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Strategies for using simulations as a vehicle to manage cognitive load
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, Indiana State University, United States ; , Indiana University Purdue University Indianapolis, United States ; , , Indiana State University, United States

Society for Information Technology & Teacher Education International Conference, in Charleston, SC, USA ISBN 978-1-880094-67-9 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC USA

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Abstract

Instructional simulations situate the learner in a psychological reality established within the simulation and engage the learner in guided learning-by-doing activities. Further, simulations have been shown to motivate the learner to engage in deep and experiential learning. However, simulations can increase cognitive load adding to student frustration and limiting the effective learning experience. This paper presents a definition of cognitive load and a discussion of how instructional simulations can help to manage cognitive load. A number of relevant variables are discussed, including scaffolding and learner prior-knowledge. Research is presented documenting the role of these variables when simulations are used to ameliorate cognitive load. The paper concludes with a discussion of the relevance of these questions to Instructional Design.

Citation

Lunce, L., Runshe, D., Hsiao, E.L. & Huang, X. (2009). Strategies for using simulations as a vehicle to manage cognitive load. In I. Gibson, R. Weber, K. McFerrin, R. Carlsen & D. Willis (Eds.), Proceedings of SITE 2009--Society for Information Technology & Teacher Education International Conference (pp. 1492-1495). Charleston, SC, USA: Association for the Advancement of Computing in Education (AACE). Retrieved August 9, 2024 from .

References

View References & Citations Map
  1. Aldrich, C. (2004). Simulations& The future of learning: An innovative (and perhaps) revolutionary approach to e-learning. San Francisco: John Wiley& Sons, Inc.
  2. Aldrich, C. (2005). Learningbydoing: A comprehensive guide to simulations, computer games and pedagogy in e-learning and other educational experiences. San Francisco, CA: John Wiley& Sons, Inc.
  3. Alessi S., & Trollip, S. (2001). Multimedia for learning: Methods and development (3rd ed.). Boston: Allyn & Bacon.
  4. Cohen, S., Portney, K., Rehberger, D. & Thorsen, C. (2006). Virtual decisions: Digital simulations for teaching reasoning in the social sciences and humanities. Mahwah, NJ: Lawrence Erlbaum Associates.
  5. De Jong, T. & Van Joolingen, W. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179–201.
  6. Dede, C. (1996). Emerging technologies and distributed learning. American Journal of Distance Education, 10(2), 4-36.
  7. DeLeeuw, K., & Mayer, R. (2008, February 1). A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. Journal of Educational Psychology, 100(1), 223-234. (ERIC Document Reproduction Service No. EJ787150) Retrieved October 11, 2008, from ERIC database.
  8. Doering, A., & Veletsianos, G. (2007). Multi-scaffolding environment: An analysis of scaffolding and its impact on cognitive load and problem-solving ability. Journal of Educational Computing Research, 37(2), 107-129.
  9. Duffy, T., & Cunningham, D. (1996). Constructivism: Implications for the design and delivery of instruction. In Jonassen, D.H. (Ed.), Handbook of Research for Educational Communications and Technology (pp.170-198). New York: Simon and Schuster.
  10. Gredler, M.E. (1992). Designing and evaluating games and simulations: A process approach. Houston, TX: Gulf Publishing Company.
  11. Heinich, R., Molenda, M., Russell, J., & Smaldino, S. (1999). Instructional media and technologies for learning (6th ed.). Upper Saddle River, New Jersey: Prentice-Hall.
  12. Hertel, J., & Millis, B. (2002). Using simulations to promote learning in higher education: An introduction (Enhancing learning series). Millis Sterling, VA: Stylus Publishing.
  13. Hogg, N.M. (2006). Measuring cognitive load. In R.A. Reynolds, R. Woods, & J. Baker (Eds.), Handbook of research on electronic surveys and measurements (pp. 188-194). Hershey, PA: Idea Group Reference.
  14. Horiguchi, T., & Hirashima, T. (2004). A simulation-based learning environment assisting scientific activities based on the classification of 'surprisingness'. In Kommers, P., & Richards, G. (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2004 (pp.497-504). Chesapeake, VA: AACE.
  15. Hung, D., & Chen, D. (2002). Two kinds of scaffolding: The dialectical process within the authenticity-generalizability (A-G) continuum. Education Technology& Society, 5(4), 148-153.
  16. Igo, L., Kiewra, K., Zumbrunn, S., & Kirschbaum, A. (2007, January 1). How best to remove the snare from the pair: Construction and cognitive load hypotheses. Journal of Experimental Education, 75(2), 130-144. (ERIC Document Reproduction Service No. EJ773674) Retrieved October 11, 2008, from ERIC database.
  17. Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of Expertise and Instructional Design. Human Factors, 40(1), 1-17.
  18. Kalyuga, S., Chandler, P., & Sweller, J. (1999). Managing split-attention and redundancy in multimedia instruction. Applied Cognitive Psychology, 13, 351-371.
  19. Mayer, R.E. (2001). Multimedia learning. Cambridge, UK: Cambridge University Press.
  20. Meichenbaum, D., & Biemiller, A. (1998). Nurturing independent learners: Helping students take charge of their learning. Cambridge, Massachusetts: Brookline Books.
  21. Miller, G.A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97. Retrieved Oct 12, 2008, from http://www.well.com/user/smalin/miller.html
  22. Miller, G.A., Galanter, E., & Pribram, K.H. (1960). Plans and the structure of behavior. New York: Holt, Rinehart& Winston.
  23. Min, R. (2001). Designing dynamical learning environments for simulation: Micro-worlds applets on the worldwide web. Retrieved Oct 12, 2008, from http://projects.edte.utwente.nl/pi/Papers/AbstractErfurt.htm
  24. Paas, F., Renkl, A. & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1-4.
  25. Quinn, C., & Conner, M. (2005). Engaging learning: Designing e-learning simulation games. San Francisco. CA: John Wiley& Sons.
  26. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning, Cognitive Science, 12, 257-285.
  27. Sweller, J. (1999). Instructional design in technical areas. Camberwell, Victoria, Australia: Australian Council for Educational Research.
  28. Sweller, J., van Merrienboer, J.J.G., & Paas, F.G.W.C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251-296.
  29. Van Memenbore, J.J., Kirschner, P.A., & Kester, L. (2003). Taking the load off a learner’s mind: Instructional design for complex learning. Educational Psychologist, 38(1), 5-13.
  30. Winn, W., & Snyder, D. (1996). Cognitive perspectives in psychology. In Jonassen, D.H. (Ed.), Handbook of Research for Educational Communications and Technology (pp.115-122). New York: Simon and Schuster.
  31. Yeung, A.S. (1999). Cognitive load and learner expertise: Split-attention and redundancy effects in reading comprehension tasks with vocabulary definitions. The Journal of Experimental Education, 67(3), 197-217.
  32. Yeung, A.S., Jin, P., & Sweller, J. (1997). Cognitive load and learner expertise: Split-attention and redundancy effects in reading with explanatory notes. Contemporary Educational Psychology, 23, 1-21.

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