Article

Using process mining to identify models of group decision making in chat data

Authors:

Jimmy Frerejean,

Kate ThompsonAuthors Info & Claims

CSCL'09: Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1

Pages 98 - 107

Published: 08 June 2009 Publication History

Abstract

This paper introduces process modeling and mining as an approach to process analysis for CSCL. This approach is particularly relevant for collaborative learning that takes a project-based form, and is applied in this study to online chat data from teams working on a complex task. The groups differed in terms of the number of members and the amount of scaffolding aimed at group processes and task requirements. The models, produced using the HeuristicsMiner algorithm, showed that the group with fewer members that received more instruction in the task requirements had a more linear decision-making process than the group that received instruction in group processes, however neither were an example of a linear, unitary phase model. This approach has relevance both for CSCL research methods and for providing feedback to students on their decision-making processes.

References

[1]

Aalst van der, W. M. P., Rubin, V., Verbeek, H. M. W., van Dongen, B. F., Kindler, E., & G��nther, C. W. (2008). Process mining: A two-step approach to balance between underfitting and overfitting (No. BPM-08-01). Eindhoven, The Netherlands: Technical University of Eindhoveno. Document Number).

[2]

Aalst van der, W. M. P., van Dongen, B. F., G�nther, C. W., Mans, R. S., Alves de Medeiros, A. K., Rozinat, A., et al. (2007). PROM4.0: Comprehensive support for real process analysis. In J. Kleijn & A. Yakovlev (Eds.), Petri nets and other models of concurrency (Proceedings 28th International Conference on Applications and Theory of Petri Nets and Other Models of Concurrency, ICATPN 2007, Siedcle, Poland, June 25-29, 2007) (pp. 484-494). Berlin: Springer.

[3]

Abbott, A., & Hrycak, A. (1990). Measuring resemblance in sequence data: an optimal matching analysis of musicians' careers. American Journal of Sociology, 96, 144-185.

[4]

Arrow, H., McGrath, J. E., & Behrdal, J. (2000). Small Groups as Complex Systems: Formation, Co-ordination, Development and Adaptation. Thousand Oaks, California: Sage Publications.

[5]

Bales, R. F., & Strodtbeck, F. L. (1951). Phases in Group Problem Solving. Journal of Abnormal and Social Psychology, 46, 485-495.

[6]

Cassandras, C. G. (1993). Discrete event systems. Homewood, IL: Richard D. Irwin, Inc.

[7]

Dillenbourg, P., & Hong, F. (2008). The mechanics of CSCL macro scripts. International Journal of Computer-supported Collaborative Learning, 3(1), 5-24.

[8]

Engestr�m, Y. (1999). Activity theory and individual and social transformation. In Y. Engestroem, R. Miettinen & R.-L. Punanm��ki (Eds.), Perspectives on activity theory (pp. 19-38). Cambridge: Cambride University Press.

[9]

Gouran, D. S. (1999). Communication in Groups: Emergence and Evolution of a Field of Study. In L. R. Frey, D. S. Gouran & M. S. Poole (Eds.), The Handbook of Group Communication: Theory and Research (pp. 3-36). Thousand Oaks, CA: Sage Publications.

[10]

Gouran, D. S., & Hirokawa, R. Y. (1996). Functional Theory and Communication in Decision-Making and Problem-Solving Groups: An Expanded View. In M. S. Poole & R. Y. Hirokawa (Eds.), Communication and Group Decision Making (2nd ed., pp. 55-80). Thousand Oaks, CA: Sage Publications.

[11]

Graham, E. E., Papa, M. J., & McPherson, M. B. (1997). An Applied Test of the Functional Communication Perspective of Small Group Decision-Making. The Southern Communication Journal, 62(4), 269-279.

[12]

Han, J., & Kamber, M. (2001). Data mining: concepts and techniques. San Francisco: Morgan Kaufman.

Digital Library

[13]

Hirokawa, R. Y., & Salazar, A. J. (1999). Task-Group Communication and Decision-Making Performance. In L. R. Frey, D. S. Gouran & M. S. Poole (Eds.), The Handbook of Group Communication Theory and Practice (pp. 167-191). Thousand Oaks, CA: Sage Publications.

