Handbook of Learning Analytics

Chapter 16

Handbook of Learning Analytics
First Edition

Multilevel Analysis of Activity and
Actors in Heterogeneous Networked Learning

Daniel D. Suthers


Learning in today’s networked environments is often distributed across multiple media and sites, and takes place simultaneously via multiple levels of agency and processes. This is a challenge for those wishing to study learning as embedded in social networks, or simply to monitor a networked learning environment for practical purposes. Traces of activity may be fragmented across multiple logs, and the granularity at which events are recorded may not match analytic needs. This chapter describes an analytic framework, Traces, for analyzing participant interaction in one or more digital settings by computationally deriving higher levels of description. The Traces framework includes concepts for modelling interaction in sociotechnical systems, a hierarchy of models with corresponding representations, and computational methods for translating between these levels by transforming representations. Potential applications include identifying sessions of interaction, key actors within sessions, relationships between actors, changes in participation over time, and groups or communities of learners.

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References (34)

Allen, I. E., & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. Babson Survey Research Group. http://www.onlinelearningsurvey.com/reports/changingcourse.pdf

Barab, S. A., Kling, R., & Gray, J. H. (2004). Designing for virtual communities in the service of learning. New York: Cambridge University Press.

Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment. doi:10.1088/1742-5468/2008/10/P10008

Castells, M. (2001). The Internet galaxy: Reflections on the Internet, business, and society. Oxford, UK: Oxford University Press.

Charles, C. (2013). Analysis of communication flow in online chats. Unpublished Master’s Thesis, University of Duisburg-Essen, Duisburg, Germany. (2245897)

Cohen, A. P. (1985). The symbolic construction of community. New York: Routledge.

de Laat, M. (2006). Networked learning. Apeldoorn, Netherlands: Politie Academie.

de Laat, M., Lally, V., Lipponen, L., & 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(1), 87–103.

Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486, 75–174.

Haythornthwaite, C., & Gruzd, A. (2008). Analyzing networked learning texts. In V. Hodgson, C. Jones, T. Kargidis, D. McConnell, S. Retalis, D. Stamatis, & M. Zenios (Eds.), Proceedings of the 6th International Conference on Networked Learning (NLC 2008), 5–6 May 2008, Halkidiki, Greece (pp. 136–143).

Joseph, S., Lid, V., & Suthers, D. D. (2007). Transcendent communities. In C. Chinn, G. Erkens, & S. Puntambekar (Eds.), Proceedings of the 7th International Conference on Computer-Supported Collaborative Learning (CSCL 2007), 16–21 July 2007, New Brunswick, NJ, USA (pp. 317–319). International Society of the Learning Sciences.

Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. New York: Oxford University Press.

Lemke, J. L. (2000). Across the scales of time: Artifacts, activities, and meanings in ecosocial systems. Mind, Culture & Activity, 7(4), 273–290.

Licoppe, C., & Smoreda, Z. (2005). Are social networks technologically embedded? How networks are changing today with changes in communication technology. Social Networks, 27(4), 317–335.

Martínez, A., Dimitriadis, Y., Gómez-Sánchez, E., Rubia-Avi, B., Jorrín-Abellán, I., & Marcos, J. A. (2006). Studying participation networks in collaboration using mixed methods. International Journal of Computer-Supported Collaborative Learning, 1(3), 383–408.

Monge, P. R., & Contractor, N. S. (2003). Theories of communication networks. Oxford, UK: Oxford University Press.

Newman, M. E. J. (2010). Networks: An introduction. Oxford, UK: Oxford University Press.

O’Reilly, T. (2005). What is Web 2.0: Design patterns and business models for the next generation of software. http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html

Renninger, K. A., & Shumar, W. (2002). Building virtual communities: Learning and change in cyberspace. Cambridge, UK: Cambridge University Press.

Rosé, C. P., Wang, Y.-C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., & Fischer, F. (2008). Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. International Journal of Computer-Supported Collaborative Learning, 3(3), 237–271. doi:10.1007/s11412-007-9034-0

Rosen, D., & Corbit, M. (2009). Social network analysis in virtual environments. Proceedings of the 20th ACM conference on Hypertext and Hypermedia (HT ’09), 29 June–1 July 2009, Torino, Italy (pp. 317–322). New York: ACM.

