Advertisement

A Conceptual Framework of Knowledge Exchange

  • Jürgen Buder
Chapter

Abstract

Knowledge exchange, defined as interpersonal interactions that change knowledge in the heads and/or knowledge in the world, is a topic of interest in many research fields. This chapter outlines a conceptual framework which captures many variables that play a role in knowledge exchange. The conceptual framework draws a distinction between input variables, process variables, and output variables. Moreover, the framework stresses the importance of taking both individual-level variables and group-level variables into account in order to describe and explain knowledge exchange. These variables can be used to describe and categorize a broad range of empirical studies from various scholarly fields. Patterns of covariation that are discovered in the network of variables have the potential to transform the conceptual framework of knowledge exchange into a theoretical framework.

Keywords

Communication Learning Attitudes Conflict Elaboration 

Notes

Acknowledgments

This chapter constitutes the summary (output variable) of many discussions (process variable) that were conducted during Lab meetings of the Tübingen IWM Knowledge Exchange Lab between 2012 and 2015. Therefore, the author would like to thank previous and current members of the Lab (in alphabetical order) for their input: Inga Bause, Carmen Biel, Moritz Borchers, Irina Brich, Brett Buttliere, Gabriele Cierniak, Tanja Engelmann, Friedrich W. Hesse, Katrin König, Richard Kolodziej, Michail Kozlov, Karsten Krauskopf, Anja Rudat, Michael Schubert, Julien Schweitzer, Christina Schwind, Irene Skuballa, Daniel Thiemann, and Daniel Wessel.

