Teaching and Learning for Epistemic Fluency

  • Lina Markauskaite
  • Peter Goodyear
Part of the Professional and Practice-based Learning book series (PPBL, volume 14)


In this chapter, we turn towards the practicalities of professional education. We use an examination of four broad approaches to education to assess what each can offer to those professional educators who are looking to teach for epistemic fluency. These educational approaches come from a range of sources – not just from professional education. All these approaches focus on fine-tuning learners’ intelligent sensitivity to the critical features of the external environment. However, each of them aims to help learners make distinct connections between different kinds of knowledge and coordinate distinct ways of knowing and acting within the world. Thus, we argue that each has a part to play in completing the jigsaw of education for epistemic fluency. In shorthand terms, the approaches focus on (a) knowledge integration and cognitive flexibility, (b) playing epistemic games, (c) designerly work on knowledge building and (d) learning to design inquiry.


Epistemic fluency Knowledge integration Epistemic games Knowledge building Designing inquiry 


  1. Ambrose, G., & Harris, P. (2010). Basics design 08: Design thinking. Lausanne, Switzerland: AVA Publishing SA.Google Scholar
  2. Bereiter, C. (2002). Education and mind in the knowledge age. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  3. Blackmore, C., & Ison, R. (2012). Designing and developing learning systems for managing systemic change in a climate change world. In A. E. J. Wals & P. B. Corcoran (Eds.), Learning for sustainability in times of accelerating change (pp. 347–364). Wageningen, The Netherlands: Wageningen Academic.CrossRefGoogle Scholar
  4. Broudy, H. S. (1977). Types of knowledge and purposes of education. In R. C. Anderson, R. J. Spiro, & W. E. Montague (Eds.), Schooling and the acquisition of knowledge (pp. 1–17). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  5. Brown, T. (2008, June). Design thinking. Harvard Business Review, pp. 85–92.Google Scholar
  6. Brown, T. (2009). Change by design: How design thinking transforms organizations and inspires innovation. New York, NY: Harper Business.Google Scholar
  7. Brown, T., & Wyatt, J. (2010). Design thinking for social innovation. Stanford Social Innovation Review, 28(Winter 2010), 30–35.Google Scholar
  8. Checkland, P. (1994). Systems theory and management thinking. The American Behavioral Scientist, 38(1), 75–91.CrossRefGoogle Scholar
  9. Checkland, P., & Poulter, J. (2006). Learning for action: A short definitive account of soft systems methodology and its use for practitioners, teachers, and students. Hoboken, NJ: John Wiley & Sons.Google Scholar
  10. Checkland, P., & Scholes, J. (1999). Soft systems methodology in action (New ed.). New York, NY: John Wiley & Sons.Google Scholar
  11. Checkland, P., & Winter, M. (2006). Process and content: Two ways of using SSM. The Journal of the Operational Research Society, 57(12), 1435–1441.CrossRefGoogle Scholar
  12. Chesler, N. C., Arastoopour, G., D’Angelo, C. M., Bagley, E. A., & Shaffer, D. W. (2013). Design of a professional practice simulator for educating and motivating first-year engineering students. Advances in Engineering Education, 3(3), 1–29.Google Scholar
  13. Chi, M. T. H., Glaser, R., & Farr, M. J. (Eds.). (1988). The nature of expertise. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  14. Collins, A. (2011). Representational competence: A commentary on the Greeno analysis of classroom practice. In T. Koschmann (Ed.), Theories of learning and studies of instructional practice (Vol. 1, pp. 105–111). New York, NY: Springer.CrossRefGoogle Scholar
  15. Collins, A., & Ferguson, W. (1993). Epistemic forms and epistemic games: Structures and strategies to guide inquiry. Educational Psychologist, 28(1), 25–42.CrossRefGoogle Scholar
  16. Coulson, R., Feltovich, P., & Spiro, R. (1997). Cognitive flexibility in medicine: An application to the recognition and understanding of hypertension. Advances in Health Sciences Education, 2(2), 141–161. doi: 10.1023/A:1009780229455.CrossRefGoogle Scholar
