A Unified Framework for Multi-Level Analysis of Distributed Learning Dan Suthers Department of Information and Computer Sciences and Communication and Information Sciences Program Devan Rosen Department of Speech University of Hawaii Funded by NSF VOSS
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Suthers & Rosen, Learning Analytics and Knowledge 2011
Presentation of Suthers, D. D., & Rosen, D. (2011). A unified framework for multi-level analysis of distributed learning Proceedings of the First International Conference on Learning Analytics & Knowledge, Banff, Alberta, February 27-March 1, 2011. Abstract: Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs. As a result, the coherence of distributed interaction and emergent phenomena are analytically cloaked. Understanding distributed learning and knowledge creation requires multi-level analysis of the situated accomplishments of individuals and small groups and of how this local activity gives rise to larger phenomena in a network. We have developed an abstract transcript representation that provides a unified analytic artifact of distributed activity, and an analytic hierarchy that supports multiple levels of analysis. Log files are abstracted to directed graphs that record observed relationships (contingencies) between events, which may be interpreted as evidence of interaction and other influences between actors. Contingency graphs are further abstracted to twomode directed graphs that record how associations between actors are mediated by digital artifacts and summarize sequential patterns of interaction. Transitive closure of these associograms yields sociograms, to which existing network analytic techniques may be applied, yielding aggregate results that can then be interpreted by reference to the other levels of analysis. We discuss how the analytic hierarchy bridges between levels of analysis and theory.
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A Unified Framework for Multi-Level Analysis of Distributed Learning
Dan SuthersDepartment of Information and Computer Sciences
andCommunication and Information Sciences Program
Devan RosenDepartment of Speech
University of Hawaii
Funded by NSF VOSS
Multiple theories of how learning takes place in social settings ▪ From social as stimulus to social entity as learning agent▪ From "networked individualism" to "maintaining a joint
conception of a problem" ▪ From "diffusion of innovations" to "knowledge building"
All involve uptake: when an actor takes (a trace of) another actor's activity as being relevant in some way for his or her current activity ▪ See Suthers (ijCSCL 2006) for discussion of learning
epistemologies; and Suthers et al. (ijCSCL 2010) for uptake
Uptake is evidenced by how individual actions are observably contingent on the actions of others in their socio-technical network contexts
Learning in Social Settings
Fundamental question: how learning takes place through the interplay between individual and collective agency ▪ Situated accomplishments of individuals and small groups▪ How these local accomplishments give rise to larger
phenomena in networksRequires coordinated multi-level analysis
Activity can be distributed across multiple media and sites ▪ Traces of activity may be fragmented across multiple logs ▪ Logs may record activity in the wrong ontology for analysis
(e.g., media-level events rather than interaction or ties) Distributed activity may be analytically cloaked
✓ Abstract transcript representation that collects relevant events into a single analytic artifact
✓ Analytic hierarchy that supports multiple levels of analysis
Uptake Graph (Interaction Model)▪ Finds uptake not manifest in threading structure▪ P3 plays an integrative role in this discussion▪ Other examples in Suthers et al. (ijCSCL 2010)
Current Focus ▪ Identifying where significant activity takes place and
characterizing the nature of that activity (talk tomorrow afternoon)
▪ Nonlocal consequences of local activities, e.g., trace contingencies to find whether actors move ideas and other actors to new settings
Current Research
As a data representation ▪ Integration of distributed data: uncloak distributed interaction▪ Common format for reuse of algorithms
As an analytic framework ▪ Multi-Level Multi-Theoretical analysis possible ▪ Multiple ontologies allow for mapping between interaction,
mediated affiliation and tie levels of analysis
Workshop: Connecting Levels of Learning in Networked Communities ▪ July 5th @ CSCL in Hong Kong ▪ http://www.isls.org/cscl2011/ or http://engaged.hnlc.org/