References

  1. Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (pp. 47–89). New York: Academic Press.Google Scholar
  2. Baker, M. J., Andriessen, J., Lund, K., van Amelsvoort, M., & Quignard, M. (2007). Rainbow: a framework for analysing computer-mediated pedagogical debates. International Journal of Computer-Supported Collaborative Learning, 2, 315–357.CrossRefGoogle Scholar
  3. Buder, J. (2011). Group awareness tools for learning: Current and future directions. Computers in Human Behavior, 27, 1114–1117.CrossRefGoogle Scholar
  4. Buder, J., & Bodemer, D. (2008). Supporting controversial CSCL discussions with augmented group awareness tools. International Journal of Computer-Supported Collaborative Learning, 3, 123–139.CrossRefGoogle Scholar
  5. Buder, J., Schwind, C., Rudat, A., & Bodemer, D. (2015). Selective reading of large online forum discussions: The impact of rating visualizations on navigation and learning. Computers in Human Behavior, 44, 191–201.CrossRefGoogle Scholar
  6. Clark, H., & Brennan, S. (1991). Grounding in communication. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  7. Cress, U., & Kimmerle, J. (2017). The interrelations of individual learning and collective knowledge construction: A cognitive-systemic framework. In S. Schwan & U. Cress (Eds.), The psychology of digital learning: Constructing, exchanging, and acquiring knowledge with digital media. New York: Springer.Google Scholar
  8. De Jong, T., & van Joolingen, W. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179–201.CrossRefGoogle Scholar
  9. Dehler, J., Bodemer, D., Buder, J., & Hesse, F. W. (2011). Guiding knowledge communication in CSCL via group knowledge awareness. Computers in Human Behavior, 27, 1068–1078.CrossRefGoogle Scholar
  10. Dehler-Zufferey, J., Bodemer, D., Buder, J., & Hesse, F. W. (2011). Partner knowledge awareness in knowledge communication: Learning by adapting to the partner. The Journal of Experimental Education, 79, 102–125.CrossRefGoogle Scholar
  11. Diehl, M., & Stroebe, W. (1987). Productivity loss in brainstorming groups: Toward the solution of a riddle. Journal of Personality and Social Psychology, 53, 497–509.CrossRefGoogle Scholar
  12. Doise, W., & Mugny, G. (1984). The social development of the intellect. Oxford: Pergamon.Google Scholar
  13. Efklides, A. (2008). Metacognition: Defining its facets and levels of functioning in relation to self-regulation and co-regulation. European Psychologist, 13, 277–287.CrossRefGoogle Scholar
  14. Engelmann, T., & Hesse, F. W. (2010). How digital concept maps about the collaborators’ knowledge and information influence computer-supported collaborative problem solving. International Journal of Computer-Supported Collaborative Learning, 5, 299–320.CrossRefGoogle Scholar
  15. Engelmann, T., & Hesse, F. W. (2011). Fostering sharing of unshared knowledge by having access to the collaborators’ meta-knowledge structures. Computers in Human Behavior, 27, 2078–2087.CrossRefGoogle Scholar
  16. Engelmann, T., Kolodziej, R., & Hesse, F. W.Preventing undesirable effects of mutual trust and the development of skepticism in virtual groups by applying the knowledge and information awareness approach. International Journal of Computer-Supported Collaborative Learning, 9, 211–235.Google Scholar
  17. Engelmann, T., Kozlov, M. D., Kolodziej, R., & Clariana, R. B. (2014). Fostering group norm development and orientation while creating awareness contents for improving net-based collaborative problem solving. Computers in Human Behavior, 37, 298–306.CrossRefGoogle Scholar
  18. Engelmann, T., Dehler, J., Bodemer, D., & Buder, J. (2009). Knowledge awareness in CSCL: a psychological perspective. Computers in Human Behavior, 25, 949–960.CrossRefGoogle Scholar
  19. Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7, 117–140.CrossRefGoogle Scholar
  20. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34, 906–911.CrossRefGoogle Scholar
  21. Fodor, J. (1975). The language of thought. Cambridge: Harvard University Press.Google Scholar
  22. Hart, J. T. (1965). Memory and the feeling-of-knowing experience. Journal of Educational Psychology, 56, 208–216.CrossRefGoogle Scholar
  23. Hart, W., Albarracin, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009). Feeling validated versus being correct: A meta-analysis of selective exposure to information. Psychological Bulletin, 135, 555–588.CrossRefGoogle Scholar
  24. Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: MIT Press.Google Scholar
  25. Jehn, K. (1995). A multimethod examination of the benefits and detriments of intragroup conflict. Administrative Science Quarterly, 40, 256–282.CrossRefGoogle Scholar
  26. Johnson, D. W., & Johnson, R. T. (1979). Conflict in the classroom: Controversy and learning. Review of Educational Research, 49, 51–69.CrossRefGoogle Scholar
  27. Kiesler, S., Siegel, J., & McGuire, T. W. (1984). Social psychological aspects of computer-mediated communication. American Psychologist, 39, 1123–1134.CrossRefGoogle Scholar
  28. Kolodziej, R., Hesse, F. W., & Engelmann, T. (2016). Improving negotiations with bar charts: The advantages of priority awareness. Computers in Human Behavior, 60, 351–360.CrossRefGoogle Scholar
  29. Kozlov, M. D., Engelmann, T., Buder, J., & Hesse, F. W. (2015). Is knowledge best shared or given to individuals? Expanding the content-based knowledge awareness paradigm. Computers in Human Behavior, 37, 298–306.Google Scholar
  30. Kruger, J., Epley, N., Parker, J., & Ng, Z. W. (2005). Egocentrism over e-mail: Can we communicate as well as we think? Journal of Personality and Social Psychology, 89, 925–936.CrossRefGoogle Scholar
  31. Lea, M., & Spears, R. (1991). Computer-mediated communication, de-individuation and group decision-making. International Journal of Man-Machine Studies, 34, 283–301.CrossRefGoogle Scholar
