Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework

Education and Information Technologies

Volumn 18, Issue 2, 2013, Pages 351-380

  Sengupta, Pratim ^a,b   Kinnebrew, John S ^a   Basu, Satabdi ^a   Biswas, Gautam ^a   Clark, Douglas ^a,b  

^a VANDERBILT UNIVERSITY   (United States)

^b VANDERBILT UNIVERSITY   (United States)

Author keywords

Agent based modeling and simulation; Biology education; Computational modeling; Computational thinking; Learning by design; Multi agent systems; Physics education; Science education; Visual programming

Indexed keywords

EID: 84876025297     PISSN: 13602357     EISSN: 15737608     Source Type: Journal    
DOI: 10.1007/s10639-012-9240-x     Document Type: Article

References (94)

1. ACM K-12 Taskforce. (2003). A Model Curriculum for K-12 Computer Science: Final Report of the ACM K-12 Task Force Curriculum Committee. New York, NY: CSTA.
2. Aristotle (350 BCE/2002) Nichomachean ethics. New York: Oxford University Press.
3. Basu, S., Sengupta, P., & Biswas, G. (In Review). A scaffolding framework to support learning in multi-agent based simulation environments. Research in Science Education.
4. Basu, S., Kinnebrew, J., Dickes, A., Farris, A. V., Sengupta, P., Winger, J., & Biswas, G. (2012). A Science Learning Environment using a Computational Thinking Approach. In: Proceedings of the 20th International Conference on Computers in Education, Singapore.
5. 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, 81-119.
6. Bravo, C., van Joolingen, W. R., & deJong, T. (2006). Modeling and simulation in inquiry learning: Checking solutions and giving advice. Simulation, 82(11), 769-784.
7. Chi, M. T. H. (2005). Common sense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Sciences, 14, 161-199.
8. 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.
9. Chinn, C. A., & Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63, 1-49.
10. Conway, M. (1997). Alice: Easy to Learn 3D Scripting for Novices, Technical Report, School of Engineering and Applied Sciences, University of Virginia, Charlottesville, VA.
11. Corcoran, T., Mosher, F., & Rogat, A. (2009). Learning progressions in science: An evidence-based approach to reform (RR-63). Philadelphia, PA: Consortium for Policy Research in Education.
12. Cross, N. (2004). Expertise in design: An overview. Design Studies, 25, 427-441.
13. Dickes, A., & Sengupta, P. (2012). Learning Natural Selection in 4th Grade with Multi Agent-Based Computational Models. Research in Science Education. doi: 10. 1007/s11165-012-9293-2.
14. diSessa, A. A. (1985). A principled design for an integrated computational environment. Human-Computer Interaction, 1(1), 1-47.
15. diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10(2/3), 105-225.
16. diSessa, A. A. (2000). Changing minds: Computers, learning, and literacy. Cambridge, MA: MIT Press.
17. diSessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22(3), 293-331.
18. diSessa, A. A. (2001). Changing minds: Computers, learning, and literacy. The MIT Press.
19. diSessa, A. A., & Abelson, H. (1986). BOXER: A reconstructible computational medium. Communications of ACM, 29(9), 859-868.
20. diSessa, A. A., Abelson, H., & Ploger, D. (1991a). An overview of boxer. Journal of Mathematical Behavior, 10(1), 3-15.
21. diSessa, A., Hammer, D., Sherin, B., & Kolpakowski, T. (1991b). Inventing graphing: Children's meta-representational expertise. Journal of Mathematical Behavior, 10(2), 117-160.
22. Driver, R., Newton, P., & Osborne, J. (2000). Establishing the norms of scientific argumentation in classrooms. Science Education, 84(3), 287-313.
23. Duschl, R. (2008). Science education in three part harmony: Balancing conceptual, epistemic and social learning goals. In J. Green, A. Luke, & G. Kelly (Eds.), Review of research in education (Vol. 32, pp. 268-291). Washington, DC: AERA.
24. Duschl, R. A., & Osborne, J. (2002). Supporting and promoting argumentation discourse in science education. Studies in Science Education, 38, 39-72.
25. Dykstra, D. I., Jr., & Sweet, D. R. (2009). Conceptual development about motion and force in elementary and middle school students. American Journal of Physics, 77(5), 468-476.
26. Edelson, D. C. (2001). Learning-for-use: A framework for the design of technology-supported inquiry activities. Journal of Research in Science Teaching, 38(3), 355-385.
27. Elby, A. (2000). What students' learning of representations tells us about constructivism. Journal of Mathematical Behavior, 19, 481-502.
28. Ford, M. J. (2003). Representing and meaning in history and in classrooms: Developing symbols and conceptual organizations of free-fall motion. Science & Education, 12(1), 1-25.
29. Giere, R. N. (1988). Explaining science: A cognitive approach. Chicago: University of Chicago Press.
