EvoBuild: A Quickstart Toolkit for Programming Agent-Based Models of Evolutionary Processes

• Bamberger, J. (2001). Turning Music Theory on its Ear: Do we hear what we see; do we see what we say? In Multidisciplinary Perspectives on Musicality: The Seashore Symposium. Iowa City: University of Iowa Press.

• 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. Int J Comput Math Learn, 14(1), 81–119.

  Article  Google Scholar 

• Bruckman, A. (1997). Moose Crossing: Construction, Community, and Learning in a Networked Virtual World for Kids. Cambridge: Massachusetts Institute of Technology.

  Google Scholar 

• Centola, D., Wilensky, U., & McKenzie, E. (2000). A Hands-on Mondeling Approach to Evolution: Learning about the Evolution of Cooperation and Altruism through Multi-Agent Modeling- The EACH Project. In Fourth Annual International Conference of the Learning Sciences. Ann Arbor.

• Collins, A., Joseph, D., & Bielaczyc, K. (2004). Design Research: Theoretical and Methodological Issues. J Learn Sci, 13(1), 15–42. https://doi.org/10.1207/s15327809jls1301.

  Article  Google Scholar 

• Dickes, A. C., & Sengupta, P. (2013). Learning Natural Selection in 4th Grade with Multi-Agent-Based Computational Models. Res Sci Educ, 43(3), 921–953. https://doi.org/10.1007/s11165-012-9293-2.

  Article  Google Scholar 

• Edwards, L. D. (1995). Microworlds as Representations. In A. A. diSessa, C. Hoyles, R. Noss, & L. D. Edwards (Eds.), Computers and Exploratory Learning (pp. 127–154). Heidelberg: Springer Berlin Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-57799-4_8.

  Chapter  Google Scholar 

• Harel, I., & Papert, S. (1991). Constructionism : research reports and essays, 1985-1990. Norwood: Ablex Pub. Corp.

• Horn, M., & Wilensky, U. (2011). NetTango 1.0. Evanston, IL: Center for Connected Learning and Computer-based Modeling, Northwestern University.

• Horn, M. S., Brady, C., Hjorth, A., Wagh, A., & Wilensky, U. (2014). Frog Pond: A Codefirst Learning Environment on Evolution and Natural Selection. In Proceedings of the 2014 Conference on Interaction Design and Children (pp. 357–360). New York: ACM. https://doi.org/10.1145/2593968.2610491.

• Ioannidou, A., Repenning, A., Lewis, C., Cherry, G., & Rader, C. (2003). Making Constructionism Work in the Classroom. Int J Comput Math Learn, 8, 63–108.

  Article  Google Scholar 

• Kafai, Y. B., Carter Ching, C., & Marshall, S. (1997). Children as designers of educational multimedia software. Comput Educ, 29(2–3), 117–126. https://doi.org/10.1016/S0360-1315(97)00036-5.

  Article  Google Scholar 

• Kahn, K. (2007a). Building computer models from small pieces. In G. Wainer (Ed.), SCSC Proceedings of the 2007 Summer Computer Simulation Conference (pp. 931–936). San Diego.

• Kahn, K. (2007b). The BehaviourComposer 2.0: a web-based tool for composing NetLogo code fragments. Retrieved July 5, 2013, from http://academia.edu/329330/The_BehaviourComposer_2.0_a_web-based_tool_for_composing_NetLogo_code_fragments

• Kahn, K., & Noble, H. (2010). The BehaviourComposer 2.0: a web-based tool for composing NetLogo code fragments. In J. Clayson & I. Kalas (Eds.), Constructionist approaches to create learning, thinking and education: Lessons for the 21st century: Proceedings for Constructionism 2010. Paris.

• Kahn, K., Noble, H., & Hjorth, A. (2012). Three-minute Constructionist Experiences. In C. Kynigos, J. Clayson, & Y. Nikoleta (Eds.), Proceedings of Constructionism 2012, Theory Practice and Impact (pp. 349–358). Athens.

