Self Regulation has its foundations in cybernetic control theory. There are a large number of variants on the idea, but core features are that:
• Processes are purposive
• Self-corrective adjustments are made to adhere to the purpose
• The corrective adjustments originate within the person
The basic metaphor is of feedback control with four elements:
• Input function
• Reference standard (defined by the purpose)
• Comparator
• Output function
Behaviour is a process of moving towards goals, or away from anti-goals, via feedback control.
Self Direction. So far I have come across no definitions in the psychology literature (although of course there are definitions in other literature). For linguistic reasons, though, I would take it as differing from self regulation in two fundamental ways:
• The purposes or goals are set by the self (in self regulation they may, but need not necessarily, be set by others)
• No specific mechanism for processing towards the purpose is proposed
I take it as similar to self regulation in
• The primacy of goal setting (both in importance and chronologically)
The cybernetic control metaphor underlying self regulation and (perhaps) self direction is well defined and has clear experimental implications when considering one goal, or factor, at a time. On this view a successful autonomous learner would have the ability to plan an efficient route to their goals, and have effective feedback mechanisms to keep them on the route. The focus of research would be on planning competencies and feedback mechanisms.
However, it seems clear that the complexity of the cybernetic control metaphor increases rapidly (?exponentially) as the number of interacting goals increases, and rapidly becomes unmanageable, especially if the algorithm connecting the goals is undefined.
Self Organisation has its origins in dynamical systems theory. One aspect of dynamical systems theory is the mathematical theory of complex systems of interacting factors. Key ideas are:
• Emergent states: the form and properties of the whole cannot be predicted from the component parts but arises from the interactions of those parts and without top-down purpose eg. snowflakes – each has a different form, and they cannot be predicted from looking at an individual water molecule – they arise from the interactions between water molecules without anyone/thing/top molecule organising all the rest. They are self-organised.
• Phase transitions: states are stable against small perturbations, but a slightly larger perturbation in one factor may trigger a change to a completely different (emergent) state of the system. For example a snowflake at -5C is stable against changes in temperature of up to 5, but as soon as the temperature rises even slightly more than this, the flake suddenly melts into water. For some systems an infinite number of phase transitions or states is possible.
• Attractors and repellers: Some configurations of the system are stable against small perturbations – these are attractors. If it is near such a configuration, the system will self organise into such a stable state. Similarly, some configurations will never be adopted and the system will not spend any time here – these are repellers. If it is near such a repeller, the system will self organise so as to move away from this state. In between are configurations that are not stable, but that the system may move through. For example, a pencil lying on its side is in an attractor. A sharp pencil will not stand on its point (repeller), but it will move through a number of leaning positions as it falls on to its side.
Such ideas have been applied to subjects from maths, physics, chemistry, to the stock exchange, social systems and psychology. From a psychology/learning viewpoint, self organisation seems a candidate metaphor for systems where there are:
• Multiple interacting factors
• Changing situations
• Emergent states
• Lack of purpose
• Lack of predictability
Carver (2004) views attractors and repellers as analogous to the goals and anti-goals of self regulation. To be successful as a self organised learner, it seems to me crucial that the attractors are educationally desirable outcomes, while the repellers are undesirable outcomes. In practice most learners are surrounded by undesirable as well as desirable stable states (truancy, addictions, low level computer games, etc). Hence, perhaps one thing that characterises successful learners is their ability to convert undesirable attractors into repellers. Another characteristic would be their ability rapidly to find and configure themselves around desirable attractors: the learner will move into a state where they can “do this”. We could, for example, model this by thinking about an individual moving through different configurations defined by cognitive, affective and social parameters. It seems to me that, assuming that at first they try different configuration solutions by sampling, there might be several ways in which they might locate and move towards the attractor more rapidly than another learner. They might sample more rapidly – eg. switch attention rapidly from one possible outcome to another. They might sample at a greater distance from their current configuration – eg. by greater cognitive leaps. They might have a greater number of sampling points – eg. by a bigger social network (although note that they might also have to switch attention more rapidly to take advantage of this). They might have a stronger pull toward the attractor once found – eg. by greater interest, recognition or reward. There is likely to be an element of randomness in all these processes.
Putting it another way, one might posit, for example, that successful learners can synthesise a large number of information/skill sources, keep a broad overview of multiple possible outcomes, are sensitive to the proximity of a stable state, and able to strengthen the connections that will move them into that state if it seems a desirable one.
Carver suggests that individuals perform both self regulation and self organisation, and that different parts of the brain may be responsible. He suggests that self organisation is the appropriate process in new and undefined situations, but that as a pattern of behaviour emerges and is recreated, it becomes familiar and defined and self regulation takes over. He cites evidence that different parts of the brain are involved in learning a new skill from repeating an established one.
On a larger scale, human systems are capable of both self organised, and regulated behaviours. Liu et al (2005) draw an analogy to patterns of traffic on a motorway composed of all the cars which are more or less autonomous within the broad constraints set by the environment. These represent a self organised system. They differ, for instance, from the very rehearsed and regulated patterns of an american football team
It seems to me that in the CA we are studying two different scales, and it is worth distinguishing between the two:
• Organisational scale. Here the organisation (HE institution or commercial company) is the system, and the people within it are the entities of which the system is comprised.
• Individual scale. Here the individual is the system, and its cognitive, affective, social predispositions are the entities of which the system is comprised
In the self organisation/complex systems approach, the description “autonomy” is applied to the entities of which the system is comprised. At individual scale, then, it does not make sense to talk of an “autonomous learner” because “learner” refers to the macroscopic behaviour of the system (the individual), while “autonomy” refers to the microscopic entities (cognitive predispositions etc) of which the individual is composed. It would make sense though, to describe the behaviour of a company, as a system, comprised of autonomous learners as entities. For example the emergent, self organised, state of a company comprised of autonomous learners might be a learning organisation (if the company was lucky:-))
It would, though, make sense to talk of an individual as a self organised learner. In this case the entities of which the individual is composed, have organised themselves into a behaviour which we would describe as “learner”.
At individual scale, then, one might look as self organised learners, their abilities and behaviours. Alternatively, at organisational scale one might look at the types of institutional emergent state that arises from different behaviours and interactions of individuals.
Summary of differences between self regulation and self organisation
| Self regulated | Self organised |
| Purpose | No purpose (maybe |
| Top down control | Bottom up organisation |
| Autonomy characterises the unit | Autonomy characterises the constituent entities |
| Clearly defined goals | No goals or poorly defined goals |
| Best when dealing with a single goal | Can accommodate multiple interacting factors |
| Constant environments | Changing environments (maybe) |
| Predictable outcomes | Unpredictable outcomes (maybe) |
Key references:
Carver, C. S. (2004) “Self-Regulation of Action and Affect” in Baumeister, R and Vohs, K (eds) Handbook of Self-Regulation, New York: Guildford Press, pp13-39
Carver, C.S. and Scheier, M.F. (2002) “Control processes and self-organization as complementary principles underlying behaviour” Personality and Social Psychology Review, 6, 304-315
Liu, J., Jin, X. & Tsui, K.C., 2005. Autonomy-Oriented Computing (AOC): Formulating Computational Systems with Autonomous Components. IEEE Transactions on systems, man, and cybernetics - part A: systems and humans, 35(6), 879-903.
Hooker, C. & Skewes, J., 2007. Abstract of “Complex Systems Dynamics: Implications for Sustainability, conception and policy.” To appear in Gabbay, D., Thagard, P., and Woods, J. (eds) Handbook of the Philosophy of Science. Elsevier. Available at: http://www.johnwoods.ca/HPS/Hooker_Skewes_abstract_v2B%20F.pdf [Accessed August 1, 2008].
