439
Views
5
CrossRef citations to date
0
Altmetric
Articles

Much.Matter.in.Motion: learning by modeling systems in chemistry and physics with a universal programing platform

ORCID Icon, & ORCID Icon
Pages 3128-3147 | Received 12 Feb 2021, Accepted 16 Apr 2021, Published online: 29 Apr 2021

References

  • Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science, 333(6046), 1096–1097. doi:10.1126/science.1204153
  • Akpan, J. P. (2001). Issues associated with inserting computer simulations into biology instruction: A review of the literature. The Electronic Journal for Research in Science & Mathematics Education, 5(3), 1–32.
  • Assaraf, O. B. Z., Dodick, J., & Tripto, J. (2013). High school students’ understanding of the human body system. Research in Science Education, 43(1), 33–56. doi:10.1007/s11165-011-9245-2
  • Bar-Yam, Y. (2003). Dynamics of complex systems. Perseus.
  • Barab, S. (2014). Design-based research: A methodological toolkit for engineering change. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 151–170). Cambridge University Press.
  • Barzilai, S., & Zohar, A. (2012). Epistemic thinking in action: Evaluating and integrating online sources. Cognition and Instruction, 30(1), 39–85. doi:10.1080/07370008.2011.636495
  • Basu, S., Kinnebrew, J. S., & Biswas, G. (2014, June). Assessing student performance in a computational-thinking based science learning environment. In international conference on intelligent tutoring systems (pp. 476–481), Springer, Cham.
  • Ben Horin, H., Orad, Y., & Welger, B. (2013). Materials Science for 7th grade. Center for Educational Technology. Tel-Aviv, Israel. [In Hebrew].
  • 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. International Journal of Computers for Mathematical Learning, 14(1), 81–119. doi:10.1007/s10758-009-9148-8
  • Buckley, B. C. (2000). Interactive multimedia and model-based learning in biology. International Journal of Science Education, 22(9), 895–935. doi:10.1080/095006900416848
  • Bybee, R. W. (2014). NGSS and the next generation of science teachers. Journal of Science Teacher Education, 25(2), 211–221. doi:10.1007/s10972-014-9381-4
  • Chen, D., & Stroup, W. (1993). General system theory: Towards a conceptual framework for science and technology education for all. Journal of Science Education and Technology, 2(3), 447–459. doi:10.1007/BF00694427
  • Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Pearson.
  • Damelin, D, Krajcik, J, Mcintyre, C, & Bielik, T. (2017). Students making system models: An accessible approach. Science Scope, 40(5), 78–82.
  • Dickes, A. C., Sengupta, P., Farris, A. V., & Basu, S. (2016). Development of mechanistic reasoning and multilevel explanations of ecology in third grade using agent-based models. Science Education, 100(4), 734–776. doi:10.1002/sce.21217
  • Eshach, H. (2014). The use of intuitive rules in interpreting students’ difficulties in reading and creating kinematic graphs. Canadian Journal of Physics, 92(1), 1–8. doi:10.1139/cjp-2013-0369
  • Forrester, J. W. (1968). Principles of systems. Pegasus Communications.
  • Gilbert, J. K., Boulter, C. J., & Elmer, R. (2000). Positioning models in Science Education and in design and Technology Education. In J. K. Gilbert & C. J. Boulter (Eds.), Developing models in Science Education (pp. 3–17). Kluwer Academic Publishers.
  • Goh, S. E., Yoon, S., Wang, J., Yang, Z., & Klopfer, E. (2012). Investigating the relative difficulty of various complex systems ideas in biology. 10th International Conference of the Learning Sciences: The Future of learning, ICLS 2012. Sydney, 2-6 July.
  • Gravemeijer, K., Cobb, P., & Whitenack, B. J. (2000). Symbolizing, modeling and instructional design. In P. Cobb, E. Yackel, & K. McClain (Eds.), Symbolizing and communicating in mathematics classrooms. Perspectives on discourse, tools, and instructional design (pp. 225–273). Lawrence Earlbaum Associates.
  • Holland, J. H. (1995). Hidden order: How adaptation builds complexity. HelixBooks/Addison-Wesley.
  • Jacobson, M. J., & Archodidou, A. (2000). The design of hypermedia tools for learning: Fostering conceptual change and transfer of complex scientific knowledge. The Journal of the Learning Sciences, 9(2), 145–199. doi:10.1207/s15327809jls0902_2