  32. Mayer, R. E. (2001). Multimedia learning. New York: Cambridge University Press.CrossRefGoogle Scholar
  33. McGrath, J. E., & Hollingshead, A. B. (1994). Groups interacting with technology. Newbury Park: Sage.Google Scholar
  34. Medina, R., & Suthers, D. D. (2013). Inscriptions becoming representations in representational practices. Journal of the Learning Sciences, 22, 33–69.CrossRefGoogle Scholar
  35. van Mierlo, T. (2014). The 1 % rule in four digital health social networks: An observational study. Journal of Medical Internet Research, 16, e33.CrossRefGoogle Scholar
  36. Mohammed, S., & Dumville, B. C. (2001). Team mental models in a team knowledge framework: Expanding theory and measurement across disciplinary boundaries. Journal of Organizational Behavior, 22, 89–106.CrossRefGoogle Scholar
  37. Monge, P. R., & Kirste, K. K. (1980). Measuring proximity in human organization. Social Psychology Quarterly, 43, 110–115.CrossRefGoogle Scholar
  38. Nelson, T. O. (1996). Consciousness and metacognition. American Psychologist, 51, 102–116.CrossRefGoogle Scholar
  39. Nelson, T. O., & Narens, L. (1994). Why investigate metacognition? In J. Metcalfe & A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 1–25). Cambridge, MA: MIT.Google Scholar
  40. Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2, 175–220.CrossRefGoogle Scholar
  41. Piaget, J., & Inhelder, B. (1969). The psychology of the child. New York: Basic Books.Google Scholar
  42. Ray, D., Neugebauer, J., Sassenberg, K., Buder, J., & Hesse, F. W. (2013). Motivated shortcomings in explanation: The role of comparative self-evaluation and awareness of explanation recipient knowledge. Journal of Experimental Psychology: General, 142, 445–457.CrossRefGoogle Scholar
  43. Reimann, P., Yacef, K., & Kay, J. (2011). Analyzing collaborative interactions with data mining methods for the benefit of learning. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing Interactions in CSCL (pp. 161–185). Berlin: Springer.CrossRefGoogle Scholar
  44. Roschelle, J. (1992). Learning by collaborating: Convergent conceptual change. The Journal of the Learning Sciences, 2, 235–276.CrossRefGoogle Scholar
  45. Rudat, A., & Buder, J. (2015). Making retweeting social: The influence of content and context information on sharing news in Twitter. Computers in Human Behavior, 46, 75–84.CrossRefGoogle Scholar
  46. Rudat, A., Buder, J., & Hesse, F. W. (2014). Audience design in Twitter: Retweeting behavior between informational value and followers' interests. Computers in Human Behavior, 35, 132–139.CrossRefGoogle Scholar
  47. Scholl, A., Landkammer, F., & Sassenberg, K. (2017). Knowledge exchange as a motivated social process. In S. Schwan & U. Cress (Eds.), The psychology of digital learning: Constructing, exchanging, and acquiring knowledge with digital media. New York: Springer.Google Scholar
  48. Schreiber, M., & Engelmann, T. (2010). Knowledge and information awareness for initiating transactive memory system processes of computer-supported collaborating ad hoc groups. Computers in Human Behavior, 26, 1701–1709.CrossRefGoogle Scholar
  49. Schubert, M., Buder, J., & Hesse, F. W. (2014). What should I say now? A metacognitive model on the regulation of information exchange in group learning. Meeting of the EARLI SIG 16 Metacognition. Istanbul, Turkey.Google Scholar
  50. Schwind, C., & Buder, J. (2012). Reducing confirmation bias and evaluation bias: When are preference-inconsistent recommendations effective—and when not? Computers in Human Behavior, 28, 2280–2290.CrossRefGoogle Scholar
  51. Schwind, C., Buder, J., Cress, U., & Hesse, F. W. (2012). Preference-inconsistent recommendations: An effective approach for reducing confirmation bias and stimulating divergent thinking? Computers & Education, 58, 787–796.CrossRefGoogle Scholar
  52. Stahl, G. (2005). Group cognition: Computer support for collaborative knowledge building. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  53. Stasser, G., & Titus, W. (2003). Hidden profiles: A brief history. Psychological Inquiry, 14, 304–313.CrossRefGoogle Scholar
  54. Suthers, D., Girardeau, L., & Hundhausen, C. (2003). Deictic roles of external representations in face-to-face and online collaboration. In B. Wasson, S. Ludvigsen, & U. Hoppe (Eds.), Designing for change (pp. 173–182). Berlin: Springer.Google Scholar
  55. Thiemann, D., & Engelmann, T. (2015). Computer-supported preference awareness in negotiation teams for fostering accurate joint priorities. In D. Cosley, A. Forte, C. Luigina, & D. McDonald (Eds.), Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing (CSCW'15 Companion) (pp. 227–230). New York, NY: ACM.Google Scholar
  56. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge: University Press.Google Scholar
  57. Webb, N. M. (1991). Task-related verbal interaction and mathematics learning in small groups. Journal for Research in Mathematics Education, 22, 366–389.CrossRefGoogle Scholar
  58. Wegner, D. M. (1987). Transactive memory: A contemporary analysis of the group mind. In B. Mullen & G. R. Goethals (Eds.), Theories of group behavior (pp. 185–208). New York: Springer.CrossRefGoogle Scholar
  59. Zahn, C. (2017). Digital design and learning: Cognitive-constructivist perspectives on individual and group knowledge processes in design problem solving. In S. Schwan & U. Cress (Eds.), The psychology of digital learning: Constructing, exchanging, and acquiring knowledge with digital media. New York: Springer.Google Scholar
  60. Zigurs, I., & Buckland, B. K. (1998). A theory of task/technology fit and group support systems effectiveness. MIS Quarterly, 1998, 313–334.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Leibniz-Institut für WissensmedienTübingenGermany

Personalised recommendations