E–learning Research: a Deleuzian method for the evaluation of virtual learning environments, 25/05/06, Transversality

May 25, 2006

E–learning Research: a Deleuzian method for the evaluation of virtual learning environments

In what sense are virtual learning environments virtual and environmental? I argue that they really are environments, and as such the experience and behaviour of learners within them is subject to the same kinds of determining, enabling and limiting factors as in any other kind of environment. Furthermore, these learning environments each assemble discontinuous multiplicities (quantifiable indexable actualities) and continuous multiplicities (qualitative and immeasurable virtualities) in varying combinations, with differing results for the learner. Just as some environments may sustain certain types of life, different VLE systems encourage different types of learning behaviour. Each environment has its own regime due to the varying composition of virtual and actual. The Elab VLE (a loosely coupled CMS/Blogs/Forums web architecture), through a deliberate policy of managed diversity, is biased towards facilitating the virtual rather than over-determining the actual. The result is an environment that is at the same time cohesive and open and indeterministic. I consequently argue that it therefore offers an environment more suited to the kind of learners for which it is designed, and that VLEs of the type represented by WebCT are inappropriate for research based higher education.

VLE

A series of questions must be addressed:

What is an environment composed of?
What varies between different environments to create differing environmental regimes?
To what extent is a virtual learning environment (VLE) an environment?
How do VLEs vary, so as to create different regimes?
Are certain such regimes preferable to others?

Environments are composed of filters

Consider the life of a polar bear, out on the Arctic ice flows. A highly attuned brain guides its bodily actions in a constant and inescapable quest for connections. Unlike the human learner (perhaps), its search seems simple and deterministic: its goals are straightforwards, being dominated for the majority of the time with the need to connect with and exploit a source of protein, and on more rare occassions, with the need to mate. What parameters there are in this life are determined by the environment in which it is played out. What form does this determination take? In this case, the behaviour of the bear is largely a process of filtering and being filtered. The environment may seem to be a desert of uniform whiteness, but even in such a simple case the animal must filter out unnecessary inputs and options. Even the act of staying insulated from the cold is a process of filtration: absorbing heat whilst deflecting the cold wind. And in return, the environment filters the bear, along lines of movement, limiting and directing its behaviour. Within a short timescale, the behaviour patterns of individual bears are shaped by their immediate landscape, and the connection with other filtered flows (the flow of pack ice, the flow of prey). On a longer scale, evolution applies filters to the species, whilst ecological interactions change the criteria with which selection exterts its pressure (the interactions of populations of predator and prey).

In this way behaviour and geophysical processes connect through an environment that is the assemblage of all of these interoperating filters over time. This is more or less true of any environment. The physical built environment is well understood in these terms, but also consider how an online application with a variety of user accessible functions filters a pool of back–end functionality. In return, the end user filters out irrelevant or inaccessible functionality. Over time these functions may even die off through lack of interest or the application of an agile development process that in some ways copies evolution.

The case of the polar bear is presented above in a simplified manner. There are many complications and possible combinations of filters that offer other kinds of environmental regime. Reality presents a range of regimes and blends of regimes, many of which occur across widely differing regimes. For example, in his work on the 'extended cognition' model of cognition, Andy Clark proposes that a kind of mangrove effect may well be a significant assemblage in the generation of new ideas. Clark describes how a new mangrove swamp begins by a single plant floating in the water, with extended roots that may catch hold of both food and other plants. This kind speculative drift is another assemblage of filters that may be a viable regime in some environments. For example, in the online world we may use a blog to float a part–formed notion. Effective use of semantic tagging and discovery tools may attract further content to the idea, allowing it to grow. It may also allow it to connect to other complimentary ideas. Over time as the mangrove–idea grows, it attracts further connections and taps into greater pools of energy.

The mangrove is an example of a regime of filtration that the philosophers Deleuze and Guattari have called rhizomatic. There are many other regimes, many of which are diagrammed in their book A Thousand Plateaus. These regimes include arborescent assemblages, which operate by applying rules of exclusion that form hierarchical branches of filtration; a familiar model in both software design and social anthopology. Other regimes of importance include:

Gadgets, or filters that follow a reproducible and generalizable design;
Animal bodies (which are gadget–like, but less portable and generalizable);
Familial structures;
Mythologies;
Geological and sedimentary processes.

We therefore have the first of our tools for understanding the construction of environments: regimes of filtration. As has been demonstrated, the principle applies across environments, and in some cases we see regimes re–occurring in quite different settings.

Environments are composed of networks

A second and relatively rare aspect of some environments can best be understood through the behaviour of a more complex animal: the honey bee.

Karl von Frisch
Waggle dance, plume theory, filters

James Gould Cognitive map
Abstraction

some filters are symbolic or digital, redundancy
codings to promote autopoiesis through symbiosis
simulations to accelerate judgement
dissimulation to exploit weaknesses in the simulations of others
the relation between filters and networks is a matter of ecology

Environments are actual

quantification and index
registers of modification
progress
the actual tends to impose limits on differentiation

Environments are virtual

signal can be quantified, the carrier (the assemblage of filters) is more complex and sensitive – a continuum
movement, freedom, creation
whole system evolves continually
different elements evolve at different speeds
differential speeds provide registers for understanding the different elements