Multi-mediated community structure in a socio-technical networksuthers
Suthers, D. D., & Chu, K.-H. (2012, April 29-May 2, 2012). Multi-mediated community structure in a socio-technical network. Paper presented at the Learning Analytics and Knowledge 2012 conference
TechLogic 2014 Keynote on Inverting an Algorithms Class (Extended Version)suthers
Discussion of the inversion of an Algorithms course: how it is motivated by learning theory; how the activities are organized; outcomes. This is an expanded version of an invited keynote talk for the "TechLogic" conference at the University of Hawaii at Manoa.
Keynote Talk at ITS 2014: Multilevel Analysis of Socially Embedded Learningsuthers
An invited keynote talk given at the Intelligent Tutoring Systems (ITS) conference in Honolulu, 2014. Begins with some fun observations about being an academic in Hawaii. Motivated both by my early work studying dyadic interaction with Belvedere and a theoretical view of the multi-dimensionality of distributed learning in socio-technical networks and consequent analytic challenges, outlines a framework called "Traces" that addresses these challenges. Most of the examples are of analysis of Tapped In, a successful online network of educational professionals from 1997-2013. Probably the most comprehensive overview of my research to date.
A Framework for Multi-Level Analysis of Distributed Interactionsuthers
Interaction, Mediation, and Ties: A Framework for Multi-Level Analysis of Distributed Interaction (presented at the workshop on Connecting Levels and Methods of Analysis in Networked Communities at the Learning Analytics and Knowledge Conference 2012, Vancouver)
Suthers & Rosen, Learning Analytics and Knowledge 2011suthers
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.
Learning as a Complex Phenomenon: Challenges for Learning Analytics suthers
Presentation given at Learning Analytics Summer Institute 2013. Theories of learning postulate multiple agencies (individual, small group, and collective) and epistemologies e.g., acquisition, intersubjective meaning making, participation). Though we may research these separately, learners experience all of these at once, so learning is a complex phenomenon. Need to connect levels of analysis. Also need to bring in multiple "voices" or theoretical and research traditions, and learn how to manage productive multivocality among them. Two efforts towards this end are briefly described. If it takes on these challenges, Learning Analytics can help by enabling us to manage multiple levels of analysis.
Multi-mediated community structure in a socio-technical networksuthers
Suthers, D. D., & Chu, K.-H. (2012, April 29-May 2, 2012). Multi-mediated community structure in a socio-technical network. Paper presented at the Learning Analytics and Knowledge 2012 conference
TechLogic 2014 Keynote on Inverting an Algorithms Class (Extended Version)suthers
Discussion of the inversion of an Algorithms course: how it is motivated by learning theory; how the activities are organized; outcomes. This is an expanded version of an invited keynote talk for the "TechLogic" conference at the University of Hawaii at Manoa.
Keynote Talk at ITS 2014: Multilevel Analysis of Socially Embedded Learningsuthers
An invited keynote talk given at the Intelligent Tutoring Systems (ITS) conference in Honolulu, 2014. Begins with some fun observations about being an academic in Hawaii. Motivated both by my early work studying dyadic interaction with Belvedere and a theoretical view of the multi-dimensionality of distributed learning in socio-technical networks and consequent analytic challenges, outlines a framework called "Traces" that addresses these challenges. Most of the examples are of analysis of Tapped In, a successful online network of educational professionals from 1997-2013. Probably the most comprehensive overview of my research to date.
A Framework for Multi-Level Analysis of Distributed Interactionsuthers
Interaction, Mediation, and Ties: A Framework for Multi-Level Analysis of Distributed Interaction (presented at the workshop on Connecting Levels and Methods of Analysis in Networked Communities at the Learning Analytics and Knowledge Conference 2012, Vancouver)
Suthers & Rosen, Learning Analytics and Knowledge 2011suthers
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.
Learning as a Complex Phenomenon: Challenges for Learning Analytics suthers
Presentation given at Learning Analytics Summer Institute 2013. Theories of learning postulate multiple agencies (individual, small group, and collective) and epistemologies e.g., acquisition, intersubjective meaning making, participation). Though we may research these separately, learners experience all of these at once, so learning is a complex phenomenon. Need to connect levels of analysis. Also need to bring in multiple "voices" or theoretical and research traditions, and learn how to manage productive multivocality among them. Two efforts towards this end are briefly described. If it takes on these challenges, Learning Analytics can help by enabling us to manage multiple levels of analysis.