Analysis of dynamic resource access patterns in a blended learning course

ACM International Conference Proceeding Series

Volumn , Issue , 2014, Pages 173-182

  Hecking, Tobias ^a   Ziebarth, Sabrina ^a   Ulrich Hoppe, H ^a  

^a UNIVERSITY OF DUISBURG ESSEN   (Germany)

Author keywords

Learning analytics; Learning resources; MOOCs; Social network analysis

Indexed keywords

COMPUTER APPLICATIONS; COMPUTER PROGRAMMING;

BLENDED LEARNING; DYNAMIC RESOURCES; LEARNING ANALYTICS; LEARNING MATERIALS; LEARNING RESOURCE; MOOCS; RESOURCE ACCESS; UNIVERSITY COURSE;

SOCIAL NETWORKING (ONLINE);

EID: 84898775100     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2567574.2567584     Document Type: Conference Paper

References (25)

1. Asur, S., Parthasarathy, S. and Ucar, D. An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Trans.Knowl.Discov.Data, 3, 4 (Dec 2009), 16:1-16:36. DOI=10.1145/1631162. 1631164.
2. Backstrom, L., Huttenlocher, D., Kleinberg, J. and Lan, X. Group formation in large social networks: membership, growth, and evolution. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. (Philadelphia, PA, USA). ACM, New York, NY, USA, 2006, 44-54.
3. Belanger, Y. and Thornton, J. Bioelectricity: A Quantitative Approach Duke University's First MOOC. Duke Space Library. Duke University. 10 March 2013 (2013).
4. Bruff, D. O., Fisher, D. H., McEwen, K. E. and Smith, B. E. Wrapping a MOOC: Student Perceptions of an Experiment in Blended Learning. Journal of Online Learning & Teaching, 9, 2 (2013).
5. Fini, A. The technological dimension of a massive open online course: The case of the CCK08 course tools. The International Review Of Research In Open And Distance Learning, 10, 5 (2009).
6. Fortunato, S. Community detection in graphs. Physics Reports, 486, 3 (2010), 75-174.
7. Garrison, D. R. and Kanuka, H. Blended learning: Uncovering its transformative potential in higher education. The internet and higher education, 7, 2 (2004), 95-105.
8. Girvan, M. and Newman, M. E. J. Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99, 12 (2002), 7821-7826. DOI=10.1073/pnas.122653799.
9. Grünewald, F., Meinel, C., Totschnig, M. and Willems, C. Designing MOOCs for the Support of Multiple Learning Styles. In Scaling up Learning for Sustained Impact. Springer, 8095, (2013), 371-382.
10. Greene, D., Doyle, D. and Cunningham, P. Tracking the Evolution of Communities in Dynamic Social Networks. In Proceedings of the International Conference on Advances in Social Network Analysis and Mining, (2010), 176-183.
11. Halatchliyski, I., Hecking, T., Göhnert, T. and Hoppe, H. U. Analyzing the flow of ideas and profiles of contributors in an open learning community. In Proceedings of the Third International Conference on Learning Analytics and Knowledge. (Leuven, Belgium). ACM, New York, NY, USA, 2013, 66-74.
12. Harrer, A., Malzahn, N., Zeini, S. and Hoppe, H. U. Combining social network analysis with semantic relations to support the evolution of a scientific community. In Anonymous Proceedings of the 8th international conference on Computer supported collaborative learning. (New Brunswick, New Jersey, USA). International Society of the Learning Sciences, 2007, 270-279.
13. Hoppe, U., Pinkwart, N., Oelinger, M., Zeini, S., Verdejo, F., Barros, B. and Mayorga, J. I. Building bridges within learning communities through ontologies and thematic objects. In Anonymous Proceedings of the 2005 conference on Computer support for collaborative learning: learning 2005: The next 10 years! (Taipei, Taiwan). International Society of the Learning Sciences, 2005, 211-220.
14. Kizilcec, R. F., Piech, C. and Schneider, E. Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In Anonymous Proceedings of the Third International Conference on Learning Analytics and Knowledge. (Leuven, Belgium). ACM, New York, NY, USA, 2013, 170-179.
15. Lehmann, S., Schwartz, M. and Hansen, L. K. Biclique communities. Phys Rev E., 78, 1 (Jul 2008), 016108. DOI=10.1103/PhysRevE.78.016108.
16. Leskovec, J., Kleinberg, J. and Faloutsos, C. Graphs over time: densification laws, shrinking diameters and possible explanations. In Anonymous Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. (Chicago, Illinois, USA). ACM, New York, NY, USA, 2005, 177-187.
17. Palla, G., Barabasi, A. L. and Vicsek, T. Quantifying social group evolution. Nature, 446, 7136 (2007), 664-667.
18. Palla, G., Derenyi, I., Farkas, I. and Vicsek, T. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435, 7043 (2005), 814-818.
19. Rodriguez, O. MOOCs and the AI-Stanford like Courses: Two successful and distinct course formats for massive open online courses. European Journal of Open, Distance, and E-Learning, (2012).
20. Rodriguez, O. The concept of openness behind c and x-MOOCs (Massive Open Online Courses). Open Praxis, 5, 1 (2013), 67-73.
21. Romero, C., Ventura, S. and García, E. Data mining in course management systems: Moodle case study and tutorial. Comput. Educ., 51, 1 (2008), 368-384.
22. Waldrop, M. M. Online learning: Campus 2.0. Nature, 495, 7440 (2013), 160-163. DOI=10.1038/495160a.
23. Wasserman, S. and Faust, K. Social Network Analysis: Methods and Applications. Cambridge University Press 1994.
24. Watts, D. J. and Strogatz, S. H. Collective dynamics of ̀small-world' networks. Nature, 393, 6684 (1998), 440-442.
25. Ziebarth, S., Malzahn, N. and Hoppe, H. U. Match balls - A multi-agent-system for ontology-based collaborative learning games. In Proceedings of the 18th international conference on Collaboration and Technology. (Raesfeld, Germany, ). Springer-Verlag, Berlin, Heidelberg, (2012), 208-222.