# Sandboxes for Model-Based Inquiry

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## Abstract

In this article, we introduce a class of constructionist learning environments that we call *Emergent Systems Sandboxes* (*ESSs*), which have served as a centerpiece of our recent work in developing curriculum to support scalable model-based learning in classroom settings. ESSs are a carefully specified form of virtual construction environment that support students in creating, exploring, and sharing computational models of dynamic systems that exhibit emergent phenomena. They provide learners with “entity”-level construction primitives that reflect an underlying scientific model. These primitives can be directly “painted” into a sandbox space, where they can then be combined, arranged, and manipulated to construct complex systems and explore the emergent properties of those systems. We argue that ESSs offer a means of addressing some of the key barriers to adopting rich, constructionist model-based inquiry approaches in science classrooms at scale. Situating the ESS in a large-scale science modeling curriculum we are implementing across the USA, we describe how the unique “entity-level” primitive design of an ESS facilitates knowledge system refinement at both an individual and social level, we describe how it supports flexible modeling practices by providing both continuous and discrete modes of executability, and we illustrate how it offers students a variety of opportunities for validating their qualitative understandings of emergent systems as they develop.

### Keywords

Constructionism Design Agent-based modeling Scalability## Notes

### Acknowledgments

The research reported here is based upon work supported by the National Science Foundation under Grant #DRL-1020101. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.

### References

- Abrahamson D, Wilensky U (2004) ProbLab: a computer-supported unit in probability and statistics. In: Proceedings of the 28th annual meeting of the international group for the psychology of mathematics education. Bergen, NorwayGoogle Scholar
- Balacheff N, Kaput J (1997) computer-based learning environments in mathematics. In: Bishop AJ, Clements K, Keitel C, Kilpatrick J, Laborde C (eds) International handbook of mathematics education. Springer, Netherlands, pp 511–564. Retrieved from http://link.springer.com/chapter/10.1007/978-94-009-1465-0_15
- Blikstein P, Wilensky U (2004) MaterialSim: an agent-based simulation toolkit for learning materials science. In: International conference on engineering education. Gainsville, FloridaGoogle Scholar
- Blikstein P, Wilensky U (2009) An atom is known by the company it keeps: a constructionist learning environment for materials science using agent-based modeling. Int J Comput Math Learn 14:81–119CrossRefGoogle Scholar
- Brady C, White T, Davis S, Hegedus S (2013) SimCalc and the networked classroom. In: Hegedus S, Roschelle J (eds) The SimCalc vision and contributions: democratizing access to important mathematics. Springer, New York, NY, pp 99–121CrossRefGoogle Scholar
- Buckley BC, Gobert JD, Kindfield A, Horwitz P, Tinker R, Gerlits B, Willett J (2004) Model-based teaching and learning with BioLogica: what do they learn? how do they learn? how do we know? J Sci Educ Technol 13:23–41CrossRefGoogle Scholar
- Burg B, Kuhn A, Parnin C (2013) 1st international workshop on live programming (LIVE 2013). In: Proceedings of the 2013 international conference on software engineering, pp 1529–1530 Retrieved from http://dl.acm.org/citation.cfm?id=2487068
- Caperton IH (2010) Toward a theory of game-media literacy: playing and building as reading and writing. Int J Gaming Comput Mediat Simul 2(1):1–16Google Scholar
- Carey S (1988) Reorganization of knowledge in the course of acquisition. In: Ontogeny, phylogeny, and historical development. Ablex, Norwood, NJ, pp 1–27Google Scholar
- Clement J (1982) Students’ preconceptions in introductory mechanics. Am J Phys 50:66–70CrossRefGoogle Scholar
