skip to main content
10.1145/2460296.2460311acmconferencesArticle/Chapter ViewAbstractPublication PageslakConference Proceedingsconference-collections
research-article

Analyzing the flow of ideas and profiles of contributors in an open learning community

Published:08 April 2013Publication History

ABSTRACT

This paper provides an introduction to the scientometric method of main path analysis and its application to detecting idea flows in an online learning community using data from Wikiversity. We see this as a step forward in adapting and adopting network analysis techniques for analyzing the evolution of artifacts in knowledge building communities. The analysis steps are presented in detail including the description of a tool environment ("workbench") designed for flexible use by non-computer experts. Through the definition of directed acyclic graphs the meaningful interconnectedness of learning resources is made accessible to analysis in consideration of the temporal sequence of their creation during a collaborative process. The potential of the method is elaborated for analyzing the overall learning process of a community as well as the individual contributions of the participants.

References

  1. Aviv, R., Erlich, Z., Ravid, G., and Geva, A. 2003. Network Analysis of Knowledge Construction in Asynchronous Learning Networks. Journal for Asynchronous Learning Networks, 7, 1--23.Google ScholarGoogle Scholar
  2. Batagelj, V. (2003). Efficient Algorithms for Citation Network Analysis. arXiv:cs/0309023 {cs.DL}. http://arxiv.org/abs/cs.DL/0309023Google ScholarGoogle Scholar
  3. Batagelj, V. and Mrvar A. 1998. Pajek: A program for large network analysis. Connections, 21, 47--58.Google ScholarGoogle Scholar
  4. Bereiter, C., and Scardamalia, M. 2003. Learning to Work Creatively with Knowledge. In Powerful learning environments: Unravelling basic components and dimensions, E. De Corte, L. Verschaffel, N. Entwistle, and J. van Merriënboer, Eds. Elsevier Science, Oxford, 73--78.Google ScholarGoogle Scholar
  5. Cress, U., and Kimmerle, J. 2008. A systemic and cognitive view on collaborative knowledge building with wikis. International Journal of Computer-Supported Collaborative Learning, 3, 105--122.Google ScholarGoogle ScholarCross RefCross Ref
  6. de Laat, M., Lally, V., Lipponen, L., and Simons, R.-J. 2007. Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis. International Journal of Computer-Supported Collaborative Learning, 2, 87--103.Google ScholarGoogle ScholarCross RefCross Ref
  7. Diesner, J., and Carley K. 2005. Revealing Social Structure from Texts: Meta-Matrix Text Analysis as a novel method for Network Text Analysis. In Causal Mapping for Information Systems and Technology Research: Approaches, Advances, and Illustrations, V. K. Naraynan, and D. J. Armstrong, Eds. Idea Group Publishing, Harrisburg, PA, 81--108.Google ScholarGoogle Scholar
  8. Gelernter, D. 1985. Generative communication in Linda. ACM Trans. Progr. Lang. Syst. (TOPLAS), pp. 80--112. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Halatchliyski, I., Kimmerle, J., and Cress, U. 2011. Divergent and convergent knowledge processes on Wikipedia. In Connecting Computer-Supported Collaborative Learning to Policy and Practice: Conference Proceedings (Hong Kong, China, July 4--8, 2011). CSCL 2011. International Society of the Learning Sciences, Hong Kong, China, 2, 566--570.Google ScholarGoogle Scholar
  10. Halatchliyski, I., Oeberst, A., Bientzle, M., Bokhorst, F., and van Aalst, J. 2012. Unraveling idea development in discourse trajectories. In The future of learning: Proceedings of the 10th international conference of the learning sciences (Sydney, Australia, July 2--6, 2012). ICLS 2012. International Society of the Learning Sciences, Sydney, Australia, 2, 162--166.Google ScholarGoogle Scholar
  11. Hara, N., Bonk, C. J. and Angeli, C. 2000. Content analysis of online discussion in an applied educational psychology course. Instr. Sci. 28, 115--152.Google ScholarGoogle ScholarCross RefCross Ref
  12. Harrer, A., Malzahn N., Zeini S., and Hoppe H. U. 2007. Combining Social Network Analysis with Semantic Relations to Support the Evolution of a Scientific Community. In Mice, Minds, and Society - The Computer Supported Collaborative Learning Conference Proceedings (New Brunswick, USA, July 16--21, 2007). CSCL 2007. Lawrence Erlbaum Associates, Mahwah, NJ, 267--276 Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Hirsch, J. E. 2005. An index to quantify an individual's scientific research output. In Proceedings of the National Academy of Sciences of the United States of America. Vol. 102, Nr. 46 (Nov. 2005), 16569--16572.Google ScholarGoogle ScholarCross RefCross Ref
