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How digital concept maps about the collaborators’ knowledge and information influence computer-supported collaborative problem solving

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Abstract

For collaboration in learning situations, it is important to know what the collaborators know. However, developing such knowledge is difficult, especially for newly formed groups participating in a computer-supported collaboration. The solution for this problem described in this paper is to provide to group members access to the knowledge structures and the information resources of their collaboration partners in the form of digital concept maps. In an empirical study, 20 triads having access to such maps and 20 triads collaborating without such maps are compared regarding their group performance in problem-solving tasks. Results showed that the triads being provided with such concept maps acquired more knowledge about the others’ knowledge structures and information, focused while collaborating mainly on problem-relevant information, and therefore, solved the problems faster and more often correctly, compared to triads with no access to their collaborators’ maps.

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Notes

  1. Partial eta-squared value of an experimental factor is defined as “the proportion of total variance attributable to the factor, partialling out (excluding) other factors from the total nonerror variation” (Pierce et al. 2004, p. 918). Due to the fact that classical eta-squared values for an effect are dependent upon the number and the magnitude of other effects, partial eta-squared values are preferred in this paper (Cohen 1973).

References

  • Bortz, J. (1999). Statistik für Sozialwissenschaftler. Berlin: Springer.

    Google Scholar 

  • Clark, H., & Brennan, S. (1991). Grounding in communication. In L. B. Resnick, R. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). Washington: American Psychological Association.

    Chapter  Google Scholar 

  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.

    Article  Google Scholar 

  • Cohen, J. (1973). Eta-squared and partial eta-squared in fixed factor ANOVA designs. Educational and Psychological Measurement, 33, 107–112.

    Article  Google Scholar 

  • Dehler, J., Bodemer, D., & Buder, J. (2007). Fostering audience design of computer-mediated knowledge communication by knowledge mirroring. In C. Chinn, G. Erkens, & S. Puntambekar (Eds.), Proceedings of the 7th Computer-Supported Collaborative Learning Conference (pp. 168–170). New Brunswick: International Society of the Learning Sciences, Inc.

    Google Scholar 

  • Engelmann, T., Tergan, S.-O., & Hesse, F. W. (2010). Evoking knowledge and information awareness for enhancing computer-supported collaborative problem solving. The Journal of Experimental Education, 78, 1–20.

    Google Scholar 

  • Fjermestad, J. (2004). An analysis of communication mode in group support systems research. Decision Support Systems, 37(2), 239–263.

    Google Scholar 

  • Fussell, S. R., & Krauss, R. M. (1989a). The effects of intended audience on message production and comprehension: Reference in a common ground framework. Journal of Experimental Social Psychology, 25, 203–219.

    Article  Google Scholar 

  • Fussell, S. R., & Krauss, R. M. (1989b). Understanding friends and strangers: The effects of audience design on message comprehension. European Journal of Social Psychology, 19, 509–526.

    Article  Google Scholar 

  • Fussell, S. R., & Krauss, R. M. (1991). Accuracy and bias in estimates of others’ knowledge. European Journal of Social Psychology, 21, 445–454.

    Article  Google Scholar 

  • Gross, T., Stary, C., & Totter, A. (2005). User-centered awareness in computer-supported cooperative work-systems: Structured embedding of findings from social sciences. International Journal of Human-Computer Interaction, 18, 323–360.

    Article  Google Scholar 

  • Gutwin, C., & Greenberg, S. (2002). A descriptive framework of workspace awareness for real-time groupware. Computer Supported Cooperative Work, 11, 411–446.

    Article  Google Scholar 

  • Janssen, J., Erkens, G., Kanselaar, G., & Jaspers, J. (2007). Visualization of participation: Does it contribute to successful computer-supported collaborative learning? Computers & Education, 49, 1037–1065.

    Article  Google Scholar 

  • Keller, T., & Tergan, S.-O. (2005). Visualizing knowledge and information: An introduction. In S.-O. Tergan & T. Keller (Eds.), Knowledge and information visualization—Searching for synergies (pp. 1–23). Berlin: Springer.

    Google Scholar 

  • Keysar, B., Ginzel, L. E., & Bazerman, M. H. (1995). States of affairs and states of mind: The effects of knowledge of beliefs. Organizational Behavior and Human Decision Processes, 64, 283–293.

    Article  Google Scholar 

  • Kiesler, S., Siegel, J., & McGuire, T. W. (1984). Social psychological aspects of computer-mediated communication. American Psychologist, 39, 1123–1134.

    Article  Google Scholar 

  • Krauss, R. M., & Fussell, S. R. (1991). Perspective-taking in communication: Representations of others’ knowledge in reference. Social Cognition, 9, 2–24.

    Google Scholar 

  • Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments. A review of the research. Computers in Human Behavior, 19, 335–353.

    Article  Google Scholar 

  • Liang, D. W., Moreland, R., & Argote, L. (1995). Group versus individual training and group performance: The mediating role of transactive memory. Personality and Social Psychology Bulletin, 21(4), 384–393.

    Article  Google Scholar 

  • Malone, T. W., & Crowston, K. (1994). The interdisciplinary study of coordination. ACM Computing Surveys, 26(1), 87–119.

    Article  Google Scholar 

  • Nickerson, R. S. (1999). How we know—and sometimes misjudge—what others know: Imputing one’s own knowledge to others. Psychological Bulletin, 125(6), 737–759.

    Article  Google Scholar 

  • Novak, J. D., & Gowin, D. B. (1984). Learning how to learn. New York: Cambridge University Press.

    Google Scholar 

  • Pierce, C. A., Block, R. A., & Aguinis, H. (2004). Cautionary note on reporting eta-squared values from multifactor ANOVA designs. Educational and Psychological Measurement, 64(6), 916–924.

    Article  Google Scholar 

  • Siemens, G. (2005). Connectivism. A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.

    Google Scholar 

  • Tergan, S.-O. (2005). Digital concept maps for managing knowledge and information. In S.-O. Tergan & T. Keller (Eds.), Knowledge and information visualization. Searching for synergies (pp. 173–191). Berlin: Springer.

    Google Scholar 

  • Tergan, S.-O., Keller, T., & Burkhard, R. (2006). Integrating knowledge and information: Digital concept maps as a bridging technology. Information Visualization, 5(3), 167–174.

    Article  Google Scholar 

  • Wegner, D. M. (1986). Transactive memory: A contemporary analysis of the group mind. In B. Mullen & G. R. Goethals (Eds.), Theories of group behaviour (pp. 185–208). New York: Springer.

    Google Scholar 

  • Wegner, D. M. (1995). A computer network model of human transactive memory. Social Cognition, 13(3), 319–339.

    Google Scholar 

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Acknowledgments

This research project was supported by the Knowledge Media Research Center in Tuebingen (Germany). The first author is supported by the European Social Fund and by the Ministry of Science, Research and the Arts Baden-Württemberg (Germany). We especially thank Prof. Dr. John Coffey of the Florida Institute for Human and Machine Cognition (USA), as well as the Media Development Group of the Knowledge Media Research Center for their technical assistance.

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  1. Knowledge Media Research Center, Konrad-Adenauer-Str. 40, 72072, Tuebingen, Germany

    Tanja Engelmann & Friedrich W. Hesse

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  1. Tanja Engelmann
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  2. Friedrich W. Hesse
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Correspondence to Tanja Engelmann.

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Engelmann, T., Hesse, F.W. How digital concept maps about the collaborators’ knowledge and information influence computer-supported collaborative problem solving. Computer Supported Learning 5, 299–319 (2010). https://doi.org/10.1007/s11412-010-9089-1

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