• Klopfer, E., Yoon, S., & Um, T. (2005). Teaching Complex Dynamic Systems to Young Students with StarLogo. J Comput Math Sci Teach, 24(2), 157–178.

  Google Scholar 

• Konold, C., & Miller, C. D. (2005). TinkerPlots: Dynamic data exploration. Computer Software. Emeryville: Key Curriculum Press Retrieved from http://scholar.google.com/scholar?cluster=5929212600541009408&hl=en&oi=scholarr.

  Google Scholar 

• Louca, L. T., & Zacharia, Z. C. (2007). The Use of Computer-based Programming Environments as Computer Modelling Tools in Early Science Education: The cases of textual and graphical program languages. Int J Sci Educ, 30(3), 287–323. https://doi.org/10.1080/09500690601188620.

  Article  Google Scholar 

• Metz, K. E. (2010). Scaffolding children’s understanding of the fit between organisms and their environment in the context of the practices of science. In Proceedings of the 9th International Conference of the Learning Sciences - Volume 1 (pp. 396–403). International Society of the Learning Sciences. Retrieved from http://dl.acm.org/citation.cfm?id=1854360.1854411.

• NGSS Lead States (2013). Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press.

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

  Google Scholar 

• Rader, C., Cherry, G., Brand, A., Repenning, A., & Lewis, C. (1998). Principles to Scaffold Mixed Textual and Iconic End-User Programming Languages. In Proceedings of the 1998 I.E. Symposium of Visual Languages (pp. 187–194). Nova Scotia.

• Repenning, A., & Sumner, T. (1995). Agentsheets: a medium for creating domain-oriented visual languages. Computer, 28(3), 17–25. https://doi.org/10.1109/2.366152.

  Article  Google Scholar 

• Resnick, M., Maloney, J., Monroy-Hernandez, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B. & Kafai, Y. (2009). Scratch: Programming for All. In Communications of the ACM (Vol. 52, pp. 60–67).

• 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. https://doi.org/10.1007/s10639-012-9240-x.

• Sherin, B. (2001). A Comparison of Programming Languages and Algebraic Notation as Expressive Languages for Physics. Int J Comput Math Learn, 6(1), 1–61. https://doi.org/10.1023/A:1011434026437.

  Article  Google Scholar 

• Simpson, G., Hoyles, C., & Noss, R. (2005). Designing a programming-based approach for modelling scientific phenomena. J Comput Assist Learn, 21(2), 143–158. https://doi.org/10.1111/j.1365-2729.2005.00121.x.

  Article  Google Scholar 

• Smith, D. C., Cypher, A., & Schmucker, K. (1996). Making Programming Easier for Children. Interactions, 3(5), 58–67. https://doi.org/10.1145/234757.234764.

  Article  Google Scholar 

• Turkle, S., & Papert, S. (1992). Epistemological Pluralism and the Revaluation of the Concrete. Journal of Mathematical Behavior, 11(1), 3–33.

  Google Scholar 

• Wagh, A. (2016). Building v/s Exploring Models: Comparing Learning of Evolutionary Processes through Agent-based Modeling (A dissertation). Northwestern University, Evanston.

• Wagh, A., Cook-Whitt, K., & Wilensky, U. (2017). Bridging inquiry-based science and constructionism: Exploring the alignment between students tinkering with code of computational models and goals of inquiry. Journal of Research in Science Teaching. https://doi.org/10.1002/tea.21379

• Wagh, A., & Wilensky, U. (2012a). Breeding birds to learn about artificial selection: Two birds with one stone? In: J. van Aalst, K. Thompson, M. Jacobson, & P. Reimann (Eds.), 10th International Conference of the Learning Sciences: The Future of Learning (Vol. 2: Short papers, pp. 426–430). Sydney, Australia, July 2-6.

• Wagh, A., & Wilensky, U. (2012b). Mechanistic Explanations of Evolutionary Change Facilitated by Agent-based Models. Paper presented at the American Educational Research Association, Vancouver, April 13-17.