30. Guzdial, M. (1995). Software-realized scaffolding to facilitate programming for science learning. Interactive Learning Environments, 4(1), 1-44.
31. Guzdial, M. (2008). Paving the way for computational thinking. Communications of the ACM: Education Column. 51(8).
32. Halloun, I. A., & Hestenes, D. (1985). The initial knowledge state of college physics students. American Journal of Physics, 53(11), 1043-1056.
33. Hambrusch, S., Hoffmann, C., Korb, J. T., Haugan, M., & Hosking, A. L. (2009). A multidisciplinary approach towards computational thinking for science majors. In Proceedings of the 40th ACM Technical Symposium on Computer Science Education (SIGCSE '09). ACM, New York, NY, USA, 183-187.
34. 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.
35. Harel, I., & Papert, S. (1991). Software design as a learning environment. Constructionism. Norwood, NJ: Ablex Publishing Corporation. pp. 51-52. ISBN 0-89391-785-0.
36. Hegedus, S. J., & Kaput, J. J. (2004). An Introduction to the Profound Potential of Connected Algebra Activities: Issues of Representation, Engagement, and Pedagogy. Proceedings of the 28th Conference of the International Group for the Psychology of Mathematics Education, 3, 129-136.
37. Ho, C. H. (2001). Some phenomena of problem decomposition strategy for design thinking: Differences between novices and experts. Design Studies, 22(1), 27-45.
38. Hundhausen, C. D., & Brown, J. L. (2007). What You See Is What You Code: A "live" algorithm development and visualization environment for novice learners. Journal of Visual Languages and Computing, 18, 22-47.
39. 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.
40. Kahn, K. (1996). ToonTalk: An animated programming environment for children. Journal of Visual Languages and Computing.
41. Kaput, J. (1994). Democratizing access to calculus: New routes using old routes. In A. Schoenfeld (Ed.), Mathematical thinking and problem solving (pp. 77-156). Hillsdale, NJ: Lawrence Erlbaum.
42. Kelleher, C., & Pausch, R. (2005) Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys, Vol. (37) 83-137.
43. Klahr, D., Dunbar, K., & Fay, A. L. (1990). Designing good experiments to test bad hypotheses. In J. Shrager & P. Langley (Eds.), Computational models of scientific discovery and theory formation (pp. 355-401). San Mateo, CA: Morgan Kaufman.
44. Klopfer, E., Yoon, S., & Um, T. (2005). Teaching complex dynamic systems to young students with StarLogo. The Journal of Computers in Mathematics and Science Teaching, 24(2), 157-178.
45. Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., Puntambekar, S., & Ryan, M. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting learning by design into practice. The Journal of Learning Sciences, 12(4), 495-547.
46. Kramer, J. (2007). Is abstraction the key to computing? Communications of the ACM, 50(4), 36-42. April 2007.
47. Kynigos, C. (2001). E-slate Logo as a basis for constructing microworlds with mathematics teachers (pp. 65-74). Lintz, Austria: Proceedings of the Ninth Eurologo Conference.
48. Kynigos, C. (2007). Using half-baked microworlds to challenge teacher educators' knowing. Journal of Computers for Math Learning, 12(2), 87-111.
49. Larkin, J. H., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335-1342.
50. 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.
51. Lehrer, R., Schauble, L., & Lucas, D. (2008). Supporting development of the epistemology of inquiry. Cognitive Development, 23(4), 512-529.
52. Leinhardt, G., Zaslavsky, O., & Stein, M. M. (1990). Functions, graphs, and graphing: Tasks, learning and teaching. Review of Educational Research, 60, 1-64.
53. 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.
54. Locke, J. (1690/1979). An essay concerning human understanding. New York: Oxford University Press.
55. Maloney, J., Burd, L., Kafai, Y., Rusk, N., Silverman, B., & Resnick, M. (2004) Scratch: A sneak preview. In Proceedings of Creating, Connecting, and Collaborating through Computing, 104-109.
56. McCloskey, M. (1983). Naive theories of motion. In D. Gentner & A. Stevens (Eds.), Mental models (pp. 299-324). Hillsdale, NJ: Lawrence Erlbaum.
57. National Research Council. (2008). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: National Academy Press.
58. National Research Council. (2010). Report of a workshop on the scope and nature of computational thinking. Washington, DC: The National Academies Press.
59. Nersessian, N. J. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In R. N. Giere (Ed.), Cognitive models of science (pp. 3-45). MN: University of Minnesota Press. Minneapolis.
60. Oshima, Y. (2005). Kedama: A GUI-based interactive massively parallel particle programming system. Proceedings of the 2005 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC'05).
61. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York, NY: Basic Books, Inc.
