Technology, Knowledge and Learning

, Volume 19, Issue 1–2, pp 53–79 | Cite as

Constructible Authentic Representations: Designing Video Games that Enable Players to Utilize Knowledge Developed In-Game to Reason About Science

  • Nathan R. HolbertEmail author
  • Uri Wilensky


While video games have become a source of excitement for educational designers, creating informal game experiences that players can draw on when thinking and reasoning in non-game contexts has proved challenging. In this paper we present a design principle for creating educational video games that enables players to draw on knowledge resources gained in-game to reason about non-game phenomena. Games that incorporate this design principle, which we call constructible authentic representations, engage players in the construction of artifacts that are visually and epistemologically aligned to tools and representations utilized in the target domain. We illustrate this principle with a study of six children (ages 7–13) playing a racing video game of our own design. Players that struggled with a formal graphing task before playing the game showed improvement on the same task in post-game interviews creating qualitatively correct velocity versus time graph that incorporated key kinematic features such as moments of constant velocity and varying degrees of acceleration. An analysis of pre- and post-game clinical interviews also revealed that players more fluidly drew on a variety of knowledge resources when reasoning about the game, real world, and formal representations. We hypothesize that designing games to include constructible authentic representations may allow for the creation of educational video games that can survive in the non-school gaming ecosystem.


Video games Design Construction Epistemology Physics 



The authors would like to thank Bruce Sherin, Reed Stevens, Rosemary Russ, Corey Brady, Michael Horn, Pryce Davis, David Weintrop, and all members of the CCL for their insightful comments on this work.


