DIGITAL LIBRARY
ONLINE LEARNER SELF REGULATION: LEARNING PRESENCE, VIEWED THROUGH QUANTITATIVE CONTENT- AND SOCIAL NETWORK ANALYSIS
University at Albany, State University of New York (UNITED STATES)
About this paper:
Appears in: EDULEARN12 Proceedings
Publication year: 2012
Pages: 3144-3151
ISBN: 978-84-695-3491-5
ISSN: 2340-1117
Conference name: 4th International Conference on Education and New Learning Technologies
Dates: 2-4 July, 2012
Location: Barcelona, Spain
Abstract:
This paper presents an extension of an ongoing study of online learning framed within the Community of Inquiry model (Garrison, Anderson & Archer, 2001) in which we further examine a new construct meant to reflect online learner self- and co-regulation. We argue that the roles of online students differ from online instructors, even within collaborative and inquiry based models and that it is important to articulate the roles of successful online learners engaged in these pedagogical approaches.

To gain insight, we present results of a study using quantitative content analysis and social network analysis (SNA) in a complementary fashion. SNA has been applied to online learning, but lacks a theoretical foundation for understanding how and why networks of interaction might promote better learning. This paper begins to fill that conceptual gap.

A great deal of educational research indicates that successful learners self regulate in ways that lead to success and emerging research indicates self-regulation is especially important in interactive online learning environments. Such regulation includes an iterative cycle of forethought and planning; monitoring and adapting strategies for learning, and reflecting on results.

To analyze online learner self regulation we used quantitative content analysis to identify metrics of self and co-regulation of learning, a construct we label “learning presence”. We also sought to apply the results of quantitative content analysis in social network analysis. We suggest that the learning presence construct reflects theory-based indicators of quality that can be applied within social network analysis to better understand the relationship between self regulation, interaction, and learning while providing insight into the application of social network analysis in online education research.

We conclude that significant benefits accrue to online students who demonstrated better self- and co-regulation and that these benefits are manifest in metrics associated with social network analysis. These results extend and confirm both the Community of Inquiry Framework and extend previous research using Social Network Analysis in investigations of online learning.
Keywords:
Community of Inquiry Model, On-line Learning, Social network analysis.