[14]

Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data. An introduction to cluster analysis. New York: Wiley.

[15]

Kay, J., Yacef, K., & Reimann, P. (2007). Visualisations for team learning: small teams working on long-term projects. 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. 351-353). New Brunswick, NJ: International Society of the Learning Sciences.

Digital Library

[16]

Kollar, I., Fischer, F., & Hesse, F. W. (2006). Collaboration scripts - a conceptual analysis. Educational Psychological Review, 18, 159-185.

[17]

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.

[18]

Olson, G. M., Herbsleb, J. D., & Rueter, H. H. (1994). Characterizing the sequential structure of interactive behaviors through statistical and grammatical techniques. Human-Computer Interaction, 9(3/4), 427-472.

Digital Library

[19]

Orlitzky, M., & Hirokawa, R. Y. (2001). To Err is Human, To Correct for it Divine: A Meta-Analysis of Research Testing the Functional Theory of Group Decision-Making Effectiveness. Small Group Research, 32(3), 313-341.

[20]

Poole, M. S. (1999). Group Communication Theory. In L. R. Frey, D. S. Gouran & M. S. Poole (Eds.), The Handbook of Group Communication: Theory and Research (pp. 37-70). Thousand Oaks, CA: Sage Publications.

[21]

Poole, M. S., & Holmes, M. E. (1995). Decision Development in Computer-Assisted Group Decision Making. Human Communication Research, 22(1), 90-127.

[22]

Poole, M. S., & Roth, J. (1989a). Decision Development in Small Groups V: Test of a Contingency Model. Human Communication Research, 15(4), 549-589.

[23]

Poole, M. S., & Roth, J. (1989b). Decision making in small groups IV: A typology of group decision paths. Human Communication Research, 15(3), 323-356.

[24]

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.

[25]

Propp, K. M., & Nelson, D. (1996). Problem-Solving Performance in Naturalistic Groups: A Test of the Ecological Validity of the Functional Perspective. Communication Studies, 47(1-2), 35-45.

[26]

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, NJ: International Society of the Learning Sciences.

[27]

Reimann, P., Thompson, K., & Weinel, M. (2007). Decision making patterns in virtual teams. Paper presented at the ICCE 2007 (5-9 Nov 2007, Hiroshima, Japan).

Digital Library

[28]

Reisig, W. (1985). Petri Nets. An introduction. Berlin: Springer.

Digital Library

[29]

Ritter, F. E., & Larkin, J. H. (1994). Developing process models as summaries of HCI action sequences. Human-Computer Interaction, 9(3/4), 345-383.

Digital Library

[30]

Ritter, F. E., Nerb, J., Lehtinen, E., & O'Shea, T. (Eds.). (2007). In order to learn: How the sequences of topics influences learning. Oxford: Oxford Press.

[31]

Sanderson, P. M., & Fisher, C. (1994). Exploratory sequential data analysis: foundations. Human-Computer Interaction, 9(3/4), 251-317.

Digital Library

[32]

Schegloff, E. A. (2007). Sequence organization in interaction. New York: Cambridge University Press.

[33]

Sch�mmer, 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, NJ: Erlbaum.

[34]

Sloane, F. C., & Kelly, A. E. (2008). Design research and the study of change. In A. E. Kelly, R. A. Lesh & J. Y. Baek (Eds.), Handbook of design research methods in education (pp. 441-448). New York: Routledge.

[35]

Stahl, G. (2006). Group cognition: computer support for building collaborative knowledge: MIT Press.

Digital Library

[36]

Sterman, J. D. (2000). Business Dynamics. Systems Thinking and Modeling for a Complex World. New York: McGraw-Hill.

Digital Library

[37]

Strijbos, J. W., Martens, R. L., Prins, F. J., & Jochems, W. M. G. (2006). Content analysis: What are they talking about? Computers & Education, 46, 29-48.

Digital Library

[38]

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.

[39]

Ullman, A., Peters, D., & Reimann, P. (2005). Developing a research supportive web-based learning System. Paper presented at the ODLAA Conference 2005, Adelaide.