  17. Cross, N. (2011). Design thinking: Understanding how designers think and work. Oxford, UK: Berg.Google Scholar
  18. Crowley, K., & Jacobs, M. (2002). Islands of expertise and the development of family scientific literacy. In G. Leinhardt, K. Crowley, & K. Knutson (Eds.), Learning conversations in museums. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  19. Dillenbourg, P., & Tchounikine, P. (2007). Flexibility in macro-scripts for computer-supported collaborative learning. Journal of Computer Assisted Learning, 23(1), 1–13. doi: 10.1111/j.1365-2729.2007.00191.x.CrossRefGoogle Scholar
  20. Donald, J. G. (2002). Learning to think: Disciplinary perspectives. San Francisco, CA: Jossey-Bass.Google Scholar
  21. Engeström, Y. (2001). Expansive learning at work: Toward an activity theoretical reconceptualization. Journal of Education and Work, 14(1), 133–156. doi: 10.1080/13639080020028747.CrossRefGoogle Scholar
  22. Engeström, Y. (2008). From teams to knots: Activity-theoretical studies of collaboration and learning at work. Cambridge, NY: Cambridge University Press.CrossRefGoogle Scholar
  23. Engeström, Y., Nummijoki, J., & Sannino, A. (2012). Embodied germ cell at work: Building an expansive concept of physical mobility in home care. Mind, Culture, and Activity, 19(3), 287–309. doi: 10.1080/10749039.2012.688177.CrossRefGoogle Scholar
  24. Engeström, Y., & Sannino, A. (2010). Studies of expansive learning: Foundations, findings and future challenges. Educational Research Review, 5(1), 1–24. doi: 10.1016/j.edurev.2009.12.002.CrossRefGoogle Scholar
  25. Engeström, Y., & Sannino, A. (2012). Concept formation in the wild. Mind, Culture, and Activity, 19(3), 201–206. doi: 10.1080/10749039.2012.690813.CrossRefGoogle Scholar
  26. Farrell, R., & Hooker, C. (2013). Design, science and wicked problems. Design Studies, 34(6), 681–705.
  27. Fischer, F., Kollar, I., Mandl, H., & Haake, J. M. (Eds.). (2007). Scripting computer-supported collaborative learning: Cognitive, computational and educational perspectives. New York, NY: Springer.Google Scholar
  28. Galle, P., & Kroes, P. (2014). Science and design: Identical twins? Design Studies, 35(3), 201–231.
  29. Glanville, R. (2002). A (Cybernetic) musing: Some examples of cybernetically informed educational practice. Cybernetics & Human Knowing, 9(3–4), 117–126.Google Scholar
  30. Goldstone, R. L., & Wilensky, U. (2008). Promoting transfer by grounding complex systems principles. Journal of the Learning Sciences, 17(4), 465–516. doi: 10.1080/10508400802394898.CrossRefGoogle Scholar
  31. Gray, D., Brown, S., & Macanufo, G. (2010). Gamestorming: A playbook for innovators, rulebreakers, and changemakers. Sebastopol, CA: O’Reilly.Google Scholar
  32. Greeno, J. (1980). Trends in the theory of knowledge for problem solving. In D. T. Tuma & F. Reif (Eds.), Problem solving and education: Issues in teaching and research (pp. 9–23). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  33. Ison, R. (2008). Systems thinking and practice for action research. In P. Reason & H. Bradbury (Eds.), The Sage handbook of action research: Participative inquiry and practice (pp. 139–158). Los Angeles, CA: Sage.CrossRefGoogle Scholar
  34. Ison, R., Blackmore, C., & Armson, R. (2007). Learning participation as systems practice. The Journal of Agricultural Education and Extension, 13(3), 209–225. doi: 10.1080/13892240701427599.CrossRefGoogle Scholar
  35. Ison, R., Blackmore, C., Collins, K., & Furniss, P. (2007). Systemic environmental decision making: Designing learning systems. Kybernetes, 36(9/10), 1340–1361.CrossRefGoogle Scholar
  36. Jacobson, M. J., & Spiro, R. J. (1995). Hypertext learning environments, cognitive flexibility, and the transfer of complex knowledge: An empirical investigation. Journal of Educational Computing Research, 12(4), 301–333.CrossRefGoogle Scholar
  37. Jonassen, D. H. (1992). Cognitive flexibility theory and its implications for designing CBI. In S. Dijkstra, H. M. Krammer, & J. G. Merriënboer (Eds.), Instructional models in computer-based learning environments (pp. 385–403). Berlin, Germany: Springer.CrossRefGoogle Scholar