  1. Annetta, L. A. (2008). Video games in education: Why they should be used and how they are being used. Theory into Practice, 47(3), 229–239.CrossRefGoogle Scholar
  2. Bamberger, J. (1996). Turning music theory on its ear: Do we hear what we see; do we see what we say? International Journal of Computers for Mathematical Learning, 1, 33–55.CrossRefGoogle Scholar
  3. Barab, S., Sadler, T. D., Heiselt, C., Hickey, D., & Zuiker, S. (2007a). Relating narrative, inquiry, and inscriptions: Supporting consequential play. Journal of Science Education and Technology, 16(1), 59–82.CrossRefGoogle Scholar
  4. Barab, S., Thomas, M., Dodge, T., Carteaux, R., & Tuzun, H. (2005). Making learning fun: Quest Atlantis, a game without guns. Educational Technology Research and Development, 53, 86–107.CrossRefGoogle Scholar
  5. Barab, S., Zuiker, S., Warren, S., Hickey, D., Ingram-Goble, A., Kwon, E., et al. (2007b). Situationally embodied curriculum: Relating formalisms and contexts. Science Education, 91(5), 750–782. doi: 10.1002/sce.20217.CrossRefGoogle Scholar
  6. Barendregt, W., & Bekker, T. M. (2011). The influence of the level of free-choice learning activities on the use of an educational computer game. Computers & Education, 56(1), 80–90. doi: 10.1016/j.compedu.2010.08.018.Google Scholar
  7. Behesthi, E., Jona, K., Horn, M., Trouille, L., Weintrop, D., & Wilensky, U. (2013). The physics of angry birds. CT-STEM Group.
  8. Boucher-Genesse, F., Riopel, M., & Potvin, P. (2011). Research results for Mecanika, a game to learn Newtonian concepts. In C. Steinkuhler, C. Martin, & A. Ochsner (Eds.), Proceedings of the 7th Annual Games, Learning, and Society Conference. Madison, WI.Google Scholar
  9. Brofenbrenner, U. (1979). The ecology of human development: Experiment by nature and design. Cambridge, MA: Harvard University Press.Google Scholar
  10. Cheng, P. C.-H. (2011). Probably good diagrams for learning: Representational epistemic recodification of probability theory. Topics in Cognitive Science, 3(3), 475–498.Google Scholar
  11. Clark, D. B., & Martinez-Garza, M. (2012). Prediction and explanation as design mechanics in conceptually integrated digital games to help players articulate the tacit understandings they build through game play. In C. Steinkuhler, K. Squire, & S. Barab (Eds.), Games, learning, and society: Learning and meaning in the digital age. Cambridge: Cambridge University Press.Google Scholar
  12. Clark, D. B., Nelson, B., Chang, H., Martinez-Garza, M., Slack, K., & D’Angelo, C. M. (2011). Exploring Newtonian mechanics in a conceptually-integrated digital game: Comparisons of learning and affective outcomes for students in Taiwan and the United States. Computers & Education, 57, 2178–2195.CrossRefGoogle Scholar
  13. Clark, D. B., Nelson, B., D’Angelo, C. M., Slack, K., & Menekse, M. (2010). SURGE: Assessing students’ intuitive and formalized understandings about kinematics and Newtonian mechanics through immersive game play. Presented at the Annual Meeting of the American Educational Research Association, Denver, CO.Google Scholar
  14. Collins, A. (1995). Design issues for learning environments. In S. Vosniadou, E. de Corte, H. Mandle, & G. Robert (Eds.), International perspectives on the psychological foundations of technology-based learning environments (pp. 347–361). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  15. Cooper, S., Dann, W., & Pausch, R. (2000). Alice: A 3-D tool for introductory programming concepts. Journal of Computing Sciences in Colleges, 15, 107–116.Google Scholar
  16. diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10, 105–225.CrossRefGoogle Scholar
  17. diSessa, A. A., Hammer, D., Sherin, B., & Kolpakowski, T. (1991). Inventing graphing; Meta-representational expertise in children. Journal of Mathematical Behavior, 10, 117–160.Google Scholar
  18. Gee, J. P. (2003). What video games have to teach us about learning and literacy. New York: Palgrave Macmillan.Google Scholar
  19. Gee, J. P. (2007). Good video games and good learning. New York, NY: Peter Lang.Google Scholar
  20. Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95, 393–408.CrossRefGoogle Scholar
  21. Gick, M. L., & Holyoak, K. J. (1980). Analogical problem-solving. Cognitive Psychology, 12, 306–355.CrossRefGoogle Scholar
  22. Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15, 1–38.CrossRefGoogle Scholar
  23. Ginsberg, H. P. (1997). Entering the child’s mind: The clinical interview in psychological research and practice. New York: Cambridge University Press.CrossRefGoogle Scholar
  24. Habgood, J. M. P., & Ainsworth, S. E. (2011). Motivating children to learn effectively: Exploring the value of intrinsic integration in educational games. Journal of the Learning Sciences, 20, 169–206.CrossRefGoogle Scholar
  25. Hammer, D., & Elby, A. (2002). On the form of personal epistemology. In B. K. Hofer & P. R. Pintrich (Eds.), Personal epistemology: The psychology of beliefs and knowledge and knowing (pp. 169–190). Mahwah, NJ: Erlbaum.Google Scholar
  26. Hammer, D., Elby, A., Scherr, R. E., & Redish, E. F. (2005). Resources, framing, and transfer. In J. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 89–120). Greenwich, CT: Information Age Publishing.Google Scholar
  27. Holbert, N. (2009). Learning Newton while crashing cars. Presented at the Games, Learning, and Society 5.0, Madison, WI.Google Scholar
  28. Holbert, N. (2013). Reimagining game design (RiGD): Exploring the design of constructible representations for science reasoning (doctoral dissertation). Evanston, IL: Northwestern University.Google Scholar
  29. Holbert, N., & Wilensky, U. (2010a). FormulaT racing. Evanston, IL: Center for Connected Learning and Computer-based Modeling.Google Scholar
  30. Holbert, N., & Wilensky, U. (2010b). FormulaT racing: Combining gaming culture and intuitive sense of mechanism for video game design. In K. Gomez & J. Radinsky (Eds.), Proceedings of the 9th International Conference of the Learning Sciences. Chicago, IL.Google Scholar
  31. Holbert, N., & Wilensky, U. (2011). FormulaT racing: Designing a game for kinematic exploration and computational thinking. In C. Steinkuhler, C. Martin, & A. Ochsner (Eds.), Proceedings of 7th Annual Games, Learning, and Society Conference. Madison, WI.Google Scholar
