (1995). Computational models and cognitive change. In T.J. Simon & G.S. Halford (Eds.), Developing cognitive competence: New approaches to process modeling (pp. 1-30). Hillsdale, NJ: Erlbaum. • van Geert, P. (1998). A dynamic systems model of basic mechanisms: Piaget, Vygotsky, and beyond. Psychological Review, 105(4), 634-677.

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