Community Learning Analytics – Challenges and Opportunities

  • Ralf Klamma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8167)

Abstract

Learning Analytics has become a major research area recently. In particular learning institutions seek ways to collect, manage, analyze and exploit data from learners and instructors for the facilitation of formal learning processes. However, in the world of informal learning at the workplace, knowledge gained from formal learning analytics is only applicable on a commodity level. Since professional communities need learning support beyond this level, we need a deep understanding of interactions between learners and other entities in community-regulated learning processes - a conceptual extension of self-regulated learning processes. In this paper, we discuss scaling challenges for community learning analytics, give both conceptual and technical solutions, and report experiences from ongoing research in this area.

Keywords

learning analytics community learning analytics visual analytics community of practice expert identification overlapping community detection 

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References

  1. 1.
    Song, E., Petrushyna, Z., Cao, Y., Klamma, R.: Learning Analytics at Large: The Lifelong Learning Network of 160,000 European Teachers. In: Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M. (eds.) EC-TEL 2011. LNCS, vol. 6964, pp. 398–411. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Baker, R.S.J.D., Yacef, K.: The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining 1, 3–17 (2009)Google Scholar
  3. 3.
    Shum, S.B., Ferguson, R.: Social Learning Analytics. Educational Technology & Society 15, 3–26 (2012)Google Scholar
  4. 4.
    Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., Ludgate, H.: NMC Horizon Report: 2013 Higher Education Edition (2013)Google Scholar
  5. 5.
    Wenger, E.: Community of Practice: Learning, Meaning, and Identity. Cambridge University Press, Cambridge (1998)CrossRefGoogle Scholar
  6. 6.
    Petrushyna, Z., Klamma, R.: No Guru, No Method, No Teacher: Self-classification and Self-modelling of E-Learning Communities. In: Dillenbourg, P., Specht, M. (eds.) EC-TEL 2008. LNCS, vol. 5192, pp. 354–365. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Krenge, J., Petrushyna, Z., Kravcik, M., Klamma, R.: Identification of Learning Goals in Forum-based Communities. In: 11th IEEE International Conference on Advanced Learning Technologies, pp. 307–309. IEEE (2011)Google Scholar
  8. 8.
    Saint-Andre, P.: RFC 3920 – Extensible Messaging and Presence Protocol (XMPP): Core (2004)Google Scholar
  9. 9.
    Rizova, P.: Are you Networked for Successful Innovation? MIT Sloan Management Review 47, 49–55 (2006)Google Scholar
  10. 10.
    Chesbrough, H.W.: Open Innovation: The new imperative for creating and profiting from technology. Harvard Business School Press (2003)Google Scholar
  11. 11.
    Burt, R.S.: The Network Structure of Social Capital. Research in Organizational Behavior 22, 345–423 (2000)CrossRefGoogle Scholar
  12. 12.
    Daniel, B., McCalla, G., Schwier, R.A.: A Process Model for Building Social Capital in Virtual Learning Communities. In: International Conference on Computers in Education, pp. 574–575. IEEE Computer Society (2002)Google Scholar
  13. 13.
    Klamma, R., Spaniol, M., Cao, Y., Jarke, M.: Pattern-Based Cross Media Social Network Analysis for Technology Enhanced Learning in Europe. In: Nejdl, W., Tochtermann, K. (eds.) EC-TEL 2006. LNCS, vol. 4227, pp. 242–256. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Worsley, M., Blikstein, P.: What’s an Expert? Using learning analytics to identify emergent markers of expertise through automated speech, sentiment and sketch analysis. In: Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero, C., Stamper, J. (eds.) 4th International Conference on Educational Data Mining, pp. 235–240 (2011)Google Scholar
  15. 15.
    Scheffel, M., Niemann, K., Pardo, A., Leony, D., Friedrich, M., Schmidt, K., Wolpers, M., Kloos, C.D.: Usage pattern recognition in student activities. In: Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M. (eds.) EC-TEL 2011. LNCS, vol. 6964, pp. 341–355. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Keim, D.A., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual Analytics: Definition, Process, and Challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Jansen, B.J.: Understanding User-Web Interactions via Web Analytics. Synthesis Lectures on Information Concepts, Retrieval, and Services 1, 1–102 (2009)CrossRefGoogle Scholar
  18. 18.
    Kaushik, A.: Web Analytics 2.0: The Art of Online Accountability & Science of Customer Centricity. Wiley, Indianapolis (2010)Google Scholar
  19. 19.
