Comprehending Emergent Phenomena: Through Direct-Manipulation Animation
PROCEEDINGS
Priscilla Aguirre, John Black, Teachers College - Columbia University, United States
EdMedia + Innovate Learning, in Honolulu, HI, USA ISBN 978-1-880094-73-0 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC
Abstract
Given the literature has established that students hold robust misconception of emergent systems phenomena, this study investigates the influence of using a direct-manipulation animation (DMA) (Chan & Black, 2006) modeling environment for increasing comprehension and transfer of such phenomena. Furthermore, we propose a cognitive processing model to elucidate how DMA impacts the cognitive process. An empirical study with 90 college students who were asked to interact with a NetLogo model that had been tweaked for 3 types of interaction conditions: no manipulation animation (NMA), post-manipulation animation (PMA), direct manipulation animation (DMA). From these results we expect to find that DMA activates the learners' cognitive processes so as to construct more robust mental models of emergent phenomena.
Citation
Aguirre, P. & Black, J. (2009). Comprehending Emergent Phenomena: Through Direct-Manipulation Animation. In G. Siemens & C. Fulford (Eds.), Proceedings of ED-MEDIA 2009--World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 3486-3490). Honolulu, HI, USA: Association for the Advancement of Computing in Education (AACE). Retrieved August 12, 2024 from https://www.learntechlib.org/primary/p/31980/.
© 2009 Association for the Advancement of Computing in Education (AACE)
References
View References & Citations Map- Baddeley, A.D. (1986), Baddeley, A.D. (1986). Working memory. Oxford, England: Oxford University Press.
- Baddeley, A.D. (1992). Is working memory working? Quarterly Journal of Educational Psychology 44(a): 1-33.
- Chan, M.S. & Black, J.B. (2006). Direct-manipulation animation: incorporating the haptic channel in the learning process to support middle school students’ in science learning and mental model acquisition.
- Chandler, P. & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition ICLS. And Instruction, 8, 293-332.
- Chi, M.T.H. (2003). Barriers to conceptual change in learning science concepts: A theoretical conjecture. Proceedings of the Fifteenth Annual Cognitive Science Society Conference, Boulder, CO.
- Chi, M.T.H (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. The Journal of the Learning Sciences 14(2): 161-199.
- Chi, M.T.H and Roscoe, R.D.(2002). The processes and challenges of conceptual change. In M. Limon & L. Mason (Eds.), Reframing the process of conceptual change: Integrating theory and practice (pp. 3-27>. Dordrecht, The Netherlands: Kluwer Academic
- Clark, J.M. And Paivio, A. (1991). Dual-coding theory and education. Educational Psychology Review 3, 149-210
- Confrey, J. (1990). A review of the research on student conceptions in mathematics, science and programming. In C.B. Cazdan (ED). Review of Research in Education, 16, 3-56
- Driver, R., Squires, A., Rushworth, P., & Wood-Robinson, V. (1994). Making sense of secondary science: Research into children’ s ideas. New York, Routhledge.
- De Jong, T. (2006). In, Elen, J. & Clark, R.E., Eds. Dealing with complexity in learning environments. Elsevier Science, London, pp 107-128
- Gell-Mann, M. (1994). The quark and the jaguar. New York, W.H. Freeman
- Gleick, J. (1987). Chaos. New York, Viking Penguin
- Goldstein, J. (1999). Emergence as a Construct: History and Issues, Emergence: Complexity and Organization 1, 49-72.
- Jacobson, M.J. (2001). Problem solving, cognition, and complex systems: differences between experts and novices. Complexity, 6(3), 41-49.
- Jacobson, M.J., & Wilensky, U. (2006). Complex Systems in Education: Scientific and Educational Importance and Implications for the Learning. The Journal of the Learning Sciences, 15(1), 1134.
- Kauffman, S. (1995). At home in the universe: the search for the laws of self-organization and complexity. Oxford, Oxford University Press.
- Kelly, K. (1994). Out of control. Reading, MA, Addison Wesley.
- Mayer, R.E. (1999). Research-based principles for the design of instructional messages: The case of multimedia explanations.. Document Design, 1, 7-20.
- Miller, C.S., Lehman, J.F., & Koedinger, K.R. (1999). Goals and learning in microworlds. Press
- Paivio, A. (1986). Mental representations: A dual-coding approach. New York, Oxford University
- Perkins, D.N. & Grotzer, T.A. (2000). Models and moves: Focusing on dimensions of complex causality to achieve deeper scientific understanding. Paper presented at the annual meeting of American Educational Research Association. New Orleans, LA.
- Pfundt, H. & Duit, R. (1993). Bibliography: Students’ alternative frameworks and science education. Kiel, FGR: Institute for Science Education
- Ram, A., Nersessian, N.J. & Keil, F.C. (1997). Special issue: Conceptual Change. The Journal of the Learning Sciences, 6, 1-91.
- Reiner, M., Slotta, J.D., Chi, M.T.H. & Resnick, L.B. (2000). Naïve physics reasoning: A commitment to substance-based conceptions. Cognition and Instruction, 18(1), 1-34
- Resnick, M. (1994). Turtles, termites, and traffic jams: Explorations in massively parallel microworlds. Cambridge, MA:MIT.
- Schank, R.C., & Farrel, R. (1988). Creativity in education: A standard for computer-based teaching. Machine-Mediated Learning, 2, 175-194.
- Schwartz, D.L., & Black, J.B. (1996). Analog imagery in mental model reasoning: Depictive models. Cognitive Psychology, 30(2), 154-219.
- Waldrop, M. (1992). Complexity: The emerging science at the edge of order and chaos. New York: Simon& Schuster.
- White, B.Y. & Fredericksen, J.R. (1998). Inquiry, modeling and metacognition: Making science accessible to all students. Cognition and Instruction, 16, 3-118
- White, B.Y. (1993). Thinker Tools: causal models, conceptual change and science education. Cognition and Instruction, 10, 1-100 Wilensky, U. (1998a). NetLogo Ants model. Http://ccl.northwestern.edu/netlogo/models/Ants. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Wilensky, U. (1998b). NetLogo Flocking model. Http://ccl.northwestern.edu/netlogo/models/Flocking. Center for Connected Learning and Computer-Based
- Wilensky, U. (2001). Modeling nature ’ s emergent patterns with multi-agent languages. Proceedings of EuroLogo 2001. Linz, Austraia.
- Wilensky, U. & Resnick, M. (1999). Thinking in levels: A dynamic systems perspective to making sense of the world. Journal of Science Education and Technology, 8(1), 3-19.
- Wilensky, U. & Reisman, K. (1999). Connected Science: Learning Biology through Constructing and Testing Computational Theories--an Embodied Modeling Approach. International Journal of Complex Systems, M. 234, pp. 1-12.
These references have been extracted automatically and may have some errors. Signed in users can suggest corrections to these mistakes.
Suggest Corrections to References