  38. Jonassen, D. H. (1996). Scaffolding diagnostic reasoning in case-based-learning environments. Journal of Computing in Higher Education, 8(1), 48–68. doi: 10.1007/BF02942395.CrossRefGoogle Scholar
  39. Jonassen, D. H. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. New York, NY: Routledge.Google Scholar
  40. Jonassen, D. H., & Kim, B. (2010). Arguing to learn and learning to argue: Design justifications and guidelines. Educational Technology Research and Development, 58(4), 439–457. doi: 10.1007/s11423-009-9143-8.CrossRefGoogle Scholar
  41. Knight, P., & Yorke, M. (2004). Learning, curriculum and employability in higher education. London: RoutledgeFalmer.Google Scholar
  42. Kolodner, J. L. (2006). Case-based reasoning. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 225–242). Cambridge, MA: Cambridge University Press.Google Scholar
  43. Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., … Ryan, M. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting learning by design(tm) into practice. Journal of the Learning Sciences, 12(4), 495–547. doi: 10.1207/S15327809JLS1204_2.
  44. Krippendorff, K. (2007). Design research, an oxymoron? In R. Michel (Ed.), Design research now: Essays and selected projects (pp. 67–80). Zürich, Switzerland: Birkhäuser Verlag.CrossRefGoogle Scholar
  45. Li, M. (2002). Fostering design culture through cultivating the user-designers’ design thinking and systems thinking. Systemic Practice and Action Research, 15(5), 385–410. doi: 10.1023/A:1019933410857.CrossRefGoogle Scholar
  46. Linn, M. C. (1995). Designing computer learning environments for engineering and computer science: The scaffolded knowledge integration framework. Journal of Science Education and Technology, 4(2), 103–126. doi: 10.1007/BF02214052.CrossRefGoogle Scholar
  47. Linn, M. C. (2006). The knowledge integration perspective on learning and instruction. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 243–264). Cambridge, MA: Cambridge University Press.Google Scholar
  48. Linn, M., & Eylon, B.-S. (2011). Science learning and instruction: Taking advantage of technology to promote knowledge integration. New York, NY: Routledge.Google Scholar
  49. Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition: The realization of the living. Dordrecht, The Netherlands: D. Reidel.CrossRefGoogle Scholar
  50. Morris, R., Hadwin, A. F., Gress, C. L. Z., Miller, M., Fior, M., Church, H., & Winne, P. H. (2010). Designing roles, scripts, and prompts to support CSCL in gStudy. Computers in Human Behavior, 26(5), 815–824. doi: 10.1016/j.chb.2008.12.001.
  51. Morrison, D., & Collins, A. (1996). Epistemic fluency and constructivist learning environments. In B. Wilson (Ed.), Constructivist learning environments: Case studies in instructional design (pp. 107–119). Englewood Cliffs, NJ: Educational Technology Publications.Google Scholar
  52. Muukkonen, H., & Lakkala, M. (2009). Exploring metaskills of knowledge-creating inquiry in higher education. International Journal of Computer-Supported Collaborative Learning, 4(2), 187–211. doi: 10.1007/s11412-009-9063-y.CrossRefGoogle Scholar
  53. Muukkonen, H., Lakkala, M., & Hakkarainen, K. (2005). Technology-mediation and tutoring: How do they shape progressive inquiry discourse? Journal of the Learning Sciences, 14(4), 527–565. doi: 10.1207/s15327809jls1404_3.CrossRefGoogle Scholar
  54. Muukkonen, H., Lakkala, M., & Paavola, S. (2011). Promoting knowledge creation and object oriented inquiry in university courses. In S. Ludvigsen, A. Lund, I. Rasmussen, & R. Säljö (Eds.), Learning across sites: New tools, infrastructures and practices (pp. 172–189). Oxon, OX: Routledge.Google Scholar
  55. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  56. Noroozi, O., Weinberger, A., Biemans, H. J. A., Mulder, M., & Chizari, M. (2012). Argumentation-based computer supported collaborative learning (ABCSCL): A synthesis of 15 years of research. Educational Research Review, 7(2), 79–106.
  57. Noroozi, O., Weinberger, A., Biemans, H. J. A., Mulder, M., & Chizari, M. (2013). Facilitating argumentative knowledge construction through a transactive discussion script in CSCL. Computers & Education, 61, 59–76.
  58. Okada, A., Buckingham Shum, S., & Sherborne, T. (2008). Knowledge cartography: Software tools and mapping techniques. London, UK: Springer.CrossRefGoogle Scholar
  59. Paavola, S., & Hakkarainen, K. (2005). The knowledge creation metaphor – An emergent epistemological approach to learning. Science & Education, 14(6), 535–557.CrossRefGoogle Scholar
  60. Paavola, S., Lipponen, L., & Hakkarainen, K. (2004). Models of innovative knowledge communities and three metaphors of learning. Review of Educational Research, 74(4), 557–576. doi: 10.3102/00346543074004557.CrossRefGoogle Scholar
  61. Paavola, S., Lakkala, M., Muukkonen, H., Kosonen, K., & Karlgren, K. (2011). The roles and uses of design principles for developing the trialogical approach on learning. Research in Learning Technology, 19(3), 233–246. doi: 10.1080/21567069.2011.624171.CrossRefGoogle Scholar
  62. Perkins, D. N. (1986). Knowledge as design. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  63. Perkins, D. N. (2009). Making learning whole. San Francisco, CA: Jossey-Bass.Google Scholar
  64. Razzouk, R., & Shute, V. (2012). What is design thinking and why is it important? Review of Educational Research, 82(3), 330–348. doi: 10.3102/0034654312457429.CrossRefGoogle Scholar
  65. Renkl, A., Mandl, H., & Gruber, H. (1996). Inert knowledge: Analyses and remedies. Educational Psychologist, 31(2), 115–121.CrossRefGoogle Scholar
  66. Ritchhart, R., Church, M., & Morrison, K. (2011). Making thinking visible: How to promote engagement, understanding, and independence for all learners. San Francisco, CA: Jossey-Bass.Google Scholar
  67. Rittel, H., & Webber, M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169.CrossRefGoogle Scholar
  68. Rummel, N., & Spanda, H. (2007). Can people learn computer mediated collaboration by following script. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported collaborative learning: Cognitive, computational and educational perspectives (pp. 39–56). New York, NY: Springer.CrossRefGoogle Scholar
  69. Runde, A., Bromme, R., & Jucks, R. (2007). Scripting net-based medical consultation: The impact of external representations on giving advice and explanations. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported collaborative learning: Cognitive, computational and educational perspectives (pp. 57–72). New York, NY: Springer.CrossRefGoogle Scholar
  70. Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy and technology. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 97–115). Cambridge, MA: Cambridge University Press.Google Scholar
  71. Schön, D. A. (1983). The reflective practitioner: How professionals think in action. New York, NY: Basic Books.Google Scholar
  72. Schön, D. A. (1987). Educating the reflective practitioner. London, UK: Jossey-Bass.Google Scholar
  73. Senge, P. M. (2000). Schools that learn: A fifth discipline fieldbook for educators, parents and everyone who cares about education. London, UK: Doubleday.Google Scholar
  74. Senge, P. M. (2006). The fifth discipline: The art and practice of the learning organization (Rev. and updated ed.). Milsons Point, NSW: Random House Business Books.Google Scholar
  75. Senge, P. M., Smith, B., Kruschwitz, N., Laur, J., & Schley, S. (2010). The necessary revolution: How individuals and organizations are working together to create a sustainable world. London., UK: Nicholas Brealey.Google Scholar
  76. Shaffer, D. W. (2004). Pedagogical praxis: The professions as models for postindustrial education. Teachers College Record, 106(7), 1401–1421.CrossRefGoogle Scholar
