Skip to main content
SpringerLink
Log in

A Scaffolding Framework to Support Learning of Emergent Phenomena Using Multi-Agent-Based Simulation Environments

  • Published:
Research in Science Education Aims and scope Submit manuscript

Abstract

Students from middle school to college have difficulties in interpreting and understanding complex systems such as ecological phenomena. Researchers have suggested that students experience difficulties in reconciling the relationships between individuals, populations, and species, as well as the interactions between organisms and their environment in the ecosystem. Multi-agent-based computational models (MABMs) can explicitly capture agents and their interactions by representing individual actors as computational objects with assigned rules. As a result, the collective aggregate-level behavior of the population dynamically emerges from simulations that generate the aggregation of these interactions. Past studies have used a variety of scaffolds to help students learn ecological phenomena. Yet, there is no theoretical framework that supports the systematic design of scaffolds to aid students’ learning in MABMs. Our paper addresses this issue by proposing a comprehensive framework for the design, analysis, and evaluation of scaffolding to support students’ learning of ecology in a MABM. We present a study in which middle school students used a MABM to investigate and learn about a desert ecosystem. We identify the different types of scaffolds needed to support inquiry learning activities in this simulation environment and use our theoretical framework to demonstrate the effectiveness of our scaffolds in helping students develop a deep understanding of the complex ecological behaviors represented in the simulation..

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://www.fossweb.com/CA/modules3-6/Environments/activities/saguaro/content_old.html

  2. Not the student’s actual name

  3. TechSmith’s Camtasia studio is Screen Recording & Video Editing Software. See http://www.techsmith.com/camtasia/

References

  • Ainsworth, S. E. (2006). DeFT: a conceptual framework for learning with multiple representations. Learning and Instruction, 16(3), 183–198.

    Article  Google Scholar 

  • Azevedo, R., Johnson, A., Chauncey, A., & Graesser, A. (2011). Use of hypermedia to convey and assess self-regulated learning. In B. Zimmerman & D. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 102–121). New York: Routledge.

    Google Scholar 

  • Bar-Yam, Y. (1997). Dynamics of complex systems (Vol. 213). Reading, MA: Addison-Wesley.

  • Basu, S., & Biswas, G. (2011). Multiple representations to support learning of complex ecological processes in simulation environments (In proceedings of the 19th international conference on computers in education). Thailand: Chiang Mai.

    Google Scholar 

  • Basu, S., Biswas, G., and Sengupta, P. (2011). Scaffolding to support learning of ecology in simulation environments. In Susan Bull and Gautam Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education. Auckland, New Zealand.

  • Bernstein, A. C., & Cowan, P. A. (1975). Children’s concepts of how people get babies. Child Development, 46, 77–91.

    Article  Google Scholar 

  • Biswas, G., Jeong, H., Kinnebrew, J., Sulcer, B., & Roscoe, R. (2010). Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology-Enhanced Learning, 5(2), 123–152.

    Article  Google Scholar 

  • Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: a constructionist learning environment for materials science using multi-agent simulation. International Journal of Computers for Mathematical Learning, 14(1), 81–119.

    Article  Google Scholar 

  • Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: a simple proposal with multiple implications. In A. Iran-Nejad & P. D. Pearson (Eds.), Review of research in education, 24 (pp. 61–101). Washington DC: American Educational Research Association.

    Google Scholar 

  • Carey, S. (1985). Conceptual change in childhood. Cambridge: MIT Press.

    Google Scholar 

  • Chen, Z., & Klahr, D. (1999). All other things being equal: acquisition and transfer of the control of variables strategy. Child Development, 70(5), 1098–1120.

    Article  Google Scholar 

  • Chi, M. T. H. (2000). Self-explaining expository texts: the dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology (pp. 161–238). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Chi, M. T. H. (2005). Common sense conceptions of emergent processes: why some misconceptions are robust. Journal of the Learning Sciences, 14, 161–199.

    Article  Google Scholar 

  • Chi, M. T. H., & Ferrari, M. (1998). The nature of naïve explanations in natural selection. International Journal of Science Education, 20(10), 1231–1256.

    Article  Google Scholar 

  • Chi, M. T. H., Slotta, J. D., & deLeeuw, N. (1994). From things to processes: a theory of conceptual change for learning science concepts. Learning and Instruction, 4, 27–43.

    Article  Google Scholar 

  • Clark, D., Nelson, B., Sengupta, P., & D’Angelo, C. (2009). Rethinking science learning through digital games and simulations: genres, examples, and evidence. Washington DC: National Research Council.

    Google Scholar 

  • d’Apollonia, S., Charles, E., & Boyd, G. (2004). Acquisition of complex systemic thinking: mental models of evolution. Educational Research and Evaluation, 10(4), 499–521.

