Mirroring Tools for Collaborative Analysis and Reflection

  • Christoph Richter
  • Ekaterina Simonenko
  • Tsuyoshi Sugibuchi
  • Nicolas Spyratos
  • Frantisek Babic
  • Jozef Wagner
  • Jan Paralic
  • Michal Racek
  • Crina DamŞa
  • Vassilis Christophides
Part of the Technology Enhanced Learning book series (TEL, volume 7)


Analysing and reflecting on one’s own and other’s working practices is an essential meta-activity for any kind of project work, but also plays a prominent role in the type of object-oriented inquiry and trialogical learning the KP-Lab project is focusing on. Analysis and reflection, therefore, are not understood just as means to optimize or improve a given way of working but also as an active and productive process, geared towards the advancement of knowledge practices.


Information Visualization Query Formulation Computer Support Collaborative Learn Collaborative System Computer Support Collaborative Learn 
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.


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  1. Amar, R. & Stasko. J. (2004). A knowledge task-based framework for design and evaluation of information visualizations. Proceedings of the IEEE Symposium on information Visualization (October 10–12. INFOVIS. Washington, DC: IEEE Computer Society; 2004. p. 143–150.Google Scholar
  2. Avouris, N.M., Dimitracopoulou A., Komis, V. & Fidas C. (2002). OCAF: An object-oriented model of analysis of collaborative problem solving. Proceedings of CSCL 2002, Colorado, January, 2002, Hillsdale: Erlbaum, pp. 92-101.Google Scholar
  3. Avouris N, Fiotakis G, Kahrimanis G, Margaritis M, Komis V. Beyond Logging of Fingertip Actions: Analysis of Collaborative Learning Using Multiple Sources of Data. Journal of Interactive Learning Research. 2007;18(2):231–250.Google Scholar
  4. Babič, F., Wagner, J., Jadlovská, S. & Leško, P. (2010). A logging mechanism for acquisition of real data from different collaborative systems for analytical purposes. SAMI 2010: 8th International Symposium on Applied Machine Intelligence and Informatics, Herľany, Slovensko. IEEE, 2010, pp. 109-112.Google Scholar
  5. Baker RSJD, Yacef K. The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining. 2009;1(1):3–17.Google Scholar
  6. Baker, R.S.J.D. (in press). Data Mining For Education. In B. McGaw, P. Peterson & R.S.J.D. Baker (Eds.). International Encyclopedia of Education (3rd edition), Oxford, UK.Google Scholar
  7. Card S, Mackinlay J, Shneiderman B. Readings in Information Visualization: Using Vision to Think. San Francisco: Morgan Kaufmann; 1999.Google Scholar
  8. Derthick, M., Kolojejchick, J. & Roth, S.F., (1997). An Interactive Visualization Environment for Data Exploration. Proceedings of Knowledge Discovery in Databases, pp. 2-9.Google Scholar
  9. Gebert D, Boerner S, Kearney E. Cross-functionality and innovation in new product development teams: A dilemmatic structure and its consequences for the management of diversity. European Journal of Work and Organizational Psychology. 2006;15(4):431–458.CrossRefGoogle Scholar
  10. Gomez-Aguilar DA, Theron R, Garcia-Penalvo FJ. Semantic spiral timeline as a support for e-learning. Journal of Universal Computer Science. 2009;15(7):1526–1545.Google Scholar
  11. Kay, J., Yacef, K. & Reimann, P. (2007). Visualisations for Team Learning: Small Teams Working on Long-Term Projects. Proceedings of the CSCL 2007 conference, New Brunswick, NJ, ISLS, pp. 351-353.Google Scholar
  12. Kerth NL. Project Retrospectives - A Handbook for Team Reviews. New York: Dorset House Publishing; 2001.Google Scholar
  13. KP-Lab (2010b). Report on Empirical Research. Deliverable IV.6.Google Scholar
