PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Analysis of Interprofessional Collaboration in an Online Learning Environment using Self-Organizing Maps
Ann Russell and Timo Honkela
In: AMKLC'05, International Symposium on Adaptive Models of Knowledge, Language and Cognition, 15 Jun 2005, Espoo, Finland.

Abstract

This exploratory paper uses the Self-Organizing Map (SOM) algorithm to analyze the online collaborative discourses of an interprofessional team (N=23) of hospital workers engaged in an 18-month reflective practice and continuous learning project. Interprofessional practice is an emerging best practice model in health care internationally. However, there is very little research investigating the usefulness of online environments for supporting such practice, or the nature of effective online interprofessional collaboration. Preliminary results demonstrate unique characteristics of the participant group's s interactivity that would otherwise remain unidentified using conventional quantitative methods of discourse analysis. The SOM analysis generated a relational profile of participants' reading and linking activity in an online learning environment that not only captures the emergent dynamics of interprofessional collaboration over time, but also highlights individual differences within and between professional groups. A central conclusion is that there is a high degree of within and between group variability as measured by interprofessional activity patterns. In general, we provide a framework for using the Self-Organizing Map method in the analysis of online interprofessional and socio-cognitive activity.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:1815
Deposited By:Timo Honkela
Deposited On:28 November 2005