• Wagh, A., & Wilensky, U. (2013). Leveling the Playing Field: Making Multi-level Evolutionary Processes Accessible through Participatory Simulations. In N. Rummel, M. Kapur, M. Nathan, & S. Puntambekar (Eds.), To See the World and a Grain of Sand: Learning across Levels of Space, Time and Scale (Vol. 2, pp. 181–184). Madison, Wisconsin, June 15-19: Proceedings of CSCL.

• Wagh, A., & Wilensky, U. (2014). Seeing patterns of change: Supporting student noticing in building models of natural selection. In G. Futschek & C. Kynigos (Eds.), Constructionism and Creativity, Proceedings of the 3rd International Constructionism Conference. Vienna: OCG (Österreichische Computer Gesellschaft).

• Wagh, A., Novak, M., Soylu, F., & Wilensky, U. (2016). Integrating agent-based modeling & case Study to learn about population dynamics: A design framework. Paper presented at NARST, Baltimore, April 14-17.

• Weintrop, D. (2015). Minding the Gap Between Blocks-Based and Text-Based Programming (Abstract Only). In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 720–720). New York: ACM. https://doi.org/10.1145/2676723.2693622.

• Wilensky, U. (1999a). GasLab—An extensible modeling toolkit for connecting micro- and macro- properties of gases. In N. Roberts, W. Feurzeig, & B. Hunter (Eds.), Computer Modeling in Science and Mathematics Education. Berlin: Springer-Verlag.

  Google Scholar 

• Wilensky, U. (1999b). NetLogo. http://ccl.northwestern.edu/netlogo/ . Evanston: Center for Connected Learning and Computer-based Modeling, Northwestern University.

• Wilensky, U. (2003). Statistical mechanics for secondary school: The GasLab modeling toolkit. International Journal of Computers for Mathematical Learning[Special Issue on Agent-Based Modeling], 8(1), 1–41.

  Google Scholar 

• Wilensky, U., & Centola, D. (2007). Simulated Evolution: Facilitating Students’ Understanding of the Multiple Levels of Fitness through Multi-Agent Modeling. In Proceedings of the Fourth International Conference on Complex Systems. Nashua.

• Wilensky, U., & Novak, M. (2010). Understanding evolution as an emergent process: Learning with agent-based models of evolutionary dynamcis. In R. Taylor & M. Ferrari (Eds.), Epistemology and Science Education: Understanding the Evolution vs. Intelligent Design Controversy. New York, Routledge.

• 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(2), 171–209.

  Article  Google Scholar 

• Wilkerson, M., & Wilensky, U. (2010). Restructuring Change, Interpreting Changes: The DeltaTick Modeling and Analysis Toolkit. In J. Clayson & I. Kalas (Eds.), Proceedings of the Constructionism 2010 Conference. Paris, France. https://doi.org/Aug 10-14 .

• Wilkerson, M. H., Gravel, B. E., & Macrander, C. A. (2014). Exploring Shifts in Middle School Learners’ Modeling Activity While Generating Drawings, Animations, and Computational Simulations of Molecular Diffusion. J Sci Educ Technol, 24(2–3), 396–415. https://doi.org/10.1007/s10956-014-9497-5.

  Google Scholar 

• Wilkerson, M., Wagh, A., & Wilensky, U. (2015). Balancing Curricular and Pedagogical Needs in Computational Construction Kits: Lessons From the DeltaTick Project. Sci Educ, 99(3), 465–499. https://doi.org/10.1002/sce.21157.

  Article  Google Scholar 

• Xiang, L., & Passmore, C. (2010). The Use of an Agent-Based Programmable Modeling Tool in 8th Grade Students’ Model-Based Inquiry. Journal of the Research Center for Educational Technology, 6(2), 130–147.

  Google Scholar 

• Yoon, S., Anderson, E., Klopfer, E., Koehler-Yom, J., Sheldon, J., Schoenfeld, I., Wendel, D., Scheintaub, H., Oztok, M., Evans, C., & Goh, S.-E. (2016). Designing Computer-supported Complex Systems Curricula for the Next Generation Science Standards in High School Science Classrooms. Systems, 4(4).

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