Siemens, G. (2013). Massive open online courses: Innovation in education? In R. McGreal, W. Kinuthia, S. Marshall, & T. McNamara (Eds.), Open educational resources: Innovation, research and practice (pp. 5–15). Vancouver, BC: Commonwealth of Learning and Athabasca University.

Suthers, D. D. (2006). Technology affordances for intersubjective meaning-making: A research agenda for CSCL. International Journal of Computer-Supported Collaborative Learning, 1(3), 315–337.

Suthers, D. D. (2015). From micro-contingencies to network-level phenomena: Multilevel analysis of activity and actors in heterogeneous networked learning environments. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼLA), 16–20 March, Poughkeepsie, NY, USA (pp. 368–377). New York: ACM.

Suthers, D. D. (2017). Applications of cohesive subgraph detection algorithms to analyzing socio-technical networks. Proceedings of the 50th Hawaii International Conference on System Sciences (HICSS-50), 4–7 January 2017, Waikoloa Village, HI, USA (CD-ROM). IEEE Computer Society.

Suthers, D. D., & Dwyer, N. (2015). Identifying uptake, sessions, and key actors in a socio-technical network. Proceedings of the 44th Hawaii International Conference on System Sciences (HICSS-44), 5–8 January 2011, Kauai, Hawai’i (CD-ROM). IEEE Computer Society.

Suthers, D. D., Dwyer, N., Medina, R., & Vatrapu, R. (2010). A framework for conceptualizing, representing, and analyzing distributed interaction. International Journal of Computer Supported Collaborative Learning, 5(1), 5–42. doi:10.1007/s11412-009-9081-9

Suthers, D. D., Fusco, J., Schank, P., Chu, K.-H., & Schlager, M. (2013). Discovery of community structures in a heterogeneous professional online network. Proceedings of the 46th Hawaii International Conference on the System Sciences (HICSS-46), 7–10 January 2013, Maui, HI, USA (CD-ROM). IEEE Computer Society.

Suthers, D. D., Lund, K., Rosé, C. P., Teplovs, C., & Law, N. (2013). Productive multivocality in the analysis of group interactions. Springer.

Suthers, D. D., & Rosen, D. (2011). A unified framework for multi-level analysis of distributed learning. In G. Conole, D. Gašević, P. Long, & G. Siemens (Eds.), Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK ʼLA), 27 February–1 March 2011, Banff, AB, Canada (pp. 64–74). New York: ACM.

Tönnies, F. (2001). Community and civil society (J. Harris & M. Hollis, Trans. from Gemeinschaft und Gesellschaft, 1887) Cambridge, UK: Cambridge University Press.

Trausan-Matu, S., & Rebedea, T. (2010). A polyphonic model and system for inter-animation analysis in chat conversations with multiple participants. In A. Gelbukh (Ed.), Computational linguistics and intelligent text processing (pp. 354–363). Springer.

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press.

Wellman, B., Quan-Haase, A., Boase, J., Chen, W., Hampton, K., Diaz, I., & Miyata, K. (2003). The social affordances of the Internet for networked individualism. Journal of Computer-Mediated Communication, 8(3). doi:10.1111/j.1083-6101.2003.tb00216.x

About this Chapter

Multilevel Analysis of Activity and Actors in Heterogeneous Networked Learning Environments

Book Title
Handbook of Learning Analytics

pp. 189-197




Society for Learning Analytics Research

Daniel D. Suthers

Author Affiliations
Department of Information and Computer Sciences, University of Hawaii, USA

Charles Lang1
George Siemens2
Alyssa Wise3
Dragan Gašević4

Editor Affiliations
1. Teachers College, Columbia University, USA
2. LINK Research Lab, University of Texas at Arlington, USA
3. Learning Analytics Research Network, New York University, USA
4. Schools of Education and Informatics, University of Edinburgh, UK


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