Abstract
In this paper, we attempt to relate types of change processes that are prevalent in groups to types of models that might be employed to represent these processes. Following McGrath’s analysis of the nature of change processes in groups and teams, we distinguish between development, adaptation, group activity, and learning. We argue that for the case where groups act as activity systems (i.e., attempt to achieve common goals in a co-ordinated manner involving planning and division of labour), the notion of a group process needs to take into account multiple types of causality and requires a holistic formal representation. Minimally, a process needs to be conceived on the level of patterns of sequences, but in many cases discrete event model formalisms might be more appropriate. We then survey various methods for process analysis with the goal to find formalization types that are suitable to model change processes that occur in activity systems. Two types of event-based process analysis are discussed in more depth: the first one works with the view of a process as a sequence pattern, and the second one sees a process as an even more holistic and designed structure: a discrete event model. For both cases, we provide examples for event-based computational methods that proved useful in analyzing typical CSCL log files, such as those resulting from asynchronous interactions (we focus on wikis), the those resulting from synchronous interactions (we focus on chats).
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References
Abbott, A. (1988). Transcending general linear theory. Sociological Theory, 6, 169–186.
Abbott, A. (1990). Conceptions of time and events in social science methods: Causal and narrative approaches. Historical Methods, 23, 140–150.
Abbott, A., & Hrycak, A. (1990). Measuring resemblance in sequence data: An optimal matching analysis of musicians’ careers. The American Journal of Sociology, 96, 144–185.
Abell, P. (1987). The syntax of social life: The theory and method of comparative narratives. Oxford: Clarendon.
Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. Paper presented at the Proceedings of International Conference on Data Engineering (ICDE95).
Argote, L., & Ingram, P. (2000). Knowledge transfer: A basis for competitive advantage in firms. Organizational Behavior and Human Decision Processes, 82(1), 150–169.
Aristotle (1941). The basic works of Aristotle. In: R. McKeon (Ed.), New York: Random House.
Arrow, H., McGrath, J. E., & Behrdal, J. (2000). Small groups as complex systems: Formation, co-ordination, development and adaptation. Thousand Oaks: Sage.
Bales, R. F., & Strodtbeck, F. L. (1951). Phases in group problem solving. Journal of Abnormal and Social Psychology, 46, 485–495.
Buitelaar, P., Cimiano, P., & Magnini, B. (Eds.). (2005). Ontology learning from text: Methods, evaluation and applications. Amsterdam: IOS Press.
Cassandras, C. G. (1993). Discrete event systems. Homewood: Richard D. Irwin.
Dillenbourg, P., Baker, M., Blaye, A., & O’Malley, C. (1995). The Evolution of Research on Collaborative Learning. In P. Reimann & H. Spada (Eds.), Learning in humans and machines: Towards an interdisciplinary learning science (pp. 189–211). London: Elsevier.
Emirbayer, M., & Mische, A. (1998). What is agency? The American Journal of Sociology, 103(4), 962–1023.
Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit Oy.
Engeström, Y. (1999). Activity theory and individual and social transformation. In Y. Engestroem, R. Miettinen, & R.-L. Punanmäki (Eds.), Perspectives on activity theory (pp. 19–38). Cambridge: Cambridge University Press.
Gersick, C. J. G. (1988). Time and transition in work teams: Toward a new model of group development. Academy of Management Journal, 31, 9–41.
Gill, A. (1962). Introduction to the Theory of Finite-state Machines. New York: McGraw-Hill.
Giudici, P., & Passerone, G. (2002). Data mining of association structures to model consumer behaviour. Computational Statistics and Data Analysis, 38(4), 533–541.
Gollwitzer, P. M. (1986). Action phases and mind-sets. In R. M. Sorrentino & E. T. Higgins (Eds.), Handbook of motivation and cognition (pp. 53–92). New York: Guilford.