    Article  Google Scholar 

  • Danish, J. A., Peppler, K., Phelps, D., & Washington, D. (2011). Life in the hive: supporting inquiry into complexity within the zone of proximal development. Journal of Science Education and Technology, 20(5), 454–467.

    Article  Google Scholar 

  • Darwin, C. (1871). The descent of man, and selection in relation to sex (1st ed.), London: John Murray, ISBN 0801420857, retrieved 2009-06-18

  • de Jong, T., & Njoo, M. (1992). Learning and instruction with computer simulations: learning processes involved. Journal of Research in Science Teaching, 30(8), 821–844.

    Google Scholar 

  • de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179–202.

    Article  Google Scholar 

  • Dickes, A., & Sengupta, P. (2013). Learning natural selection in 4th grade with multi-agent-based computational models. Research in Science Education, 43(3), 921–953.

    Article  Google Scholar 

  • Dillenbourg, P. (1999). Collaborative learning: cognitive and computational approaches. Advances in Learning and Instruction Series. Elsevier Science, Inc., PO Box 945, Madison Square Station, New York, NY 10160–0757.

  • diSessa, A. A. (1993). Towards an epistemology of physics. Cognition and Instruction, 10(2–3), 105–225.

    Article  Google Scholar 

  • Goldstone, R. L., & Son, J. Y. (2005). The transfer of scientific principles using concrete and idealized simulations. Journal of the Learning Sciences, 14, 69–114.

    Article  Google Scholar 

  • Goldstone, R. L., & Wilensky, U. (2008). Promoting transfer by grounding complex systems principles. Journal of the Learning Sciences, 17(4), 465–516.

  • Gupta, A., Hammer, D., & Redish, E. F. (2009). Ontology of physics concepts: variability and local coherences. AERA 2009 Annual Meeting, San Diego, CA.

  • Guzdial, M. (1994). Software‐realized scaffolding to facilitate programming for science learning. Interactive Learning Environments, 4(1), 1–44.

    Article  Google Scholar 

  • Hendrix, J. R., Mertens, T. R., & Baumgartner, R. S. (1981). Individualizing instruction through concept assessment. American Biology Teaching, 43(5), 246–253.

    Article  Google Scholar 

  • Hmelo-Silver, C. E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28(1), 127–138.

    Article  Google Scholar 

  • Holland, J. (1995). Hidden order: how adaptation builds complexity. Cambridge: Perseus.

    Google Scholar 

  • Holland, J. (1998). Emergence: from chaos to order. Reading, MA: Perseus Books.

    Google Scholar 

  • Ingaki, K. & Hatano, G. (2002). Life, liveliness, and living kinds: how young children think about the biological world. Review of “Young Children’s Naive Thinking about the Biological World”. International Journal of Behavioral Development.

  • Jacobson, M. J. (2001). Problem solving, cognition, and complex systems: differences between experts and novices. Complexity, 6(3), 41–49.

    Article  Google Scholar 

  • Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: scientific and education importance and implications for the learning sciences. The Journal of the Learning Sciences, 15(1), 11–34.

    Article  Google Scholar 

  • Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.

    Article  Google Scholar 

  • Kapur, M., & Kinzer, C. K. (2009). Productive failure in CSCL groups. International Journal of Computer-Supported Learning, 4(1), 21–46.

    Article  Google Scholar 

  • Kauffman, S. (1995). At home in the universe: the search for laws of self-organization and complexity. New York: Oxford University Press.

    Google Scholar 

  • Klopfer, E. (2003). Technologies to support the creation of complex systems models—using StarLogo software with students. Biosystems, 71(1), 111–122.

    Article  Google Scholar 

  • Kozma, R. B., Russell, J., Jones, T., Marx, N., & Davis, J. (1996). The use of multiple linked representations to facilitate science understanding. In S. Vosniadou, R. Glaser, E. De Corte, & H. Mandel (Eds.), International perspective on the psychological foundations of technology-based learning environments (pp. 41–60). Hillsdale: Erlbaum.

    Google Scholar 

  • Leelawong, K., & Biswas, G. (2008). Designing learning by teaching agents: the Betty’s brain system. International Journal of Artificial Intelligence in Education, 18(3), 181–208.

    Google Scholar 

  • Lehrer, R., & Schauble, L. (2004). Modeling natural variation through distribution. American Educational Research Journal, 41(3), 635–679.

  • Lehrer, R., & Schauble, L. (2006). Cultivating model-based reasoning in science education. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 371–388). New York: Cambridge University Press.

  • Lehrer, R., & Schauble, L. (2008). A modeling foundation for reasoning about evolution. Presentation to the Consortium for Policy Research in Education Learning Progression Working Group, Ann Arbor, MI.

