Computational approaches to connecting levels of analysis in networked learning communities
Pages 285 - 286
Abstract
The focus of this workshop is on the potential benefits and challenges of using specific computational methods to analyze interactions in networked learning environments, particularly with respect to integrating multiple analytic approaches towards understanding learning at multiple levels of agency, from individual to collective. The workshop is designed for researchers interested in analytical studies of collaborative and networked learning in socio-technical networks, using data-intensive computational methods of analysis (including social-network analysis, log-file analysis, information extraction and data mining). The workshop may also be of interest to pedagogical professionals and educational decision makers who want to evaluate the potential of learning analytics techniques to better inform their decisions regarding learning in technology-rich environments.
References
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A. Harrer, N. Malzahn, S. Zeini, and H. U. Hoppe, Combining Social Network Analysis with semantic relations to support the evolution of a scientific community, in C. Chinn, G. Erkens and S. Puntambekar, eds., The Computer Supported Collaborative Learning (CSCL) Conference 2007, International Society of the Learning Sciences, New Brunswick, 2007, pp. 267--276.
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C. Haythornthwaite, Social network methods and measures for examining e-learning, E-learning seminar, University of Southampton, 2005.
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A. Martinez, Y. Dimitriadis, E. Gomez-Sanchez, B. Rubia-Avi, I. Jorrin-Abellan and J. A. Marcos, Studying participation networks in collaboration using mixed methods, International Journal of Computer-Supported Collaborative Learning, 1 (2006), pp. 383--408.
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P. Reimann, Time is precious: Variable- and event-centred approaches to process analysis in CSCL research, Computer Supported Collaborative Learning, 4 (2009), pp. 239--257.
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D. D. Suthers, H. U. Hoppe, M. De Laat, and S. Buckingham Shum. LAK, Connecting levels and methods of analysis in networked learning communities. Proceedings of LAK 2012. ACM, (2012), pp. 11--13.
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D. D. Suthers and D. Rosen, 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, 2011.
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Index Terms
- Computational approaches to connecting levels of analysis in networked learning communities
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Information & Contributors
Information
Published In
March 2014
301 pages
ISBN:9781450326643
DOI:10.1145/2567574
- Conference Chairs:
- Mattew Pistilli,
- James Willis,
- Drew Koch,
- Kimberly Arnold,
- Program Chairs:
- Stephanie Teasley,
- Abelardo Pardo
Copyright © 2014 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
- JNGI: John N. Gardner Institute for Excellence in Undergraduate Education
- University of Wisc-Madison: University of Wisconsin-Madison
- SoLAR: The Society for Learning Analytics Research
- Purdue University: Purdue University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 March 2014
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LAK '14
Sponsor:
- JNGI
- University of Wisc-Madison
- SoLAR
- Purdue University
LAK '14: Learning Analytics and Knowledge Conference 2014
March 24 - 28, 2014
Indiana, Indianapolis, USA
Acceptance Rates
LAK '14 Paper Acceptance Rate 13 of 44 submissions, 30%;
Overall Acceptance Rate 236 of 782 submissions, 30%
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