  14. Hummon, N. P. and Doreian, P. 1989. Connectivity in a Citation Network: The Development of DNA Theory. Soc. Networks, 11, 39--63.Google ScholarGoogle ScholarCross RefCross Ref
  15. Knorr-Cetina, K. 2001. Objectual practice. In The practice turn in contemporary theory, T. R. Schatzki, K. Knorr-Cetina, and E. von Savigny, Eds. Routledge, London and NY, 175--188.Google ScholarGoogle Scholar
  16. Liu, J. S. and Lu, L. Y. Y. 2012. An integrated approach for main path analysis: Development of the Hirsch index as an example. J. Am. Soc. Inf. Sci. Technol. 63, 3 (March 2012), 528--542. DOI= http://dx.doi.org/10.1002/asi.21692 Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Mika, P. 2007. Social Networks and the Semantic Web. Springer, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Popper, K. R. 1968. Epistemology without a knowing subject. Studies in Logic and the Foundations of Mathematics, 52, 333--373.Google ScholarGoogle ScholarCross RefCross Ref
  19. Reffay, C. and Chanier, T. 2002. Social Network Analysis used for modelling collaboration in distance learning groups, In ITS 2002 Lecture Notes in Computer Science, S. A. Cerri, G. Gouardères, and F. Paraguaçu, Eds., Springer, Berlin, Heidelberg, 2363, 31--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Reinhardt, W., Moi, M., and Varlemann, T. 2009. Artefact-Actor-Networks as tie between social networks and artefact networks. In Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing (Washington DC, USA, November, 11--14, 2009). CollaborateCom 2009. IEEE Computer Society.Google ScholarGoogle ScholarCross RefCross Ref
  21. Scardamalia, M., and Bereiter, C. 1994. Computer support for knowledge-building communities. J. Learn. Sci., 3, 265--283.Google ScholarGoogle ScholarCross RefCross Ref
  22. Schön, D. 1983. The Reflective Practitioner, How Professionals Think In Action. Temple Smith, London.Google ScholarGoogle Scholar
  23. Schrire, S. 2004. Interaction and cognition in asynchronous computer conferencing. Instr. Sci. 32, 475--502.Google ScholarGoogle ScholarCross RefCross Ref
  24. Stahl, G., Koschmann, T., and Suthers, D. 2006. Computer-supported collaborative learning: An historical perspective. In Cambridge handbook of the learning sciences, R. K. Sawyer, Ed. Cambridge University Press, Cambridge.Google ScholarGoogle Scholar
  25. Suthers, D. D., Dwyer, N., Medina, R., and Vatrapu, R. 2010. A framework for conceptualizing, representing, and analyzing distributed interaction. International Journal of Computer-Supported Collaborative Learning, 5, 5--42.Google ScholarGoogle ScholarCross RefCross Ref
  26. Wasserman, S. and Faust, K. 1994. Social Networks Analysis: Methods and Applications. Cambridge University Press, Cambridge.Google ScholarGoogle Scholar
  27. Weinbrenner, S., Giemza, A., Hoppe H. U. 2007. Engineering heterogeneous distributed learning environments using tuple spaces as an architectural platform. In Proceedings of the 7th IEEE International Conference on Advanced Learning Technologies (Niigata, Japan, July 18--20, 2007). ICALT 2007. IEEE Computer Society, 434--436.Google ScholarGoogle ScholarCross RefCross Ref
  28. Zeini, S., Göhnert, T., Hoppe, H. U. 2012. The impact of measurement time on subgroup detection in online communities. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (Istanbul, Turkey, August 26--29, 2012). ASONAM 2012. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Analyzing the flow of ideas and profiles of contributors in an open learning community

              Recommendations

              Comments

              Login options

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

              Sign in
              • Published in

                cover image ACM Conferences
                LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge
                April 2013
                300 pages
                ISBN:9781450317856
                DOI:10.1145/2460296

                Copyright © 2013 ACM

                Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

                Publisher

                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 8 April 2013

                Permissions

                Request permissions about this article.

                Request Permissions

                Check for updates

                Qualifiers

                • research-article

                Acceptance Rates

                LAK '13 Paper Acceptance Rate16of58submissions,28%Overall Acceptance Rate236of782submissions,30%

              PDF Format

              View or Download as a PDF file.

              PDF

              eReader

              View online with eReader.

              eReader