  • Lemke, J. L. (2000). Across the scales of time: artifacts, activities, and meanings in ecosocial systems. Mind, Culture, and Activity, 7(4), 273–290.

    Article  Google Scholar 

  • Levy, S. T., & Wilensky, U. (2008). Inventing a “mid-level” to make ends meet: reasoning through the levels of complexity. Cognition & Instruction, 1–47.

  • Mataric, M. J. (1993). Designing emergent behaviors: from local interactions to collective intelligence. In Proceedings of the Second International Conference on Simulation of Adaptive Behavior (pp. 432–441), August.

  • Maxwell, J.C. (1871). Remarks on the mathematical classification of physical quantities. Proceedings of the London Mathematical Society, s1–3, 224–233.

  • McNeill, K. L., Lizotte, D. J., Krajcik, J., & Marx, R. W. (2006). Supporting students’ construction of scientific explanations by fading scaffolds in instructional materials. The Journal of the Learning Sciences, 15(2), 153–191.

    Article  Google Scholar 

  • Metz, K. E. (1995). Reassessment of developmental constraints on children’s science instruction. Review of Educational Research, 65(2), 93–127.

  • Mitchell, M. (2009). Complexity: a guided tour. New York: Oxford University Press.

  • Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309–326.

    Article  Google Scholar 

  • Okebukola, P. A. (1990). Attaining meaningful learning of concepts in genetics and ecology: an examination of the potency of the concept-mapping technique. Journal of Research in Science Teaching, 27, 493–504.

    Article  Google Scholar 

  • Papert, S. (1980). Mindstorms: children, computers and powerful ideas. New York: Basic Books.

    Google Scholar 

  • Pathak, S. A., Jacobson, M. J., Kim, B., Zhang, B., & Feng D. (2008). Learning the physics of electricity with agent based models: paradox of productive failure. Paper presented at the International Conference in Computers in Education. Oct. 27–31 Taipei.

  • Penner, D. E. (2001). Cognition, computers, and synthetic science: building knowledge and meaning through modeling. Review of Research in Education, 25, 1–37.

    Google Scholar 

  • Penner, R., Steinmetz, E., Soken, N., Nelson, K., & Whitlow, S. (2000). Model-based design automation for mixed-initiative interactions with complex digital control systems. International Journal of Systems, Man, and Cybernetics.

  • Piaget, J. (1929). The child’s conception of the world. London: Routledge & Kegan Paul.

    Google Scholar 

  • Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., et al. (2004). A scaffolding design framework for software to support science inquiry. Journal of the Learning Sciences, 13(3), 337–386.

    Article  Google Scholar 

  • Reid, D. J., Zhang, J., & Chen, Q. (2003). Supporting scientific discovery learning in a simulation environment. Journal of Computer Assisted Learning, 19(1), 9–20.

    Article  Google Scholar 

  • Reiser, B. J., Tabak, I., Sandoval, W. A., Smith, B. K., Steinmuller, F., & Leone, A. J. (2001). BGuILE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms. In S. M. Carver, & D. Klahr (Eds.), Cognition and instruction: twenty-five years of progress (pp. 263–305). Mahwah, NJ: Erlbaum.

  • Resnick, M., & Wilensky, U. (1998). Diving into complexity: developing probabilistic decentralized thinking through role-playing activities. The Journal of the Learning Sciences, 7(2), 153–172.

    Article  Google Scholar 

  • Resnick, M., & Wilensky, U. (1993). Beyond the deterministic, centralized mindsets: New thinking for new sciences. Paper presented at the annual conference of the American Educational Research Association, Atlanta, GA

  • Scheiter, K., Gerjets, P., Vollmann, B., & Catrambone, R. (2009). The impact of learner characteristics on information utilization strategies, cognitive load experienced, and performance in hypermedia learning. Learning and Instruction, 19(5), 387–401.

    Article  Google Scholar 

  • Schwartz, D. L. (1995). The emergence of abstract representations in dyad problem solving. The Journal of the Learning Sciences, 4(3), 321–354.

    Article  Google Scholar 

  • Schwartz, D. L., & Martin, T. (2004). Inventing to prepare for learning: the hidden efficiency of original student production in statistics instruction. Cognition & Instruction, 22, 129–184.

    Article  Google Scholar 

  • Schwarz, B., & Dreyfus, T. (1993). Measuring integration of information in multirepresentational software. Interactive Learning Environments, 3(3), 177–198.

  • Sengupta, P., & Wilensky, U. (2008). On learning electricity with multi-agent based computational models (NIELS). Proceedings of the International Conference for the Learning Sciences.

