Document Type
Conference Proceeding
Publication Date
2014
Publication Title
Proceedings of the 4th International Conference of Learning Analytics and Knowledge
First Page
217
Abstract
To statistically model large data sets of knowledge processes during asynchronous, online forums, we must address analytic difficulties involving the whole data set (missing data, nested data and the tree structure of online messages), dependent variables (multiple, infrequent, discrete outcomes and similar adjacent messages), and explanatory variables (sequences, indirect effects, false positives, and robustness). Statistical discourse analysis (SDA) addresses all of these issues, as shown in an analysis of 1,330 asynchronous messages written and self-coded by 17 students during a 13-week online educational technology course. The results showed how attributes at multiple levels (individual and message) affected knowledge creation processes. Men were more likely than women to theorize. Asynchronous messages created a micro-sequence context; opinions and asking about purpose preceded new information; anecdotes, opinions, different opinions, elaborating ideas, and asking about purpose or information preceded theorizing. These results show how informal thinking precedes formal thinking and how social metacognition affects knowledge creation.
Last Page
225
Included in
Curriculum and Instruction Commons, Higher Education Commons, Online and Distance Education Commons
Comments
Available at ACM Ditigal Library
http://dl.acm.org/citation.cfm?id=2567580