[40]

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, NL: Eindhoven University of Technologyo. Document Number).

[41]

Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71-95.

Digital Library

[42]

Wever, B. d., Schellens, T., Valcke, M., & Keer, H. v. (2006). Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review. Computers & Education, 46, 6-28.

Digital Library

[43]

Winne, P. H., & Nesbit, J. C. (1995). Graph theoretic techniques for examining patterns and strategies in students' studying: An application of LogMill. Paper presented at the American Educational Research Association, April 1995.

[44]

Wittenbaum, G. M., Hollingshead, A. B., Paulus, P. B., Hirokawa, R. Y., Ancona, D. G., Peterson, R. S., et al. (2004). The Functional Perspective as a Lens for Understanding Groups. Small Group Research, 35(1), 17-43.

Cited By

Porouhan PPremchaiswadi W(2017)Process Mining and Learners' Behavior Analytics in a Collaborative and Web-Based Multi-Tabletop EnvironmentInternational Journal of Online Pedagogy and Course Design10.4018/IJOPCD.20170701037:3(29-53)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.4018/IJOPCD.2017070103
Goggins SGalyen KPetakovic ELaffey J(2016)Connecting performance to social structure and pedagogy as a pathway to scaling learning analytics in MOOCsJournal of Computer Assisted Learning10.1111/jcal.1212932:3(244-266)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1111/jcal.12129
Wang GWang HLi JAbrahams AFan W(2014)An Analytical Framework for Understanding Knowledge-Sharing Processes in Online Q&A CommunitiesACM Transactions on Management Information Systems10.1145/26294455:4(1-31)Online publication date: 12-Dec-2014
https://dl.acm.org/doi/10.1145/2629445
Show More Cited By

Index Terms

Using process mining to identify models of group decision making in chat data

Recommendations

Technololgy, group process, and group outcomes: testing the connections in computer-mediated and face-to-face groups

This article examines the effect of communication media on group processes and the consequent effect of processes on group cohesiveness, satisfaction, and productivity using mediated regression analysis. Data are based on Straus and McGrath (1994), in ...
Improving group decision making: a fuzzy GSS approach

Group decision-making methods have been developed extensively, but their adaptation for use in organizations has been problematic. According to Arrow's (1963) Impossibility theorem, one conceivable reason is that a group decision outcome could never ...
Group decision-making in computer-supported cooperative work environments

Comments

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

CSCL'09: Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1

June 2009

664 pages

ISBN:9781409285984

Sponsors

Prefecture of Dodecanese
General Secretary of Aegean and Island Policy: Ministry of Mercantile Marine, Aegean and Island Policy
Rhodian Paideia School
Municipality of Kallithea, Island of Rhodes
Hellenic Ministry of Education
Didaskaleio Konstantinos Karatheodoris, University of the Aegean
Kritiki Editions
Hellenic Telecommunications Organization (OTE S.A.)

Publisher

International Society of the Learning Sciences

Publication History

Published: 08 June 2009

Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 182 of 334 submissions, 54%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
308
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Porouhan PPremchaiswadi W(2017)Process Mining and Learners' Behavior Analytics in a Collaborative and Web-Based Multi-Tabletop EnvironmentInternational Journal of Online Pedagogy and Course Design10.4018/IJOPCD.20170701037:3(29-53)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.4018/IJOPCD.2017070103
Goggins SGalyen KPetakovic ELaffey J(2016)Connecting performance to social structure and pedagogy as a pathway to scaling learning analytics in MOOCsJournal of Computer Assisted Learning10.1111/jcal.1212932:3(244-266)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1111/jcal.12129
Wang GWang HLi JAbrahams AFan W(2014)An Analytical Framework for Understanding Knowledge-Sharing Processes in Online Q&A CommunitiesACM Transactions on Management Information Systems10.1145/26294455:4(1-31)Online publication date: 12-Dec-2014
https://dl.acm.org/doi/10.1145/2629445
Bergenthum RDesel JHarrer AMauser S(2012)Modeling and mining of learnflowsTransactions on Petri Nets and Other Models of Concurrency V10.5555/2231056.2231058(22-50)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.5555/2231056.2231058

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents