Skip to main content
SpringerLink
Log in

Lowering the Learning Threshold: Multi-Agent-Based Models and Learning Electricity

  • Chapter
  • First Online:
Models and Modeling

Part of the book series: Models and Modeling in Science Education ((MMSE,volume 6))

Abstract

Over the past few decades, misconceptions researchers have shown that middle and high school students find electricity very difficult to understand. We argue that a learning environment based on emergent multi-agent-based computational representations of electric current in linear circuits can greatly reduce these difficulties and lower the learning threshold so that even fifth graders can develop a deep understanding of electric current. In an emergent multi-agent-based perspective, aggregate- or system-level phenomena (e.g., electric current) emerge from simple rule-based interactions (e.g., push, pull, and bouncing) between many individual-level agents (e.g., electrons and ions). Specifically, we discuss the epistemic affordances and challenges of such an emergent pedagogical approach in the context of middle and high school learners’ development of understanding of electric current as a “rate.” We report a design-based research study that demonstrates how a suite of emergent multi-agent-based computational models (NIELS: NetLogo Investigations in Electromagnetism) can be designed and appropriated to represent electric current in linear resistive circuits so that it is intuitive and easily understandable by a wide range of physics novices: from 5th to 12th grade students.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abrahamson, D., Janusz, R. M., & Wilensky, U. (2006). There once was a 9-Block—A middle-school design for probability and statistics. Journal of Statistics Education, 8, 1.

    Google Scholar 

  • Abrahamson, D., & Wilensky, U. (2005). Understanding chance: From student voice to learning supports in a design experiment in the domain of probability. In G. M. Lloyd, M. Wilson, J. L. M. Wilkins, & S. L. Behm, (Eds.), Proceedings of the twenty seventh annual meeting of the North American chapter of the international group for the psychology of mathematics education (pp. 1–7). Roanoke, VA: Virginia Tech University.

    Google Scholar 

  • American Association for the Advancement of Science. (1993). Benchmarks for scientific literacy. New York: Oxford University Press.

    Google Scholar 

  • Ashcroft, J. N., & Mermin, D. (1976). Solid state physics. Holt: Rinegart and Winston.

    Google Scholar 

  • Bagno, E., & Eylon, B.-S. (1997). From problem solving to a knowledge structure: An example from the domain of electromagnetism. American Journal of Physics, 65, 726.

    Article  Google Scholar 

  • Bagno, E., Eylon, B.-S., & Ganiel, U. (2000). From fragmented knowledge to a knowledge structure: Linking the domains of mechanics and electromagnetism. Physics Education Research Supplement; American Journal of Physics, 68(S2), S16–S26.

    Google Scholar 

  • Belcher, J. W., & Olbert, S. (2003). Field line motion in classical electromagnetism. American Journal of Physics, 71, 220.

    Article  Google Scholar 

  • Blikstein, P., & Wilensky, U. (2006). A case study of multi-agent-based simulation in undergraduate materials science education. Paper presented at the Annual Conference of the American Society for Engineering Education, Chicago, IL, June 18–21.

    Google Scholar 

  • Blikstein, P., & Wilensky, U. (June 2008). Implementing agent-based modeling in the classroom—lessons from empirical studies in undergraduate engineering education. In G. Kanselaar, J. van Merinboer, P. Kirschner, & T. de Jong (Eds.), Proceedings of the international conference of the learning sciences (ICLS). (pp. 266–267). Utrecht: ICLS.

    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 

  • Centola, D., McKenzie, E., & Wilensky, U. (2000). Survival of the groupiest: facilitating students’ understanding of multi-level evolution through multi-agent modeling—the EACH project, The 4th international conference on complex systems. Nashua, NH: New England Complex Systems Institute.

    Google Scholar 

  • Chabay, R. W., & Sherwood, B. A. (2000). Matter & Interactions I: Modern Mechanics and Matter & Interactions II: Electric & Magnetic Interactions. New York: Wiley.

    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., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Cohen, R., Eylon, B. S., & Ganiel, U. (1983). Potential difference and current in simple electric circuits: A study of students’ concepts. American Journal of Physics, 51, 407–412.

    Article  Google Scholar 

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

    Google Scholar 

  • diSessa, A. A., & Sherin, B. L. (1998). What changes in conceptual change? International Journal of Science Education, 20(10), 1155–1191.

    Article  Google Scholar 

  • Drude, P. (1900). Zur Elektronentheorie der Metalle. Annalen der Physik, 1:566–613.

