The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.06 - June (2009 vol.21)
pp: 759-772
Dilhan Perera , University of Sydney, Sydney
Judy Kay , University of Sydney, Sydney
Irena Koprinska , University of Sydney, Sydney
Kalina Yacef , University of Sydney, Sydney
Osmar R. Zaïane , University of Alberta, Edmonton, Alberta
ABSTRACT
Group work is widespread in education. The growing use of online tools supporting group work generates huge amounts of data. We aim to exploit this data to support mirroring: presenting useful high-level views of information about the group, together with desired patterns characterizing the behavior of strong groups. The goal is to enable the groups and their facilitators to see relevant aspects of the group's operation and provide feedback if these are more likely to be associated with positive or negative outcomes and indicate where the problems are. We explore how useful mirror information can be extracted via a theory-driven approach and a range of clustering and sequential pattern mining. The context is a senior software development project where students use the collaboration tool TRAC. We extract patterns distinguishing the better from the weaker groups and get insights in the success factors. The results point to the importance of leadership and group interaction, and give promising indications if they are occurring. Patterns indicating good individual practices were also identified. We found that some key measures can be mined from early data. The results are promising for advising groups at the start and early identification of effective and poor practices, in time for remediation.
INDEX TERMS
Data mining, clustering, sequential pattern mining, learning group work skills, collaborative learning, computer-assisted instruction.
CITATION
Dilhan Perera, Judy Kay, Irena Koprinska, Kalina Yacef, Osmar R. Zaïane, "Clustering and Sequential Pattern Mining of Online Collaborative Learning Data", IEEE Transactions on Knowledge & Data Engineering, vol.21, no. 6, pp. 759-772, June 2009, doi:10.1109/TKDE.2008.138
REFERENCES
[1] E. Salas, D.E. Sims, and C.S. Burke, “Is There a ‘Big Five’ in Teamwork?” Small Group Research, vol. 36, pp. 555-599, 2005.
[2] Educational Data Mining, http:/www.educationaldatamining.org, 2008.
[3] Educational Data Mining Events, http://www.educationaldata mining.orgevents.html , 2008.
[4] A. Merceron and K. Yacef, “Clustering Students to Help Evaluate Learning,” Technology Enhanced Learning, J.-P. Courtiat, C. Davarakis, and T. Villemur, eds., vol. 171, pp. 31-42, Springer, 2005.
[5] C. Romero, S. Ventura, C.d. Castro, W. Hall, and N.H. Ng, “Using Genetic Algorithms for Data Mining in Web-Based Educational Hypermedia Systems,” Proc. Workshop Adaptive Systems for Web-Based Education, 2002.
[6] R. Mazza and V. Dimitrova, “CourseVis: Externalising Student Information to Facilitate Instructors in Distance Learning,” Proc. 11th Int'l Conf. Artificial Intelligence in Education (AIED), 2003.
[7] W. Wang, J.-F. Weng, J.-M. Su, and S.-S. Tseng, “Learning Portfolio Analysis and Mining in SCORM Complaint Environment,” Proc. 34th ASEE/IEEE Frontiers in Education Conf. (FIE), 2004.
[8] R. Cheng and J. Vassileva, “Design and Evaluation of an Adaptive Incentive Mechanism for Sustained Educational Online Communities,” User Modeling and User-Adapted Interaction, vol. 16, pp. 321-348, 2006.
[9] L. Talavera and E. Gaudioso, “Mining Student Data to Characterize Similar Behavior Groups in Unstructured Collaboration Spaces,” Proc. 16th European Conf. Artificial Intelligence (ECAI), 2004.
[10] A. Soller and A. Lesgold, “A Computational Approach to Analyzing Online Knowledge Sharing Interaction,” Proc. 11th Int'l Conf. Artificial Intelligence in Education (AIED), 2003.
[11] A. Soller, “Computational Modeling and Analysis of Knowledge Sharing in Collaborative Distance Learning,” User Modeling and User-Adapted Interaction, vol. 14, pp. 351-381, 2004.