  77. Shaffer, D. W. (2006). Epistemic frames for epistemic games. Computers & Education, 46(3), 223–234.
  78. Shaffer, D. W. (2009). Wag the kennel: Games, frames, and the problem of assessment. In R. Fertig (Ed.), Handbook of research on effective electronic gaming in education (pp. 577–592). Hershey, PA: IGI Global.CrossRefGoogle Scholar
  79. Simon, H. A. (1966/1996). The sciences of the artificial (1 & 3 ed.). Cambridge, MA: MIT Press.Google Scholar
  80. Spada, H. (2010). Of scripts, roles, positions, and models. Computers in Human Behavior, 26(4), 547–550.
  81. Spiro, R. J., & Jehng, J. (1990). Cognitive flexibility and hypertext: Theory and technology for the non-linear and multidimensional traversal of complex subject matter. In D. Nix & R. Spiro (Eds.), Cognition, education, and multimedia (pp. 163–205). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  82. Spiro, R. J., Coulson, R. L., Feltovich, P. J., & Anderson, D. K. (1988/2013). Cognitive flexibility theory: Advanced knowledge acquisition in ill-structured domains. In D. E. Alvermann, N. J. Unrau, & R. B. Ruddell (Eds.), Theoretical models and processes of reading (6th ed., pp. 544–557). Newark, DE: International Reading Association.Google Scholar
  83. Spiro, R. J., Collins, B. P., & Ramchandran, A. R. (2007). Modes of openness and flexibility in cognitive flexibility hypertext learning environments. In B. H. Khan (Ed.), Flexible learning in an information society (pp. 18–25). Hershey, PA: Information Science.CrossRefGoogle Scholar
  84. Stahl, E. (2007). Scripting group cognition. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported collaborative learning: Cognitive, computational and educational perspectives (pp. 327–336). New York, NY: Springer.CrossRefGoogle Scholar
  85. Strijbos, J.-W., & Weinberger, A. (2010). Emerging and scripted roles in computer-supported collaborative learning. Computers in Human Behavior, 26(4), 491–494.
  86. Tchounikine, P. (2008). Operationalizing macro-scripts in CSCL technological settings. International Journal of Computer-Supported Collaborative Learning, 3(2), 193–233. doi: 10.1007/s11412-008-9039-3.CrossRefGoogle Scholar
  87. Vickers, G. Sir. (1965). Art of judgement. London, UK: Chapman and Hall.Google Scholar
  88. von Foerster, H. (2003). Ethics and second-order cybernetics. In Understanding understanding: Essays on cybernetics and cognition (pp. 287–304). New York, NY: Springer.CrossRefGoogle Scholar
  89. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.Google Scholar
  90. Vygotsky, L. S. (1986). Thought and language (2nd ed.). Cambridge, MA: MIT Press.Google Scholar
  91. Waloszek, G. (2012, September 1). Introduction to design thinking. SAP Design Guild. Retrieved October 1, 2014 from
  92. Wals, A. E. J., & Corcoran, P. B. (Eds.). (2012). Learning for sustainability in times of accelerating change. Wageningen, The Netherlands: Wageningen Academic.Google Scholar
  93. Weinberger, A., Ertl, B., Fischer, F., & Mandl, H. (2005). Epistemic and social scripts in computer-supported collaborative learning. Instructional Science, 33(1), 1–30. doi: 10.1007/s11251-004-2322-4.CrossRefGoogle Scholar
  94. Weinberger, A., Stegmann, K., & Fischer, F. (2010). Learning to argue online: Scripted groups surpass individuals (unscripted groups do not). Computers in Human Behavior, 26, 506–515.CrossRefGoogle Scholar
  95. Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  96. 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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Lina Markauskaite
    • 1
  • Peter Goodyear
    • 1
  1. 1.Centre for Research on Learning and Innovation (CRLI), Faculty of Education & Social WorkThe University of SydneySydneyAustralia

Personalised recommendations