    Google Scholar 

  • Duit, R. (1991). On the role of analogies and metaphors in learning science. Science Education, 75, 649–672.

    Article  Google Scholar 

  • Dupin, J., & Johsua, S. (1987). Conceptions of French pupils concerning electric circuits: Structure and evolution. Journal of Research in Science Teaching, 24, 791–806.

    Article  Google Scholar 

  • Einstein, A. (1950). Out of my later years. New York: Philosophical Library.

    Google Scholar 

  • Eylon, B. -S., & Ganiel, U. (1990). Macro-micro relationships: The missing link between electrostatics and electrodynamics in student reasoning. International Journal of Science Education, 12(1), 79–94.

    Article  Google Scholar 

  • Fredericksen, J., & White, B. (1992). Mental models and understanding: A problem for science education. In E. Scanlon, & T. O’Shea, (Eds.), New directions in educational technology. (pp. 211–226). New York: Springer.

    Google Scholar 

  • Frederiksen, J., & White, B. (1988). Teaching and learning generic modeling and reasoning skills. Journal of Interactive Learning Environments, 5, 33–51.

    Article  Google Scholar 

  • Frederiksen, J., White, B., & Gutwill, J. (1999). Dynamic mental models in learning science: The importance of constructing derivational linkages among models. Journal of Research in Science Teaching, 36(7), 806–836.

    Article  Google Scholar 

  • Forbus, K., & Gentner, D. (1986). Learning physical domains: towards a new theoretical framework. In R. Michalski, J. Carbonell, & T. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach (Vol. 2), 311–348. Tioga press.

    Google Scholar 

  • Gentner, D., & Gentner, D. R. (1983). Flowing waters or teeming crowds: Mental models of electricity. In D. Gentner, & A. L. Stevens, (Eds.), Mental models (pp. 99–129), Lawrence Erlbaum Associates, New Jersey.

    Google Scholar 

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

    Article  Google Scholar 

  • Haertel, H. (1987). A qualitative approach to electricity. Report No. IRL87-001lI, Xerox PARC, Palo Alto, CA.

    Google Scholar 

  • Hammer, D. (1996). Misconceptions or p-prims: How may alternative perspectives of cognitive structure influence instructional perceptions and intentions? Journal of the Learning Sciences, 5(2), 97–127.

    Article  Google Scholar 

  • Hardiman, P. T., Well, A. D., & Pollatsek, A. (1984). The usefulness of a balance model in understanding the mean. Journal of Educational Psychology, 7(6), 792–801.

    Google Scholar 

  • Hartel, H. (1982). The electric circuit as a system: A new approach. European Journal of Science Education, 4, 45–55.

    Google Scholar 

  • Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence. New York: Basic.

    Google Scholar 

  • Jackson, S. (1965). The growth of logical thinking in normal and subnormal children. British Journal of Educational Psychology, 3(5), 255–258.

    Google Scholar 

  • Joshua, S., & Dupin, J. J. (1987). Taking into account student conceptions in instructional strategy: An example in physics. Cognition and Instruction, 4, 117–135.

    Article  Google Scholar 

  • Kaput, J. & West, M. (1994). Missing-value proportional reasoning problems: factors affecting informal reasoning patterns. In G. Harel, & J. Confrey (Eds.), The development of multiplicative reasoning in the learning of mathematics. (pp. 235–287). Albany, NY: State University of New York Press.

    Google Scholar 

  • Lasser, C., & Omohundro, S. M. (1986). The essential starlisp manual. Waltham, MA: Thinking Machines Corporation.

    Google Scholar 

  • Lee, V. R., & Sherin, B. (2006). Beyond transparency: How students make representations meaningful. Proceedings of the Seventh International Conference of the Learning Sciences, Bloomington, IN, 397–403.

    Google Scholar 

  • Lehrer, R., & Schauble, L. (2006). Scientific thinking and science literacy. In W. Damon, R. Lerner, K. A. Renninger, & I. E. Sigel (Eds.), Handbook of child psychology, 6th Ed., Vol. 4: Child psychology in practice (pp. 153–196). Hoboken, NJ: Wiley.

    Google Scholar 

  • Levy, S. T., Kim, H., & Wilensky, U. (2004). Connected Chemistry—A study of secondary students using agent-based models to learn chemistry. Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA, April 12–16.

