Identity and the itinerant online learner

Authors

  • Marguerite Koole Athabasca University

DOI:

https://doi.org/10.19173/irrodl.v15i6.1879

Keywords:

digital identity, relational learning, online interaction,

Abstract

This paper outlines a preliminary study of the kinds of strategies that master students draw upon for interpreting and enacting their identities in online learning environments. Based primarily on the seminal works of Goffman (1959) and Foucault (1988), the Web of Identity Model (Koole, 2009; Koole and Parchoma, 2012) is used as an underlying theoretical framework for this research study. The WoI model suggests that there are five major categories of “dramaturgical” strategies: technical, political, structural, cultural, and personal-agential. In the data collection, five online master of education students participated in semi-structured, online interviews. Phenomenography guided the data collection and analysis resulting in an outcome space for each strategy of the WoI model. The study results indicate that online learners actively employ a variety of strategies in interpreting and enacting their identities. The outcome spaces provide insights into ways in which online learners can manage their identity performances and strategies for ontological re-alignment (reconceptualization of oneself). Further study has the potential to elucidate how learning designers and online instructors might facilitate such identity-work in order to shape productive online environments.

Author Biography

Marguerite Koole, Athabasca University

Marguerite Koole has a PhD in E-Research and Technology Enhanced learning from Lancaster University. Her main areas of interest are digital identity in online learning, and mobile learning. Marguerite is an assistant professor for the Department of Curriculum Studies, College of Education at the University of Saskatchewan.

Published

2014-10-22

How to Cite

Koole, M. (2014). Identity and the itinerant online learner. The International Review of Research in Open and Distributed Learning, 15(6). https://doi.org/10.19173/irrodl.v15i6.1879

Issue

Section

Research Articles