An Evolving Design Framework for Game-Based Learning Platforms

  • Fengfeng Ke
  • Valerie Shute
  • Kathleen M. Clark
  • Gordon Erlebacher
Part of the Advances in Game-Based Learning book series (AGBL)


In this concluding chapter, we discuss an evolving, experiential design framework for game-based learning platforms, by synthesizing the salient design problem-solving events, experiences, and solution-exploration findings reported in the previous chapters. It is not aimed to be prescriptive or exhaustive, but acts as a starting point for specifying the structuring and important concepts of the interdisciplinary design of game-based learning. Results of our phenomenological inquiry assert that solving a game design problem is mainly about problem structuring or transforming an indeterministic problem space to partially limited. The structuring of the interdisciplinary design of game-based learning platforms consists of three norms: (a) transformation of the design goals into functional specifications of the design artifact, (b) coevolution of the problem and solution spaces as well as exploration and syntheses of partial solution of the subproblems, and (c) use of a common symbol (or representation) system to communicate and focus information while augmenting collective memory and processing.


Design problem space Design model Representation Meaningful play Game design space 


  1. Alexander, C. (1964). Notes on the synthesis of form. Cambridge, MA: Harvard University Press.Google Scholar
  2. Brown, D. C., & Chandrasekaran, B. (2014). Design problem solving: Knowledge structures and control strategies. San Mateo, CA: Morgan Kaufmann.Google Scholar
  3. Butler, E., Andersen, E., Smith, A. M., Gulwani, S., & Popović, Z. (2015, April). Automatic game progression design through analysis of solution features. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 2407–2416). New York: ACM.Google Scholar
  4. Chang, K. E., Wu, L. J., Weng, S. E., & Sung, Y. T. (2012). Embedding game-based problem-solving phase into problem-posing system for mathematics learning. Computers & Education, 58(2), 775–786.CrossRefGoogle Scholar
  5. Dede, C. (2009). Immersive interfaces for engagement and learning. Science, 323(5910), 66–69.CrossRefGoogle Scholar
  6. Dorst, K., & Cross, N. (2001). Creativity in the design process: Co-evolution of problem–solution. Design Studies, 22(5), 425–437.CrossRefGoogle Scholar
  7. Habgood, M. J., Ainsworth, S. E., & Benford, S. (2005). Endogenous fantasy and learning in digital games. Simulation & Gaming, 36(4), 483–498.CrossRefGoogle Scholar
  8. Ke, F. (2016). Designing and integrating purposeful learning in game play: A systematic review. Educational Technology Research and Development, 64(2), 219–244.CrossRefGoogle Scholar
  9. Malone, T. W. (1981). Toward a theory of intrinsically motivating instruction. Cognitive Science, 5(4), 333–369.CrossRefGoogle Scholar
  10. Nelson, M. J., & Mateas, M. (2007, September). Towards automated game design. In Congress of the Italian Association for Artificial Intelligence (pp. 626–637). Berlin/Heidelberg, Germany: Springer.Google Scholar
  11. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  12. Salen, K., & Zimmerman, E. (2005). Game design and meaningful play. In J. Raessens & J. Goldstein (Eds.), Handbook of computer game studies (pp. 59–79). Cambridge, MA: MIT Press.Google Scholar
  13. Simon, H. A. (1973). The structure of ill structured problems. Artificial Intelligence, 4(3–4), 181–201.CrossRefGoogle Scholar
  14. Sorenson, N., & Pasquier, P. (2010, April). Towards a generic framework for automated video game level creation. In European conference on the applications of evolutionary computation (pp. 131–140). Berlin/Heidelberg, Germany: Springer.CrossRefGoogle Scholar
  15. Star, J. R., Chen, J., & Dede, C. (2015). Applying motivation theory to the design of game-based learning environments. In Describing and studying domain-specific serious games (pp. 83–91). Cham, Switzerland: Springer.CrossRefGoogle Scholar
  16. Togelius, J., & Schmidhuber, J. (2008, December). An experiment in automatic game design. In Computational intelligence and games, 2008. CIG'08. IEEE symposium (pp. 111–118). IEEE Xplore.
  17. Wouters, P., & Van Oostendorp, H. (2013). A meta-analytic review of the role of instructional support in game-based learning. Computers & Education, 60(1), 412–425.CrossRefGoogle Scholar
  18. Yin, R. (2003). Case study research (3rd ed.). Thousand Oaks, CA: Sage.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Fengfeng Ke
    • 1
  • Valerie Shute
    • 1
  • Kathleen M. Clark
    • 2
  • Gordon Erlebacher
    • 3
  1. 1.Department of Educational Psychology and Learning SystemsFlorida State UniversityTallahasseeUSA
  2. 2.School of Teacher EducationFlorida State UniversityTallahasseeUSA
  3. 3.Department of Scientific ComputingFlorida State UniversityTallahasseeUSA

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