    Google Scholar 

  • Levy, S. T., & Wilensky, U. (2005). Students’ patterns in exploring NetLogo models, embedded in the Connected Chemistry curriculum. In J. Gobert (Chair) & J. Pellegrino (Discussant) (Eds.), Logging students’ learning in complex domains: Empirical considerations and technological solutions. Paper presented at the annual meeting of the American Educational Research Association, Montreal, QC, Canada, April 11–15.

    Google Scholar 

  • Levy, S. T., & Wilensky, U. (2008). Inventing a “mid-level” to make ends meet: Reasoning through the levels of complexity. Cognition and Instruction, 26(1), 1–47.

    Google Scholar 

  • Levy, S. T., & Wilensky, U. (2009). Crossing levels and representations: The Connected Chemistry (CC1) curriculum. Journal of Science Education and Technology, 18(3), 224–242.

    Google Scholar 

  • Lovell, K. (1961). A follow-up study of Inhelder and Piaget’s “The growth of logical thinking.” British Journal of Psychology, 52, 143–153.

    Google Scholar 

  • Metz, K. E. (2004). Children’s understanding of scientific inquiry: Their conceptualization of uncertainty in investigations of their own design. Cognition and Instruction, 2(22), 219–291.

    Article  Google Scholar 

  • National Academies Press. (1996). National science education standards. Retrieved March 30, 2010, from http://www.nap.edu/catalog.php?record_id=4962

  • Niederrer, H., & Goldberg, F. (1996). Learning processes in electric circuits. St. Louis: Presented at NARST.

    Google Scholar 

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

    Google Scholar 

  • Papert, S. (1991). Situating constructionism. In I. Harel, & S. Papert (Eds.), Constructionism. Norwood, NJ: Ablex Publishing Corporation.

    Google Scholar 

  • Pfund, H., & Duit, R. (1998). Bibliography: Students’ alternative frameworks and science education. Kiel, Alemania: IPN.

    Google Scholar 

  • Rand, W., Novak, M., & Wilensky, U. (2007). BEAGLE curriculum. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University.

    Google Scholar 

  • Reiner, M., Slotta, J. D., Chi, T. H., & Resnick, L. B. (2000). Naïve physics reasoning: A commitment to substance-based conceptions. Cognition and Instruction, 18(1), 1–34.

    Article  Google Scholar 

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

    Google Scholar 

  • Roschelle, J. (1991). Students’ construction of qualitative physics knowledge: Learning about velocity and acceleration in a computer microworld. Unpublished doctoral dissertation, University of California, Berkeley.

    Google Scholar 

  • Sengupta, P. (2009). Designing across ages: Multi-agent based models and learning electricity. Unpublished doctoral dissertation. Northwestern University, USA.

    Google Scholar 

  • Sengupta, P., & Wilensky, U. (2005). N.I.E.L.S: An emergent multi-agent based modeling environment for learning physics. Proceedings of the agent-based systems for human learning workshop, 4th international joint conference on Autonomous Agents and Multiagent Systems (AAMAS 2005), Utrecht, Netherlands.

    Google Scholar 

  • Sengupta, P., & Wilensky, U. (2006) NIELS: An agent-based modeling environment for learning electromagnetism. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.

    Google Scholar 

  • Sengupta, P., & Wilensky, U. (2008a). Designing across ages: On the low-threshold-high-ceiling nature of netlogo based learning environments. Paper presented at the annual meeting of the American Educational Research Association (AERA 2008), New York.

    Google Scholar 

  • Sengupta, P., & Wilensky, U. (2008b). On the learnability of electricity as a complex system. In G. Kanselaar, J. van Merri’nboer, P. Kirschner, & T. de Jong (Eds.), Proceedings of the Eighth International Conference of the Learning Sciences—ICLS 2008 (Vol. 3, pp. 122–124). Utrecht, The Netherlands: ICLS.

    Google Scholar 

  • 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 

  • Smith, J. P., diSessa, A. A., & Roschelle, J. (1993). Misconceptions reconceived: A constructivist analysis of knowledge in transition. Journal of the Learning Sciences, 3(2), 115–163.

    Google Scholar 

  • Siegler, R. S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8, 481–520.

    Google Scholar 

  • Siegler, R. S. (1978). The origins of scientific reasoning. In R. S. Siegler (Ed.), Children’s thinking: What develops? (pp. 109–151). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Siegler, R. S., & Klahr, D. (1982). Why do children learn? The relationship between existing knowledge and the acquisition of new knowledge. In R. Glaser (Ed.), Advances in instructional psychology (Vol. 2, pp. 121–211). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Simon, H. A. (1969). The sciences of the artificial. Cambridge, MA: The MIT Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Stieff, M., & Wilensky, U. (2003). Connected chemistry—incorporating interactive simulations into the chemistry classroom. Journal of Science Education and Technology, 12(3), 285–302.

    Article  Google Scholar 

  • Thompson, P. W. (1994). The development of the concept of speed and its relationship to concepts of rate. In G. Harel & J. Confrey (Eds.), The development of multiplicative reasoning in the learning of mathematics (pp. 179–234). Albany, NY: SUNY Press.

    Google Scholar 

  • White, B., & Frederiksen, J. (1998). Inquiry, modeling, and metacognition: making science accessible to all students. Cognition and Instruction, 16(1), 3–118.

    Article  Google Scholar 

  • White, B., Frederiksen, J., & Spoehr, K. (1993). Conceptual models for understanding the behavior of electrical circuits. In M. Caillot (Ed.), Learning electricity and electronics with advanced educational technology (pp. 77–95). New York: Springer.

    Google Scholar 

  • Wilensky, U. (1991). Abstract meditations on the concrete and concrete implications for mathematics education. In I. Harel, & S. Papert (Eds.), Constructionism (pp. 193–203). Norwood, MA: Ablex.

    Google Scholar 

  • Wilensky, U. (1993). Connected mathematics: Building concrete relationships with mathematical knowledge. Unpublished doctoral dissertation Cambridge, MA: MIT.

    Google Scholar 

  • Wilensky, U. (1999a). NetLogo. Center for connected learning and computer-based modeling. Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo. Accessed 1 Jul 2010.

  • Wilensky, U. (1999b). GasLab: An extensible modeling toolkit for exploring micro-and macro-views of gases. In N. Roberts, W. Feurzeig, & B. Hunter, (Eds.), Computer modeling and simulation in science education (pp. 151–178). Berlin: Springer.

    Google Scholar 

  • Wilensky, U. (2001). Modeling nature’s emergent patterns with multi-agent languages proceedings of EuroLogo 2001. Austria: Linz.

    Google Scholar 

  • Wilensky, U. (2003). Statistical mechanics for secondary school: The GasLab modelling toolkit. International Journal of Computers for Mathematical Learning, 8(1), 1–41. (special issue on agent-based modeling)

    Article  Google Scholar 

  • Wilensky, U. (April, 2006). Complex systems and restructuration of scientific disciplines: Implications for learning, analysis of social systems, and educational policy. In J. Kolodner (Chair), C. Bereiter (Discussant), & J. D. Bransford (Discussant), Complex systems, learning, and education: Conceptual principles, methodology. Paper presented at the Annual Meeting of the American Educational Researchers’ Association.

    Google Scholar 

  • Wilensky, U., Hazzard, E., & Longenecker, S. (2000). A bale of turtles: A case study of a middle school science class studying complexity using StarLogoT. Paper presented at the meeting of the Spencer Foundation, New York, October 11–13.

    Google Scholar 

  • Wilensky, U., & Papert, S. (in preparation). Restructurations: Reformulations of knowledge disciplines through new representational forms. (Manuscript in preparation)

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wilensky, U., & Resnick, M. (1995). New thinking for new sciences: Constructionist approaches for exploring complexity. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.

    Google Scholar 

  • 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).

    Google Scholar 

  • Wilensky, U., Papert, S., Sherin, B., diSessa, A., Kay, A., & Turkle, S. (2005). Center for learning and computation-based knowledge (CLiCK). Proposal to the National Science Foundation—Science of Learning Center. Unpublished manuscript.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mind, Matter & Media Lab, Department of Teaching and Learning, Peabody College, Vanderbilt University, Nashville, TN, 37240, USA

    Pratim Sengupta

  2. Center for Connected Learning & Computer Based Modelling, Departments of Learning Sciences and Computer Science, Northwestern University, Evanston, IL, 60208, USA

    Uri Wilensky

Authors
  1. Pratim Sengupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Uri Wilensky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pratim Sengupta .

Editor information

Editors and Affiliations

  1. Bahrain Teachers College, Manama, Bahrain

    Myint Swe Khine

  2. Bahrain Teachers College, Manama, Bahrain

    Issa M. Saleh

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Sengupta, P., Wilensky, U. (2011). Lowering the Learning Threshold: Multi-Agent-Based Models and Learning Electricity. In: Khine, M., Saleh, I. (eds) Models and Modeling. Models and Modeling in Science Education, vol 6. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0449-7_7

Download citation

Publish with us

Policies and ethics

Search

Navigation