  • Sengupta, P., & Wilensky, U. (2009). Learning electricity with NIELS: thinking with electrons and thinking in levels. International Journal of Computers for Mathematical Learning, 14(1), 21–50.

    Article  Google Scholar 

  • Sengupta, P., & Wilensky, U. (2011). Lowering the learning threshold: multi-agent-based models and learning electricity. In M. S. Khine & I. M. Saleh (Eds.), Dynamic modeling: cognitive tool for scientific inquiry (pp. 141–171). New York: Springer.

  • Sherin, B., Reiser, B. J., & Edelson, D. (2004). Scaffolding analysis: extending the scaffolding metaphor to learning artifacts. In: Journal of the Learning Sciences, 13(3), 387–421.

    Google Scholar 

  • Simon, H.A. (1969). The sciences of the artificial. [The Karl Taylor Compton lectures.] Cambridge: The MIT Press.

  • Slotta, J.D. (2004). The Web-based Inquiry Science Environment (WISE): scaffolding knowledge integration in the science classroom. In M.C. Linn, P. Bell and E. Davis (Eds). Internet Environments for Science Education. 203–232. LEA.

  • Slotta, J. D. & Chi, M. T. H. (2006). The impact of ontology training on conceptual change: helping students understand the challenging topics in science. Cognition and Instruction, 24(2), 261–289.

  • Smith, A. (1977). An inquiry into the nature and causes of the wealth of nations. University of Chicago Press.

  • Stone, C. A. (1998). The metaphor of scaffolding: its utility for the field of learning disabilities. Journal of the learning Disabilities, 31, 344–364.

    Article  Google Scholar 

  • Tan, J., & Biswas, G. (2007). Simulation-based game learning environments: building and sustaining a fish tank. In Proceedings of the First IEEE International Workshop on Digital Game and Intelligent Toy Enhanced Learning (pp. 73–80). Jhongli, Taiwan.

  • Tisue, S., & Wilensky, U. (2004). NetLogo: a simple environment for modeling complexity. In International Conference on Complex Systems (pp. 16–21), May.

  • Van Gendt, K. & Verhagen, P. (2001). Visual testing. Searching for guidelines. Paper presented at the 24th National Convention of the Association for Educational Communications and Technology, Atlanta, Georgia.

  • Van Joolingen, W. R., De Jong, T., & Dimitrakopoulou, A. (2007). Issues in computer supported inquiry learning in science. Journal of Computer Assisted Learning, 23(2), 111–119.

    Article  Google Scholar 

  • van Someren, M. W., Reimann, P., Boshuizen, H., & de Jong, T. (1998). Learning with multiple representations. Advances in Learning and Instruction Series. New York: Elsevier Science, Inc.

  • Vygotsky, L. S. (1978). Mind and Society: The development of higher mental processes. Cambridge: Harvard University Press.

  • Wertsch, J. V. (1985). Vygotsky and the social formation of mind. Cambridge: Harvard University Press.

  • White, P., A. (1997). Naïve ecology: causal judgments about a simple ecosystem. The British Journal of Psychology, 88, 219–233.

  • Wilensky, U. (1999). NetLogo (and NetLogo User Manual), Center for Connected Learning and Computer-Based Modeling, Northwestern University.

  • Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: learning biology through constructing and testing computational theories—an embodied modeling approach. Cognition and Instruction, 24(2), 171–209.

  • Wilensky, U., & Resnick, M. (1999). Thinking in levels: a dynamic systems approach to making sense of the world. Journal of Science Education and Technology, 8(1), 3–19.

    Article  Google Scholar 

  • Wilensky, U., & Stroup, W. (2003). Embedded complementarity of object-based and aggregate reasoning in students developing understanding of dynamic systems. In annual meeting of the American Educational Research Association, Chicago, IL.

  • Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 17, 89–100.

    Article  Google Scholar 

Download references

Acknowledgments

The authors’ acknowledgment support from NSF Cyberlearning grant # 1237350 for this work.

Author information

Authors and Affiliations

  1. Institute for Software Integrated Systems, Department of Electrical Engineering and Computer Science, Vanderbilt University, 1025 16th Avenue South, Nashville, TN, 37212, USA

    Satabdi Basu & Gautam Biswas

  2. Department of Teaching and Learning, Vanderbilt University, Box 230, 230 Appleton Place, Nashville, TN, 37203-5721, USA

    Pratim Sengupta

Authors
  1. Satabdi Basu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pratim Sengupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gautam Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gautam Biswas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Basu, S., Sengupta, P. & Biswas, G. A Scaffolding Framework to Support Learning of Emergent Phenomena Using Multi-Agent-Based Simulation Environments. Res Sci Educ 45, 293–324 (2015). https://doi.org/10.1007/s11165-014-9424-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11165-014-9424-z

Keywords

Search

Navigation