- Committee for the Workshops on Computational Thinking (2010) Report of a workshop on the scope and nature of computational thinking. National Research Council, Washington, DCGoogle Scholar
- Committee for the Workshops on Computational Thinking (2011) Report of a workshop on the pedagogical aspects of computational thinking. National Research Council, Washington, DCGoogle Scholar
- Davis SM (2010) Generative activities: making sense of 1098 functions. In: Lesh R, Galbraith PL, Haines CR, Hurford A (eds) Modeling students’ mathematical modeling competencies. Springer, New York, NY, pp 189–198CrossRefGoogle Scholar
- diSessa AA (1988) Knowledge in pieces. In: Constructivism in the computer age. Lawrence Erlbaum Associates, Inc., Hillsdale, NJ, pp. 49–70Google Scholar
- diSessa AA (1993) Toward an epistemology of physics. Cogn Instr 10:105–225CrossRefGoogle Scholar
- diSessa AA (1996) What do “just plain folk” know about physics? In: The handbook of education and human development: new models of learning, teaching, and schooling. Blackwell, Oxford, pp 709–730Google Scholar
- diSessa AA (2000) Changing minds: computers, learning and literacy. The MIT Press, Cambridge, MAGoogle Scholar
- diSessa AA, Sherin B (1998) What changes in conceptual change? Int J Sci Educ 20:1155–1191CrossRefGoogle Scholar
- Driver R, Squires A, Rushworth P, Wood-Robinson V (1994) Making sense of secondary science: research into children’s ideas. Routledge, New York, NYGoogle Scholar
- Finzer W, Erickson T, Binker J (2002) Fathom dynamic statistics software. Key Curriculum Press Technologies, Emeryville, CAGoogle Scholar
- Glaser R, Chi MTH (1988) Overview. In: The nature of expertise. Lawrence Erlbaum Associates, Inc., Hillsdale, NJ, pp xv–xxviiGoogle Scholar
- Gobert J, Horwitz P, Tinker B, Buckley B, Wilensky U, Levy ST, Dede C (2003) Modeling across the curriculum: scaling up modeling using technology. In: Paper presented at the twenty-fifth annual meeting of the cognitive science society, Boston, MAGoogle Scholar
- Goody J (1977) The domestication of the savage mind. Cambridge University Press, Cambridge, MAGoogle Scholar
- Guzdial M (1994) Software-realized scaffolding to facilitate programming for science learning. Interact Learn Environ 4(1):001–044CrossRefGoogle Scholar
- Hammer D (1996) Misconceptions or p-prims: how may alternative perspectives of cognitive structure influence instructional perceptions and intentions? J Learn Sci 5:97–127CrossRefGoogle Scholar
- Hammer D, Elby A, Scherr RE, Redish EF (2005) Resources, framing, and transfer. In: Transfer of learning from a modern multidisciplinary perspective. Information Age Publishing, Greenwich, CT, pp 89–120Google Scholar
- Hawkins D (1974) The informed vision: essays on learning and human nature. Algora Publishing, New York, NYGoogle Scholar
- Jackiw N (1991) The geometer’s sketchpad [Software]. Key Curriculum Press Technologies, Emeryville, CAGoogle Scholar
- Jona K, Wilensky U, Trouille L, Horn MS, Orton K, Weintrop D, Beheshti E (2014) Embedding computational thinking in science, technology, engineering, and math (CT-STEM). In: Paper presented at the future directions in computer science education summit meeting, Orlando, FLGoogle Scholar
- Kafai YB (1995) Minds in play: computer game design as a context for children’s learning. Lawrence Erlbaum Associates, Hillsdale, NJGoogle Scholar
- Kafai YB (2006) Constructionism. In: The Cambridge handbook of the learning sciences. Cambridge University Press, New YorkGoogle Scholar
- Kaput J, Roschelle J (1996) SimCalc MathWorlds. University of Massachusetts, Dartmouth, MAGoogle Scholar
- Konold C, Miller CD (2005) TinkerPlots: dynamic data exploration. Key Curriculum Press, Emeryville, CAGoogle Scholar
- Kuhn TS (1970) The structure of scientific revolutions. University of Chicago Press, ChicagoGoogle Scholar
- Laborde JM (1990) CABRI Geometry [Software]. Brooks-Cole Publishing Co, New York, NYGoogle Scholar