62. Papert, S. (1991). Situating constructionism. In I. Harel & S. Papert (Eds.), Constructionism. Norwood, NJ: Ablex Publishing Corporation.
63. Penner, D. E., Lehrer, R., & Schauble, L. (1998). From physical models to biomechanics: A design-based modeling approach. Journal of the Learning Sciences, 7(3-4), 429-449.
64. Perkins, D. N., & Simmons, R. (1988). Patterns of misunderstanding: An integrative model for science, math, and programming. Review of Educational Research, 58(3), 303-326.
65. Redish, E. F., & Wilson, J. M. (1993). Student programming in the introductory physics course: M. U. P. P. E. T. American Journal of Physics, 61, 222-232.
66. Reiner, M., Slotta, J. D., Chi, M. T. H., & Resnick, L. B. (2000). Naive physics reasoning: A commitment to substance-based conceptions. Cognition and Instruction, 18(1), 1-34.
67. Repenning, A. (1993). Agentsheets: A tool for building domain-oriented visual programming. Conference on Human Factors in Computing Systems, 142-143.
68. Resnick, M. (1994). Turtles, termites, and traffic jams: Explorations in massively parallel microworlds. Cambridge, MA: MIT Press.
69. Roschelle, J., & Teasley, S. D. (1994). The construction of shared knowledge in collaborative problem solving. NATO ASI Series F Computer and Systems Sciences, 128, 69-69.
70. Roschelle, J., Digiano, C., Pea, R. D., & Kaput, J. (1999). Educational Software Components of Tomorrow (ESCOT), Proceedings of the International Conference on Mathematics/Science Education & Technology (M/SET), March 1-4, 1999. San Antonio, USA.
71. Sandoval, W. A., & Millwood, K. (2005). The quality of students' use of evidence in written scientific explanations. Cognition and Instruction, 23(1), 23-55.
72. Schauble, L., Klopfer, L. E., & Raghavan, K. (1991). Students' transition from an engineering model to a science model of experimentation. Journal of Research in Science Teaching, 28, 859-882.
73. Schmidt, D. C. (2006). Guest editor's introduction: Model-driven engineering. Computer, 39(2), 25-31.
74. Segedy, J. R., Kinnebrew, J. S., & Biswas, G. (2012). Promoting metacognitive learning behaviors using conversational agents in a learning by teaching environment. Educational Technology Research & Development.
75. Sengupta, P. (2011). Design Principles for a Visual Programming Language to Integrate Agent-based modeling in K-12 Science. In: Proceedings of the Eighth International Conference of Complex Systems (ICCS 2011), pp 1636-1637.
76. 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 & Children, pp 78-87.
77. 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.
78. 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, NY: Springer.
79. Sengupta, P., Farris, A. V., & Wright, M. (2012). From agents to aggregation via aesthetics: Learning mechanics with visual agent-based computational modeling. Technology, Knowledge & Learning, 17(1-2), 23-42.
80. Sherin, B. (2001). A comparison of programming languages and algebraic notation as expressive languages for physics. International Journal of Computers for Mathematics Learning:, 6, 1-61.
81. Sherin, B., diSessa, A. A., & Hammer, D. M. (1993). Dynaturtle revisited: Learning physics through collaborative design of a computer model. Interactive Learning Environments, 3(2), 91-118.
82. 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.
83. Smith, D., Cypher, A., & Tesler, L. (2000). Programming by example: Novice programming comes of age. Communications of the ACM, 43(3), 75-81.
84. Soloway, E. (1993). Should we teach students to program? Communications of the ACM, 36(10), 21-24.
85. 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.
86. Tanimoto, S. L. (1990). VIVA: A visual language for image processing. Journal of Visual Languages and Computing, 1, 127-139.
87. Von Glaserfeld, E. (1991). Abstraction, re-presentation, and reflection: An interpretation of experience and of Piaget's approach. In L. P. Steffe (Ed.), Epistemological foundations of mathematical experience (pp. 45-67). New York: Springer.
88. White, B. Y., & Frederiksen, J. R. (1990). Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42(1), 99-157.
89. Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling (http://ccl. northwestern. edu/netlogo). Northwestern University, Evanston, IL.
90. Wilensky, U., & Novak, M. (2010). Understanding evolution as an emergent process: Learning with agent-based models of evolutionary dynamics. In R. S. Taylor & M. Ferrari (Eds.), Epistemology and science education: Understanding the evolution vs. Intelligent design controversy. New York: Routledge.
91. 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 & Instruction, 24(2), 171-209.
92. 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).
93. Wing, J. M. (2006) Computational Thinking. Communications of the ACM, vol. 49, no. 3 March 2006, pp. 33-35.
94. Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society, 366, 3717-3725.