  14. KP-Lab (2010b). Report on Empirical Research. Deliverable IV.6.Google Scholar
  15. Krogstie, B.R. (2009). A model of retrospective reflection in project based learning utilizing historical data in collaborative tools. Proc. of the 4th European Conference on Technology Enhanced Learning (EC-TEL 2009) - Learning in the Synergy of Multiple Disciplines (pp. 418-432). Berlin: Springer Verlag.Google Scholar
  16. Langley A. Strategies for Theorizing from Process Data. Academy of Management Review. 1999;24(4):619–710.CrossRefGoogle Scholar
  17. Livny M, Ramakrishnan R, Beyer K, Chen G, Donjerkovic D, Lawande S, Myllymaki J, Wenger K. DEVise: integrated querying and visual exploration of large datasets. SIGMOD Rec. 1997;26(2):301–312.CrossRefGoogle Scholar
  18. Paavola, S. & Hakkarainen, K. (2009). From meaning making to joint construction of knowledge practices and artefacts - A trialogical approach to CSCL. Proceedings of the CSCL 2009 conference, Rhodes, Greece, ISLS, pp. 83-92.Google Scholar
  19. Perera D, Kay J, Yacef K, Koprinska I, Zaiane O. Clustering and Sequential Pattern Mining of Online Collaborative Learning Data. IEEE Trans. on Knowl. and Data Eng.. 2009;21(6):759–772.CrossRefGoogle Scholar
  20. Psaromiligkos, Y., Orfanidou, M., Kytagias & C., Zafeiri, E. (2009). Mining Log Data for the Analysis of Learners Behavior in Web-based Learning Management Systems. Operational Research Journal, 1109-2858 (Print) 1866-1505 (Online).Google Scholar
  21. Soller A, Martinez A, Jermann P, Muehlenbrock M. From Mirroring to Guiding: A Review of State of the Art Technology for Supporting Collaborative Learning. Int. J. Artif. Intell. Ed.. 2005;15(4):261–290.Google Scholar
  22. Song, M., & van der Aalst, W.M.P. (2007). Supporting Process Mining by Showing Events at a Glance. Seventeenth Annual Workshop on Information Technologies and Systems (WITS’07), Montreal, Canada, December 8-9, 2007, pp. 139-145.Google Scholar
  23. SpotfireHP: Spotfire Web site.
  24. Spyratos N, Simonenko E, Sugibuchi T. A Functional Model for Data Analysis and Result Visualization. ICEB. 2009;2009:57–6.Google Scholar
  25. Stolte C, Tang D, Hanrahan P. Polaris: A System for Query, Analysis, and Visualization of Multidimensional Relational Databases. IEEE Transactions on Visualization and Computer Graphics. 2002;8(1):52–65.CrossRefGoogle Scholar
  26. Sugibuchi, T., Spyratos, N. & Simonenko, E. (2009). A Framework to Analyze InformationGoogle Scholar
  27. Visualization Based on the Functional Data Model. 13th international Conference on Information Visualization, Los Alamitos, CA, USA, pp. 18-24.Google Scholar
  28. Van der Aalst, W.M.P., van Dongen, Günther, C., Rozinat, A., Verbeek, E. & Weijters, T. (2009) ProM. The Process Mining Toolkit, Proceedings of the BPM 2009 Demonstration Track, Volume 489 of, Ulm, Germany.Google Scholar

Copyright information

© Sense Publishers 2012

Authors and Affiliations

  • Christoph Richter
    • 1
  • Ekaterina Simonenko
    • 2
  • Tsuyoshi Sugibuchi
    • 3
  • Nicolas Spyratos
    • 4
  • Frantisek Babic
    • 5
  • Jozef Wagner
    • 6
  • Jan Paralic
    • 7
  • Michal Racek
    • 8
  • Crina DamŞa
    • 9
  • Vassilis Christophides
    • 10
  1. 1.Christian-Albrechts-Universität zuKielGermany
  2. 2.Univeriste Paris SudParisFrance
  3. 3.Univeriste Paris SudParisFrance
  4. 4.Univeriste Paris SudParisFrance
  5. 5.Technical University of KosiceSlovakia
  6. 6.Technical University of KosiceSlovakia
  7. 7.Technical University of KosiceSlovakia
  8. 8.Pöyry Forest Industry OyFinland
  9. 9.InterMediaUniversity of OsloNorway
  10. 10.FORTH-ICSGreece

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