  • Jacobson, M., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15(1), 11–34. doi:10.1207/s15327809jls1501_4
  • Kauffman, S. (1995). At home in the universe: The search for the laws of self-organization and complexity. Oxford University Press.
  • King, G. P., Bergan-Roller, H., Galt, N., Helikar, T., & Dauer, J. T. (2019). Modelling activities integrating construction and simulation supported explanatory and evaluative reasoning. International Journal of Science Education, 41(13), 1764–1786. doi:10.1080/09500693.2019.1640914
  • Lehrer, R., & Schauble, L. (2006). Cultivating model-based reasoning in science education. Cambridge University Press.
  • Levy, S. T., Saba, J., & Hel-Or, H. (2018). Much.Matter.in.Motion (MMM) platform: Widget-based platform for constructing computational models in science. Systems Learning & Development Lab, University of Haifa, Israel.
  • Levy, S. T., & Wilensky, U. (2009a). Crossing levels and representations: The connected chemistry (CC1) curriculum. Journal of Science Education and Technology, 18(3), 224–242. doi:10.1007/s10956-009-9152-8
  • Levy, S. T., & Wilensky, U. (2009b). Students’ learning with the Connected Chemistry (CC1) curriculum: Navigating the complexities of the particulate world. Journal of Science Education and Technology, 18(3), 243–254. doi:10.1007/s10956-009-9145-7
  • Lobato, J. (2006). Alternative Perspectives on the Transfer of Learning: History, Issues, and Challenges for Future Research. Journal of the Learning Sciences, 15(4), 431–449.
  • Luckin, R, & Boulay, Du. (2002), The role of communication in learning to model (pp. 99–126). Lawrence Erlbaum Associates.
  • Mahaffy, P. G., Krief, A., Hopf, H., Mehta, G., & Matlin, S. A. (2018). Reorienting chemistry education through systems thinking. Nature Reviews Chemistry, 2(4), 1–3. doi:10.1038/s41570-018-0126
  • Mandinach, E. B., & Cline, H. F. (1994). Project components: Systems thinking, graphical user interface, and modeling. In J. Hadfield (Ed.), Classroom dynamics: Implementing a technology-based learning environment (pp. 45–72). Hillsdale, NJ: Lawrence Erlbaum.
  • Metcalf, S. H. J., Krajcik, J., & Soloway, E. (2000). MODEL-IT: A design retrospective. In M. J. Jacobson & R. B. Kozma (Eds.), Innovations in science and mathematics education (pp. 77–116). Lawrence Erlbaum Associates.
  • Mitchell, M. (2009). Complexity: A guided tour. Oxford University Press.
  • NGSS Lead States. (2013). Next generation science standards: For states, by states. National Academies Press.
  • Ramadas, J. (2009). Visual and spatial modes in science learning. International Journal of Science Education, 31(3), 301–318. doi:10.1080/09500690802595763
  • Rates, C. A., Mulvey, B. K., & Feldon, D. F. (2016). Promoting conceptual change for complex systems understanding: Outcomes of an agent-based participatory simulation. Journal of Science Education and Technology, 25(4), 610–627. doi:10.1007/s10956-016-9616-6
  • Repenning, A., Webb, D., & Ioannidou, A. (2010). Scalable game design and the development of a checklist for getting computational thinking into public schools. Proceedings of the 41st ACM technical symposium on computer science education (pp. 265-269), Milwaukee: ACM Press.
  • Resnick, L. B. (1987). Education and learning to think. National Academy Press.
  • Resnick, M. (1994). Turtles, termites and traffic jams: Explorations in massively parallel microworlds. MIT Press.
  • Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B., & Kafai, Y. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60–67. doi:10.1145/1592761.1592779
  • Roschelle, J, Kaput, J J, & Stroup, W. (2000, Innovations in science and mathematics education (pp. 47–76). Lawrence Erlbaum Associates.
  • Russ, R. S., Scherr, R. E., Hammer, D., & Mikeska, J. (2008). Recognizing mechanistic reasoning in student scientific inquiry: A framework for discourse analysis developed from philosophy of science. Science Education, 92(3), 499–525. doi:10.1002/sce.20264