Gouran, D. S., & Hirokawa, R. Y. (1996). Functional theory and comunication in decision-making and problem-solving groups: An expanded view. In R. Y. Hirokawa & M. S. Poole (Eds.), Communication and group decision making (pp. 55–80). Thousand Oaks: Sage.
Han, J., & Kamber, M. (2001). Data mining: Concepts and techniques. San Francisco: Morgan Kaufman.
Jermann, P., Soller, A., & Muehlenbrock, M. (2001). From mirroring to guiding: a review of the state of the art technology for supporting collaborative learning. In P. Dillenbourg, A. Eurelings, & K. Hakkarainen (Eds.), European perspectives on computer-supported learning (pp. 324–331). Maastricht: University of Maastricht.
Kapur, M., Hung, D., Jacobson, M. J., Voiklis, J., Kinzer, C. K., & Victor, C. D.-T. (2007). Emergence of learning in computer-supported, large-scale collective dynamics: A research agenda. Proceedings of the International Conference on Computer-supported Collaborative Learning (CSCL2007). New Brunswick, NJ.
Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data. An introduction to cluster analysis. New York: Wiley.
Kay, J., Maisonneuve, N., Yacef, K., & Reimann, P. (2006). The Big Five and Visualisations for Team Work Activity. In M. Ikeda, K. D. Ashley, & T.-W. Chan (Eds.), Proceedings of intelligent tutoring systems (ITS06) (pp. 197–206). Heidelberg: Springer.
Kindler, E., Rubin, V., & Schäfer, W. (2006). Process Mining and Petri Net Synthesis. Lecture Notes in Computer Science (Vol. 4103, pp. 105–116). Business Process Management Workshops, Berlin: Springer.
McGrath, J. E., & Argote, L. (2001). Group processes in organizational contexts. In M. A. Hogg & R. S. Tindale (Eds.), Blackwell handbook of social psychology (Vol. 3, pp. 603–627). Oxford: Blackwell.
McGrath, J. E., & Beehr, T. A. (1990). Time and the stress process: Some temporal issues in the conceptualization and measurement of stress. Stress Medicine, 6, 95–104.
McGrath, J. E., & Tschan, F. (2004). Temporal matters in social psychology: Examining the role of time in the lives of groups and individuals. Washington, DC: American Psychological Association.
McIntyre, R. M., & Salas, E. (1995). Measuring and managing for team performance: Emerging principles from complex environments. In R. A. Guzzo & E. Salas (Eds.), Team effectiveness and decision making in organizations (pp. 9–45). San Francisco: Jossey-Bass.
Merceron, A., & Yacef, K. (2005). TADA-Ed for Educational Data Mining. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 7(1), http://imej.wfu.edu/articles/2005/2001/2003/index.asp.
Mohr, L. (1982). Explaining organizational behavior. San Francisco: Jossey-Bass.
Monge, P. R. (1990). Theoretical and analytical issues in studying organizational processes. Organization Science, 1(4), 406–430.
Muukkonen, H., Hakkarainen, K., Konsonen, K., Jalonen, S., Heikkil, A., Lonka, K., et al. (2007). Process- and context-sensitive research on academic knowledge practices: Developing CASS-tools and methods. In C. Chinn, G. Erkens, & S. Puntambekar (Eds.), Minds, mind, and society. Proceedings of the 6th International Conference on Computer-supported Collaborative Learning (CSCL 2007) (pp. 541–543). New Brunswick: International Society of the Learning Sciences.
Perera, D., Kay, J., Koprinska, I., Yacef, K., & Zaiane, O. (2008). Clustering and sequential pattern mining of online collaborative learning data. IEEE Transactions on Knowledge and Data Engineering, 21(6), 759–772.
Poole, M. S., & Doelger, J. A. (1986). Developmental processes in group decision-making. In R. Hirokawa & M. S. Poole (Eds.), Communication and group decision-making (pp. 35–62). Berverly Hills: Sage.