  32. Holbert, N., & Wilensky, U. (2012). Particles!. Evanston, IL: Center for Connected Learning and Computer-based Modeling.Google Scholar
  33. Itō, M. (2008). Education vs. entertainment: A cultural history of children’s software. In K. Salen (Ed.), The ecology of games: Connecting youth, games, and learning (pp. 89–116). Cambridge, MA: MIT Press.Google Scholar
  34. Itō, M. (2010). Hanging out, messing around, and geeking out: Kids living and learning with new media. Cambridge, MA: MIT Press.Google Scholar
  35. Kafai, Y. B. (1996). Learning design by making games: Children’s development of design strategies in the creation of a complex computational artifact. In Y. B. Kafai & M. Resnick (Eds.), Constructionism in practice: Designing, thinking, and learning in a digital world. Mahwah, NJ: Lawrence Erlbaum. Google Scholar
  36. Kelleher, C., & Pausch, R. (2006). Lessons learned from designing a programming system to support middle school girls creating animated stories. In J. Grundy & J. Howse (Eds.), IEEE symposium on visual languages and human-centric computing (pp. 165–172). Brighton, UK.Google Scholar
  37. Ketelhut, D. J., Clarke, J., & Nelson, B. C. (2010). The development of river city, a multi-user virtual environment-based scientific inquiry curriculum: Historical and design evolutions. In M. J. Jacobson & P. Reimann (Eds.), Designs for learning environments of the future (pp. 89–110). US: Springer.CrossRefGoogle Scholar
  38. Lenhart, A., Kahne, J., Middaugh, E., Macgill, A. R., Evans, C., & Vitak, J. (2008). Teens, video games, and civics. PEW Internet & American Life Project.Google Scholar
  39. Levy, S. T., & Wilensky, U. (2009). Students’ learning with the connected chemistry (CC1) curriculum: Navigating the complexities of the particulate world. Journal of Science Education and Technology, 18, 243–254.CrossRefGoogle Scholar
  40. Mestre, Jose. (2002). Transfer of learning: Issues and research agenda. Arlington, VA: National Science Foundation.Google Scholar
  41. Minsky, M. (1986). The society of mind. New York: Simon & Schuster.Google Scholar
  42. Nemirovsky, R., Tierney, C., & Wright, T. (1998). Body motion and graphing. Cognition and Instruction, 16, 119–172.CrossRefGoogle Scholar
  43. Noss, R., & Hoyles, C. (1996). Windows on mathematical meanings: Learning cultures and computers. Dordrecht: Kluwer Academic Press.CrossRefGoogle Scholar
  44. Papastergiou, M. (2009). Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation. Computers & Education, 52(1), 1–12.CrossRefGoogle Scholar
  45. Papert, S. (1980). Mindstorms: Children, computers and powerful ideas. New York: Basic Books.Google Scholar
  46. Papert, S. (2000). What’s the big idea? Toward a pedagogy of idea power. IBM Systems Journal, 39(3.4), 720–729.CrossRefGoogle Scholar
  47. Papert, S., & Harel, I. (1991). Situating constructionism. In S. Papert & I. Harel (Eds.), Constructionism. New York: Ablex Publishing.Google Scholar
  48. Piaget, J. (1929). The child’s conception of the world. London: Rowman & Littlefield. Google Scholar
  49. Pratt, D., & Noss, R. (2010). Designing for mathematical abstraction. International Journal of Computers for Mathematical Learning, 15(2), 81–97.Google Scholar
  50. Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., et al. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60–67.CrossRefGoogle Scholar
  51. Robertson, J., & Howells, C. (2008). Computer game design: Opportunities for successful learning. Computers & Education, 50(2), 559–578. doi: 10.1016/j.compedu.2007.09.020.CrossRefGoogle Scholar
  52. Roschelle, J., Kaput, J. J., & Stroup, W. (2000). SimCalc: Accelerating students’ engagement with the mathematics of change. In M. J. Jacobson & R. B. Kozma (Eds.), Innovations in science and mathematics education: Advanced designs for technologies of learning (pp. 47–76). Hillsdale, NJ: Earlbaum.Google Scholar
  53. Russ, R. S., Lee, V. R., & Sherin, B. L. (2012). Framing in cognitive clinical interviews about intuitive science knowledge: Dynamic student understandings of the discourse interaction. Science Education, 96(4), 573–599. doi: 10.1002/sce.21014.CrossRefGoogle Scholar
  54. Sherin, B. (2000). How students invent representations of motion: A genetic account. Journal of Mathematical Behavior, 19, 399–441.CrossRefGoogle Scholar
  55. Squire, K. (2006). From content to context: Videogames as designed experience. Educational Researcher, 35, 19–29.CrossRefGoogle Scholar
  56. Squire, K., & Barab, S. (2004). Replaying history: Engaging urban underserved students in learning world history through computer simulation games. In Proceedings of the 6th International Conference on the Learning Sciences (pp. 505–512).Google Scholar
  57. Tasar, M. H. (2010). What part of the concept of acceleration is difficult to understand: The mathematics, the physics, or both? ZDM, 42, 469–482.CrossRefGoogle Scholar
  58. The Alice Team. (n.d.). Alice. What is Alice?
  59. Trowbridge, D. E. (1989). Graphs and tracks: An application of manipulable graphics. Academic Computing, May, 24–25. Google Scholar
  60. Trowbridge, D. E., & McDermott, L. C. (1981). Investigation of student understanding of the concept of acceleration in one dimension. American Journal of Physics, 49, 242–253.CrossRefGoogle Scholar
  61. Valve Corporation. (2013). Teach with portals.
  62. Wilensky, U. (1991). Abstract meditations on the concrete and concrete implications for mathematics education. In I. Harel & S. Papert (Eds.), Constructionism. Norwood, NJ: Ablex Publishing Corp.Google Scholar
  63. Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories–and embodied modeling approach. Cognition and Instruction, 24, 171–209.CrossRefGoogle Scholar
  64. Wilkerson-Jerde, M. H., & Wilensky, U. (2010). Restructuring change, interpreting changes: The deltatick modeling and analysis toolkit. In J. E. Clayson (Ed.), Proceedings of constructionism 2010. Paris, France.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Northwestern UniversityEvanstonUSA

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