[12] B. Barros and M.F. Verdejo, “Analysing Student Interaction Processes in Order to Improve Collaboration. The DEGREE Approach,” Int'l J. Artificial Intelligence in Education, vol. 11, pp. 221-241, 2000.
[13] P. Jermann, A. Soller, and M. Muehlenbrock, “From Mirroring to Guiding: A Review of State of the Art Technology for Supporting Collaborative Learning,” Proc. First European Conf. Computer-Supported Collaborative Learning (Euro-CSCL), 2001.
[14] J. Kay, P. Reimann, and K. Yacef, “Mirroring of Group Activity to Support Learning as Participation,” Proc. 13th Int'l Conf. Artificial Intelligence in Education (AIED), 2007.
[15] T. Erickson, C. Halverson, W.A. Kellogg, M. Laff, and T. Wolf, “Social Translucence: Designing Social Infrastructures that MakeCollective Activity Visible,” Comm. ACM, vol. 45, pp. 40-44, 2002.
[16] T. Erickson and W.A. Kellogg, “Social Translucence: An Approach to Designing Systems That Support Social Processes,” ACM Trans. Computer-Human Interaction, vol. 7, pp. 59-83, 2000.
[17] XP—Extreme Programming, www.extremeprogramming.org, 2007.
[18] TRAC, http:/trac.edgewall.org/, 2007.
[19] J. Kay, P. Reimann, and K. Yacef, “Visualisations for Team Learning: Small Teams Working on Long-Term Projects,” Proc.Int'l Conf. Computer-Supported Collaborative Learning (CSCL), 2007.
[20] P.-N. Tan, M. Steinback, and V. Kumar, Introduction to Data Mining. Pearson Addison Wesley, 2006.
[21] I. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.
[22] WEKA, www.cs.waikato.ac.nz/mlweka, 2007.
[23] Cluster Software, http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/ software/clustersoftware.htm, 2006.
[24] TreeView Software, http:/jtreeview.sourceforge.net/, 2006.
[25] R. Srikant and R. Agrawal, “Mining Sequential Patterns: Generalizations and Performance Improvements,” Proc. Fifth Int'l Conf. Extending Database Technology (EDBT), 1996.
[26] J. Kay, N. Maisonneuve, K. Yacef, and O. Zaïane, “Mining Patterns of Events in Students' Teamwork Data,” Proc. Workshop Educational Data Mining at the Eighth Int'l Conf. Intelligent Tutoring Systems, C. Heiner, R. Baker, and K. Yacef, eds., pp.45-52, 2006.
[27] D. Cummins, K. Yacef, and I. Koprinska, “A Sequence Based Recommender System for Learning Resources,” Australian J.Intelligent Information Processing Systems, vol. 9, pp. 49-56, 2006.
[28] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Datal, and M.-C. Hsu, “Freespan: Frequent Pattern-Projected Sequential Pattern Mining,” Proc. Sixth Int'l Conf. Knowledge Discovery and Data Mining (KDD), 2000.
[29] J. Pei, B. Mortazavi-Asl, H. Punto, Q. Chen, U. Dayal, and M.-C. Hsu, “PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth,” Proc. 17th Int'l Conf. Data Eng. (ICDE), 2001.
[30] F.E. Fiedler, A Theory of Leadership Effectiveness. McGraw-Hill, 1967.
[31] E.A. Fleishman, “The Description of Supervisory Behavior,” J. Applied Psychology, vol. 37, pp. 1-6, 1953.
[32] G. Dong and J. Li, “Efficient Mining of Emerging Patterns: Discovering Trends and Differences,” Knowledge Discovery and Data Mining, pp. 43-53, 1999.
[33] S. Bay and M. Pazzani, “Detecting Group Differences: Mining Contrast Sets,” Data Mining and Knowledge Discovery, vol. 5, pp. 213-246, 2001.
[34] C. Rust, “Impact of Assessment on Student Learning,” Active Learning in Higher Education, vol. 3, pp. 145-158, 2002.
[35] P. Ramsden, Learning to Teach in Higher Education. Routledge-Falmer, 2003.
6 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool