Personalised Profiling and Self-Organization as strategies in the formation and support of open m-learning communities

Abstract

Mobile and wireless technologies are globally aware therefore so to do institutions have to think globally. By this is meant not simply making learning objects available to international students, but inventing ways to engage students from any geographical location with these objects in such a way that the outcome is knowledge. This paper explores the applicability of personalised profiling as a means to link students studying similar disciplines to each other, and proposes a self-organising 'living systems' model that aims to overcome present impediments to the creation of sustainable, 'open', m-learning communities. 'Open' m-learning communities are characterized by their ability to self organize and adapt to changing circumstances. Their conceptual framework is systems theoretical, which draws on understandings about the natural world from the biological and physical sciences. Concepts such as 'open structure', 'self organization' and 'living systems', have currency in the discourses of information and computing sciences, where much endeavour is expended in attempting to (i.e., the research fields of artificial life and artificial intelligence). In the biological scientific view, the sole purpose of a living organism is to renew itself by opening itself up to its environment, or to another structure. In natural scientific terms, an organism that is in equilibrium is a dead organism. Living organisms continually maintain themselves in a state far from equilibrium, which is the state of life. The transfer of understandings about the operations of living systems is evident in the approaches of computer game designers and programmers, where 'swarming' and other empathetic behaviours of organisms such as bees, fireflies and even stem cells, provide the basis for the design of software to support massively multi-user on-line gaming. This new knowledge may have applicability in new approaches to m-learning, for example, through learner self-profiling and the automated matching of learner profiles to other learners and learning opportunities. The first step in this process is that of understanding how the specificities of emerging mobile and wireless technologies might facilitate open m_learning and the formation of m-learning communities.