Analyzing Collaborative Interactions with Data Mining Methods for the Benefit of Learning

  • Peter ReimannEmail author
  • Kalina Yacef
  • Judy Kay
Part of the Computer-Supported Collaborative Learning Series book series (CULS, volume 12)


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).


Dependency Graph Process Instance Data Mining Method Sequential Pattern Mining Collaboration Script 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has been supported by a Discovery Grant from the Australian Research Council.


  1. Abbott, A. (1988). Transcending general linear theory. Sociological Theory, 6, 169–186.CrossRefGoogle Scholar
  2. Abbott, A. (1990). Conceptions of time and events in social science methods: Causal and narrative approaches. Historical Methods, 23, 140–150.Google Scholar
  3. 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.CrossRefGoogle Scholar
  4. Abell, P. (1987). The syntax of social life: The theory and method of comparative narratives. Oxford: Clarendon.Google Scholar
  5. Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. Paper presented at the Proceedings of International Conference on Data Engineering (ICDE95).Google Scholar
  6. Argote, L., & Ingram, P. (2000). Knowledge transfer: A basis for competitive advantage in firms. Organizational Behavior and Human Decision Processes, 82(1), 150–169.CrossRefGoogle Scholar
  7. Aristotle (1941). The basic works of Aristotle. In: R. McKeon (Ed.), New York: Random House.Google Scholar
  8. Arrow, H., McGrath, J. E., & Behrdal, J. (2000). Small groups as complex systems: Formation, co-ordination, development and adaptation. Thousand Oaks: Sage.Google Scholar
  9. Bales, R. F., & Strodtbeck, F. L. (1951). Phases in group problem solving. Journal of Abnormal and Social Psychology, 46, 485–495.CrossRefGoogle Scholar
  10. Buitelaar, P., Cimiano, P., & Magnini, B. (Eds.). (2005). Ontology learning from text: Methods, evaluation and applications. Amsterdam: IOS Press.Google Scholar
  11. Cassandras, C. G. (1993). Discrete event systems. Homewood: Richard D. Irwin.Google Scholar
  12. 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.Google Scholar
  13. Emirbayer, M., & Mische, A. (1998). What is agency? The American Journal of Sociology, 103(4), 962–1023.CrossRefGoogle Scholar
  14. Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit Oy.Google Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. Gill, A. (1962). Introduction to the Theory of Finite-state Machines. New York: McGraw-Hill.Google Scholar
  18. Giudici, P., & Passerone, G. (2002). Data mining of association structures to model consumer behaviour. Computational Statistics and Data Analysis, 38(4), 533–541.CrossRefGoogle Scholar
  19. 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.Google Scholar
  20. 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.Google Scholar
  21. Han, J., & Kamber, M. (2001). Data mining: Concepts and techniques. San Francisco: Morgan Kaufman.Google Scholar
  22. 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.Google Scholar
  23. 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.Google Scholar
  24. Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data. An introduction to cluster analysis. New York: Wiley.CrossRefGoogle Scholar
  25. 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.CrossRefGoogle Scholar
  26. 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.Google Scholar
  27. 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.Google Scholar
  28. 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.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.Google Scholar
  31. Merceron, A., & Yacef, K. (2005). TADA-Ed for Educational Data Mining. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 7(1),
  32. Mohr, L. (1982). Explaining organizational behavior. San Francisco: Jossey-Bass.Google Scholar
  33. Monge, P. R. (1990). Theoretical and analytical issues in studying organizational processes. Organization Science, 1(4), 406–430.CrossRefGoogle Scholar
  34. 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.Google Scholar
  35. 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.CrossRefGoogle Scholar
  36. 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.Google Scholar
  37. Poole, M. S., & Holmes, M. E. (1995). Decision development in computer-assisted group decision making. Human Communication Research, 22(1), 90–127.CrossRefGoogle Scholar
  38. 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.Google Scholar
  39. 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.Google Scholar
  40. 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.Google Scholar
  41. Reisig, W. (1985). Petri Nets. An introduction. Berlin: Springer.Google Scholar
  42. Salas, E., Sims, D. E., & Burke, C. S. (2005). Is there a “Big Five” in teamwork? Small Group Research, 36(5), 555–599.CrossRefGoogle Scholar
  43. Sanderson, P. M., & Fisher, C. (1994). Exploratory sequential data analysis: Foundations. Human-Computer Interaction, 9(3/4), 251–317.CrossRefGoogle Scholar
  44. 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.Google Scholar
  45. Searle, J. R., Kiefer, F., & Bierwisch, M. (1980). Speech act theory and pragmatics. Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
  46. Sterman, J. D. (2000). Business dynamics. Systems thinking and modeling for a complex world. New York: McGraw-Hill.Google Scholar
  47. 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.CrossRefGoogle Scholar
  48. Tuckman, B. W. (1965). Developmental sequences in small groups. Psychological Bulletin, 65, 384–399.CrossRefGoogle Scholar
  49. Tuckman, B. W., & Jensen, M. A. C. (1977). Stages of small-group development revisited. Group and Organizational Studies, 2, 419–427.CrossRefGoogle Scholar
  50. 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.Google Scholar
  51. 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.Google Scholar
  52. Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71–95.CrossRefGoogle Scholar
  53. Wheelan, S. A. (1994). Group processes: A developmental perspective. Sydney: Allyn & Bacon.Google Scholar
  54. 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.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.The Faculty of Education and Social WorkUniversity of SydneySydneyAustralia

Personalised recommendations