- Lehrer R, Schauble L (2006) Cultivating model-based reasoning in science education. In: Cambridge handbook learning sciences, pp 371–388Google Scholar
- Lesh R, Doerr H (2000) Symbolizing, communicating, and mathematizing: key components of models and modeling. In: Symbolizing and communicating in mathematics classrooms. Lawrence Erlbaum Associates, Hillsdale, NJ, pp 361–384Google Scholar
- Lesh R, Doerr H (2003) Foundations of a models and modeling perspective on mathematics teaching, learning, and problem solving. In: Beyond constructivism: a models & modeling perspective on mathematics teaching, learning, and problem solving. Lawrence Erlbaum Associates, Hillsdale, NJGoogle Scholar
- Lesh R, Doerr H (eds) (2003b) Beyond constructivism: a models & modeling perspective on mathematics teaching, learning, and problems solving. Lawrence Erlbaum Associates, Hillsdale, NJGoogle Scholar
- Lesh R, Doerr H (2012) Alternatives to trajectories and pathways to describe development in modeling and problem solving. In: Blum W, Ferri RB, Maaß K (eds) Mathematikunterricht im Kontext von Realität, Kultur und Lehrerprofessionalität. Springer Fachmedien Wiesbaden, Germany, pp 138–147Google Scholar
- Lesh R, Hoover M, Hole B, Kelly A, Post T (2000) Principles for developing thought-revealing activities for students and teachers. In: Kelly A, Lesh R (eds) Handbook of research design in mathematics and science education. Lawrence Erlbaum Associates, Mahwah, NJ, pp 591–646Google Scholar
- Lesh R, Hamilton E, Kaput J (eds) (2008) Models & modeling as foundations for the future in mathematics education. Lawrence Erlbaum Associates, Hillsdale, NJGoogle Scholar
- Levy ST, Wilensky U (2009a) Crossing levels and representations: the Connected Chemistry (CC1) curriculum. J Sci Educ Technol 18:224–242CrossRefGoogle Scholar
- Levy ST, Wilensky U (2009b) Students’ learning with the Connected Chemistry (CC1) curriculum: navigating the complexities of the particulate world. J Sci Educ Technol 18:243–254CrossRefGoogle Scholar
- Levy ST, Novak M, Wilensky U (2006) Students’ foraging through the complexities of the particulate world in the Connected Chemistry (MAC) curriculum. In: Annual meeting of the American educational research association, San Francisco. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.170.3899&rep=rep1&type=pdf
- Martin F, Hjalmarson M, Wankat P (2006) When the model is a program. In: Lesh R, Hamilton E, Kaput J (eds) Foundations for the future in mathematics education. Lawrence Erlbaum Associates, Hillsdale, NJGoogle Scholar
- McCloskey M (1984) Naive theories of motion. In: Mental models. Lawrence Erlbaum, Hillsdale, NJGoogle Scholar
- McDermott LC (1983) Critical review of research in the domain of mechanics. In: First international workshop research on physics education. Paris, pp 139–182Google Scholar
- Minsky M (1986) The society of mind. Simon & Schuster, New YorkGoogle Scholar
- Moreno-Armella L, Hegedus SJ (2009) Co-action with digital technologies. ZDM Math Educ 41(4):505–519. doi: 10.1007/s11858-009-0200-x CrossRefGoogle Scholar
- Moreno-Armella L, Sriraman B (2005) The articulation of symbol and mediation in mathematics education. ZDM Math Educ 37(6):476–486CrossRefGoogle Scholar
- Newell A, Simon HA (1972) Human problem solving, vol 14. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
- NGSS Lead States (2013) Next generation science standards: for states, by states. The National Academies Press, Washington, DCGoogle Scholar
- Noss R, Hoyles C (1996) Windows on mathematical meanings: learning cultures and computers. Kluwer, DordrechtCrossRefGoogle Scholar
- Osborne R, Wittrock M (1985) The generative learning model and its implications for science education. Stud Sci Educ 12:59–87CrossRefGoogle Scholar
- Papert S (1980) Mindstorms. Basic Books, New YorkGoogle Scholar
- Papert S, Harel I (1991) Situating constructionism. In: Constructionism. Ablex Publishing, New YorkGoogle Scholar
- Passmore CM, Svoboda J (2012) Exploring opportunities for argumentation in modelling classrooms. Int J Sci Educ 34(10):1535–1554CrossRefGoogle Scholar
- Posner GJ, Strike KA, Hewson PW, Gertzog WA (1982) Accommodation of a scientific conception: toward a theory of conceptual change. Sci Educ 66(2):211–227. doi: 10.1002/sce.3730660207
- Roth WM (1995) Affordances of computers in teacher–student interactions: the case of interactive physics™. J Res Sci Teach 32(4):329–347CrossRefGoogle Scholar
- Schwartz JL, Yerushalmy M (1987) The geometric supposer: an intellectual prosthesis for making conjectures. Coll Math J 18(1):58–65CrossRefGoogle Scholar
- Schwarz CV, Reiser BJ, Davis EA, Kenyon L, Achér A, Fortus D, Krajcik J (2009) Developing a learning progression for scientific modeling: making scientific modeling accessible and meaningful for learners. J Res Sci Teach 46(6):632–654CrossRefGoogle Scholar
- Sengupta P, Wilensky U (2005) NIELS: an emergent multi-agent based modeling environment for learning physics. In: Proceedings of the 4th international joint conference on autonomous agents and multi-agent systems (AAMAS). Utrecht, NetherlandsGoogle Scholar
- Sengupta P, Wilensky U (2009) Learning electricity with NIELS: thinking with electrons and thinking in levels. Int J Comput Math Learn 14:21–50Google Scholar
- Sengupta P, Farris AV, Wright M (2012) From agents to continuous change via aesthetics: learning mechanics with visual agent-based computational modeling. Technol Knowl Learn 17(1–2):23–42CrossRefGoogle Scholar
- Sengupta P, Kinnebrew JS, Basu S, Biswas G, Clark D (2013) Integrating computational thinking with K-12 science education using agent-based computation: a theoretical framework. Educ Inf Technol 18:351–380Google Scholar
- Sherin B (2006) Common sense clarified: the role of intuitive knowledge in physics problem solving. J Res Sci Teach 43:535–555CrossRefGoogle Scholar
- Sherin B, diSessa AA, Hammer D (1993) Dynaturtle revisited: learning physics through the collaborative design of a computer model. Interact Learn Environ 3:91–118CrossRefGoogle Scholar
- Simon HA, Chase WG (1973) Skill in chess: experiments with chess-playing tasks and computer simulation of skilled performance throw light on some human perceptual and memory processes. Am Sci 61(4):394–403Google Scholar
- Smith JP, diSessa AA, Roschelle J (1994) Misconceptions reconceived: a constructivist analysis of knowledge in transition. J Learn Sci 3(2):115–163. doi: 10.1207/s15327809jls0302_1 CrossRefGoogle Scholar
- Stewart J, Cartier JL, Passmore CM (2005) Developing understanding through model-based inquiry. In: How students learn: science in the classroom. The National Academies Press, Washington, DC, pp 515–565Google Scholar
- Stieff M, Wilensky U (2003) Connected chemistry—incorporating interactive simulations into the chemistry classroom. J Sci Educ Technol 12:285–302CrossRefGoogle Scholar
- Stroup W, Wilensky U (2014) On the embedded complementarity of agent-based and aggregate reasoning in students' developing understanding of dynamic systems. Technol Knowl Learn 19(1–2):1–34Google Scholar
- Stroup W, Ares N, Hurford A (2005) A dialectic analysis of generativity: issues of network-supported design in mathematics and science. Math Think Learn 7(3):181–206CrossRefGoogle Scholar
- Stroup W, Ares N, Hurford A, Lesh RA (2007) Diversity by design: the what, why and how of generativity in next-generation classroom networks. In: Lesh RA, Kaput JJ (eds) Foundations of the future: twenty-first century models and modeling. Lawrence Erlbaum, New York, NYGoogle Scholar
- Tasar MH (2010) What part of the concept of acceleration is difficult to understand: the mathematics, the physics, or both? ZDM Math Educ 42:469–482CrossRefGoogle Scholar
- Tisue S, Wilensky U (2004) NetLogo: design and implementation of a multi-agent modeling environment. In: Proceedings of the agent 2004 conference on social dynamics: interaction, reflexivity and emergence, Chicago, ILGoogle Scholar
- Trouille L, Beheshti E, Horn M, Jona K, Kalogera V, Weintrop D, Wilensky U (2013) Bringing computational thinking into the high school science and math classroom. In: American astronomical society meeting abstracts, vol 221Google Scholar
- Trowbridge DE, McDermott LC (1980) Investigation of student understanding of the concept of velocity in one dimension. Am J Phys 48:1020–1028CrossRefGoogle Scholar
- Trowbridge DE, McDermott LC (1981) Investigation of student understanding of the concept of acceleration in one dimension. Am J Phys 49:242–253CrossRefGoogle Scholar
- White BY (1993) ThinkerTools: causal models, conceptual change, and science education. Cogn Instr 10(1):1–100. doi: 10.2307/3233779 CrossRefGoogle Scholar
- Wieman CE, Adams WK, Perkins KK (2008) PhET: simulations that enhance learning. Science 322(5902):682–683CrossRefGoogle Scholar
- Wilensky U (1996) Making sense of probability through paradox and programming: a case study in a connected mathematics framework. In: Constructionism in practice: designing, thinking, and learning in a digital world. Lawrence Erlbaum, Mahwah, NJGoogle Scholar
- Wilensky U (1999a) NetLogo [computer software] version. Center for connected learning and computer-based modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo
- Wilensky U (1999b) GasLab: an extensible modeling toolkit for exploring micro-and-macro- views of gases. In: Roberts N, Feurzeig W, Hunter B (eds) Computer modeling and simulation in science education. Springer, Berlin, pp 151–178CrossRefGoogle Scholar
- Wilensky U (2001) Modeling nature’s emergent patterns with multi-agent languages. In: Proceedings of EuroLogo 2001. Linz, AustriaGoogle Scholar
- Wilensky U (2003) Statistical mechanics for secondary school: the GasLab modeling toolkit [special issue]. Int J Comput Math Learn 8(1): 1–4Google Scholar
- Wilensky U (2014) Computational thinking through modeling and simulation. In: Whitepaper presented at the summit on future directions in computer education. Orlando, FL. http://www.stanford.edu/~coopers/2013Summit/WilenskyUriNorthwesternREV.pdf
- Wilensky U, Papert S (2006) Restructurations: reformulations of knowledge disciplines through new representational forms. In: Annual meeting of the American educational research association, San FranciscoGoogle Scholar
- Wilensky U, Papert S (2010) Restructurations: reformulations of knowledge disciplines through new representational forms. In: Proceedings of constructionism 2010 Paris, FranceGoogle 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. Cogn Instr 24:171–209CrossRefGoogle Scholar
- Wilensky U, Stroup W (1999a) HubNet. Center for connected learning and computer-based modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo
- Wilensky U, Stroup W (1999) Learning through participatory simulations: Network-based design for systems learning in classrooms. In: Proceedings of the 1999 conference on computer support for collaborative learning, CSCL ‘99 Palo Alto, CAGoogle Scholar
- Wilensky U, Levy S, Novak M (2004) Connected chemistry curriculum. Retrieved from http://ccl.northwestern.edu/curriculum/ConnectedChemistry/
- Wilensky U, Brady C, Horn M (2014) Fostering computational literacy in science classrooms. Commun ACM 57(8):17–21Google Scholar
- Wilkerson-Jerde MH (2012) The DeltaTick project: learning quantitative change in complex systems with expressive technologies. Retrieved from http://dl.acm.org/citation.cfm?id=2522404
- Wilkerson-Jerde MH, Wilensky U (2010) Restructuring change, interpreting changes: the deltatick modeling and analysis toolkit. In: Proceedings of constructionism 2010. Paris, FranceGoogle Scholar
- Windschitl M, Thompson J, Braaten M (2008) Beyond the scientific method: model-based inquiry as a new paradigm of preference for school science investigations. Sci Educ 92(5):941–967CrossRefGoogle Scholar
- Wittrock M (1989) Generative processes of comprehension. Educ Psychol 24(4):345–376CrossRefGoogle Scholar
- Wittrock M (1992) Generative learning processes of the brain. Educ Psychol 27(4):531–541CrossRefGoogle Scholar