Poole, M. S., & Holmes, M. E. (1995). Decision development in computer-assisted group decision making. Human Communication Research, 22(1), 90–127.
Poole, M. S., van de Ven, A., Dooley, K., & Holmes, M. E. (2000). Organizational change and innovation processes. Theories and methods for research. New Oxford: Oxford University Press.
Reimann, P. (2007). Time is precious: Why process analysis is essential for CSCL (and also can help to bridge between experimental and descriptive methods). In C. Chinn, G. Erkens, & S. Puntambekar (Eds.), Minds, minds, and society. Proceedings of the Computer-supported Collaborative Learning Conference (CSCL 2007) (pp. 598–607). New Brunswick: International Society of the Learning Sciences.
Reimann, P., Frerejean, J., & Thompson, K. (2009). Using process mining to identify models of group decision making processes in chat data. In C. O’Malley, D. Suthers, P. Reimann, & A. Dimitracopoulou (Eds.), Computer-supported collaborative learning practives: CSCL2009 conference proceedings (pp. 98–107). International Society for the Learning Sciences.
Reisig, W. (1985). Petri Nets. An introduction. Berlin: Springer.
Salas, E., Sims, D. E., & Burke, C. S. (2005). Is there a “Big Five” in teamwork? Small Group Research, 36(5), 555–599.
Sanderson, P. M., & Fisher, C. (1994). Exploratory sequential data analysis: Foundations. Human-Computer Interaction, 9(3/4), 251–317.
Schümmer, T., Strijbos, J.-W., & Berkel, T. (2005). A new direction for log file analysis in CSCL: Experiences with a spatio-temporal metric. In T. Koschmann, D. Suthers, & T. W. Chan (Eds.), Computer Supported Collaborative Learning 2005: The next 10 years! (pp. 567–576). Mahwah: Erlbaum.
Searle, J. R., Kiefer, F., & Bierwisch, M. (1980). Speech act theory and pragmatics. Dordrecht: Kluwer Academic.
Sterman, J. D. (2000). Business dynamics. Systems thinking and modeling for a complex world. New York: McGraw-Hill.
Suthers, D. D. (2006). A qualitative analysis of collaborative knowledge construction through shared representations. Research and Practice in Technology Enhanced Learning, 1(2), 115–142.
Tuckman, B. W. (1965). Developmental sequences in small groups. Psychological Bulletin, 65, 384–399.
Tuckman, B. W., & Jensen, M. A. C. (1977). Stages of small-group development revisited. Group and Organizational Studies, 2, 419–427.
Van der Aalst, W. M. P., & Günther, C. W. (2007). Finding structure in unstructured processes: the case of process mining. In T. Basten, G. Juhas, & S. Shukla (Eds.), Proceedings the 7th International Conference on Applications of Concurrency to System Design (ACSD 2007; Bratislava, Slovak Republic) (pp. 3–12). Los Alamitos: IEEE Computer Society Press.
Weijters, A. J. M. M., Aalst, W. M. P. V. D., & Medeiros, A. K. A. D. (2006). Process mining with the heuristics miner-algorithm. BETA Working Paper Series WP 166. Eindhoven: Eindhoven University of Technology.
Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71–95.
Wheelan, S. A. (1994). Group processes: A developmental perspective. Sydney: Allyn & Bacon.
Zumbach, J., & Reimann, P. (2003). Influence of feedback on distributed problem based learning. Paper presented at the CSCL 2003 conference, June 15th to 18th, Bergen, Norway.
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This research has been supported by a Discovery Grant from the Australian Research Council.
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Reimann, P., Yacef, K., Kay, J. (2011). Analyzing Collaborative Interactions with Data Mining Methods for the Benefit of Learning. In: Puntambekar, S., Erkens, G., Hmelo-Silver, C. (eds) Analyzing Interactions in CSCL. Computer-Supported Collaborative Learning Series, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-7710-6_8
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