  • Samon, S., & Levy, S. T. (2010). Who understands the gas? Curricular unit that includes models and worksheets [in Hebrew] targeting kinetic molecular theory and gas laws for junior-high school students. Adaptation of the Connected Chemistry, CC1. Evanston, IL: Center for Connected Learning and Computer Based Modeling, Northwestern. University Retrieved from http://cclnorthwestern.edu/curriculum/chemistry/.
  • Samon, S., & Levy, S. T. (2017). Micro–macro compatibility: When does a complex systems approach strongly benefit science learning? Science Education, 101(6), 985–1014. doi:10.1126/science.1204153
  • Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., Shwartz, Y., Hug, B., & Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 46(6), 632–654.
  • Sengupta, P, & Farris, A. V. (2012). Learning kinematics in elementary grades using agent-based computational modeling: A visual programming-based approach. Proceedings of the 11th International Conference on interaction design and children, 12-15 June, pp. 78–87.
  • Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380. doi:10.1007/s10639-012-9240-x
  • Settlage, J., Jr (1994). Conceptions of natural selection: A snapshot of the sense-making process. Journal of Research in Science Teaching, 31(5), 449–457. doi:10.1002/tea.3660310503
  • Smetana, L. K., & Bell, R. L. (2012). Computer simulations to support science instruction and learning: A critical review of the literature. International Journal of Science Education, 34(9), 1337–1370. doi:10.1080/09500693.2011.605182
  • Sweeney, L. B., & Sterman, J. D. (2007). Thinking about systems: Student and teacher conceptions of natural and social systems. System Dynamics Review, 23(2-3), 285–311. doi:10.1002/sdr.366
  • VanLehn, K., Wetzel, J., Grover, S., & Van De Sande, B. (2016). Learning how to construct models of dynamic systems: An initial evaluation of the dragoon intelligent tutoring system. IEEE Transactions on Learning Technologies, 10(2), 154–167. doi:10.1109/TLT.2016.2514422
  • Wagh, A., & Wilensky, U. (2018). Evobuild: A quickstart toolkit for programming agent-based models of evolutionary processes. Journal of Science Education and Technology, 27(2), 131–146. doi:10.1007/s10956-017-9713-1
  • White, B. Y., & Frederiksen, J. R. (2000). Technological tools and instructional approaches for making science inquiry accessible to all. In M. J. Jacobson, & R. B. Kozma (Eds.), Innovations in science and Mathematics education (pp. 321–359). Lawrence Erlbaum Associates.
  • Wilensky, U. (1999). NetLogo models library [Computer software]. In Center for connected learning and computer-based modeling. Northwestern University. Available from http://cclnorthwestern.edu/netlogo/models/.
  • Wilensky, U. (2003). Statistical mechanics for secondary school: The GasLab modeling toolkit. International Journal of Computers for Mathematical Learning, 8(1), 1–41. doi:10.1023/A:1025651502936
  • Wilensky, U, & Papert, S. (2010). Restructurations: Reformulations of knowledge disciplines through new representational forms. In J. Clayson & I. Kalas (Eds.), Proceedings of the Constructionism 2010 conference. Constructionism. Paris. 10–14 Aug, p 97.
  • Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social and engineered complex systems with NetLogo. MIT Press.
  • 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. doi:10.1207/s1532690xci2402_1
  • 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. doi:10.1023/A:1009421303064
  • Wilkerson-Jerde, M., Wagh, A., & Wilensky, U. (2015). Balancing curricular and pedagogical needs in computational construction kits: Lessons from the DeltaTick project. Science Education, 99(3), 465–499. doi:10.1002/sce.21157
  • Yoon, S. A. (2008). An evolutionary approach to harnessing complex systems thinking in the science and technology classroom. International Journal of Science Education, 30(1), 1–32. doi:10.1080/09500690601101672
  • Zhang, Z. H., & Linn, M. C. (2011). Can generating representations enhance learning with dynamic visualizations? Journal of Research in Science Teaching, 48(10), 1177–1198. doi:10.1002/tea.20443

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.