    Zimmerman, B.J.: Becoming a Self-Regulated Learner: An Overview. Theory Into Practice 41, 64–70 (2002)CrossRefGoogle Scholar
  20. 20.
    Fruhmann, K., Nussbaumer, A., Albert, D.: A Psycho-Pedagogical Framework for Self-Regulated Learning in a Responsive Open Learning Environment. In: Hambach, S., Martens, A., Tavangarian, D., Urban, B. (eds.) 3rd International Conference eLearning Baltics Science, Fraunhofer Verlag, Rostock (2010)Google Scholar
  21. 21.
    van Harmelen, M.: Personal Learning Environments. In: Kinshuk, Koper, R., Kommers, P., Kirschner, P.A., Sampson, D.G., Didderen, W., eds.: 6th IEEE International Conference on Advanced Learning Technologies, 815–816. IEEE (2006)Google Scholar
  22. 22.
    Nussbaumer, A., Berthold, M., Dahrendorf, D., Schmitz, H.-C., Kravcik, M., Albert, D.: A Mashup Recommender for Creating Personal Learning Environments. In: Popescu, E., Li, Q., Klamma, R., Leung, H., Specht, M. (eds.) ICWL 2012. LNCS, vol. 7558, pp. 79–88. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  23. 23.
    Berthold, M., Nussbaumer, A., Albert, D.: Integrating Collaborative Learning Into the Self-regulated Learning Process Model. In: 11th IEEE International Conference on Advanced Learning Technologies, pp. 615–616. IEEE (2011)Google Scholar
  24. 24.
    Drachsler, H., Hummel, H.G.K., Koper, R.: Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model. International Journal of Learning Technology 3, 404–423 (2008)CrossRefGoogle Scholar
  25. 25.
    Ackerman, M.S., Pipek, V., Wulf, V.: Expertise Sharing: Beyond Knowledge Management. MIT Press, Cambridge (2002)Google Scholar
  26. 26.
    Lin, C.Y., Ehrlich, K., Griffiths-Fisher, K., Desforges, C.: SmallBlue: People Mining for Expertise Search. IEEE Multimedia 15, 78–84 (2008)Google Scholar
  27. 27.
    Reichling, T., Veith, M., Wulf, V.: Expert Recommender: Designing for a Network Organization. Journal of Computer Supported Cooperative Work (JCSCW) 16, 431–465 (2007)CrossRefGoogle Scholar
  28. 28.
    Kleinberg, J.M.: Authoritative Sources in a Hyperlinked Environment. Journal of the ACM 46, 604–632 (1999)MathSciNetCrossRefMATHGoogle Scholar
  29. 29.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69 (2004)Google Scholar
  30. 30.
    Yang, J., Leskovec, J.: Structure and Overlaps of Communities in Networks. CoRR abs/1205.6228 (2012)Google Scholar
  31. 31.
    Rashed, K.A.N., Renzel, D., Klamma, R., Jarke, M.: Community and trust-aware fake media detection. Multimedia Tools and Applications, 1–30 (2012)Google Scholar
  32. 32.
    Kovachev, D., Cao, Y., Klamma, R., Jarke, M.: Learn-as-you-go: New Ways of Cloud-Based Micro-learning for the Mobile Web. In: Leung, H., Popescu, E., Cao, Y., Lau, R.W.H., Nejdl, W. (eds.) ICWL 2011. LNCS, vol. 7048, pp. 51–61. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  33. 33.
    Klamma, R., Spaniol, M., Cao, Y.: Community Aware Content Adaptation for Mobile Technology Enhanced Learning. In: Nejdl, W., Tochtermann, K. (eds.) EC-TEL 2006. LNCS, vol. 4227, pp. 227–241. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  34. 34.
    Porter, M.: Clusters and Competition: New Agendas for Companies, Governments, and Institutions. Harvard Business School Press, Boston (1998)Google Scholar
  35. 35.
    Barabási, A.L., Ravasz, E., Oltvai, Z.: Hierarchical organization of modularity in complex networks. Statistical Mechanics of Complex Networks 625, 46–65 (2003)CrossRefGoogle Scholar
  36. 36.
    Renzel, D., Klamma, R.: From Micro to Macro: Analyzing activity in the ROLE Sandbox. In: Suthers, D., Verbert, K., Duval, E., Ochoa, X. (eds.) The Third ACM International Conference on Learning Analytics, pp. 250–254. ACM (2013)Google Scholar
  37. 37.
    Renzel, D., Klamma, R., Spaniol, M.: MobSOS - A Testbed for Mobile Multimedia Community Services. In: Ninth International Workshop on Image Analysis for Multimedia Interactive Services, pp. 139–142. IEEE (2008)Google Scholar
  38. 38.
    Delone, W.H., McLean, E.R.: Information Systems Success: The Quest for the Dependent Variable. Information Systems Research 3, 60–95 (1992)CrossRefGoogle Scholar
  39. 39.
    Renzel, D., Klamma, R.: Semantic Monitoring and Analyzing Context-aware Collaborative Multimedia Services. In: International Conference on Semantic Computing, pp. 630–635 (2009)Google Scholar
  40. 40.
    Cao, Y., Renzel, D., Jarke, M., Klamma, R., Lottko, M., Toubekis, G., Jansen, M.: Well-Balanced Usability and Annotation Complexity in Interactive Video Semantization. In: 4th International Conference on Multimedia and Ubiquitous Engineering, pp. 1–8 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ralf Klamma
    • 1
  1. 1.Advanced Community Information Systems (ACIS) Informatik 5RWTH Aachen UniversityAachenGermany

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