Although more recent brain imaging techniques have shown that the notion of a differentiation of brain functions into left and right halves may be far too simplistic it is still clear that formal education systems have tended to emphasise a rather narrow range of brain capabilities.

Whole-brain learning uses techniques that integrate the synthetic and imaginative brain skills with the analytical and language skills. Simple strategies can make better use of the whole brain and can dramatically improve learning and performance skills.

An understanding of how the brain works most effectively has led to a number of "brain-based" learning principles:

Putting groups together in a room does not mean cooperative learning is taking place. In order to have effective cooperative learning each group member must:

• contribute while also depending on others to accomplish a shared goal or task.
• praise, encourage, support, or assist others.
• take responsibility for their own learning and contribution as well as group achievement.
• develop leadership, decision-making, trust-building and communication skills.
• reflect on group effectiveness and think of ways to improve group work.

Cooperative learning can produce greater student achievement than traditional learning methodologies. Students who work individually often compete against others to gain praise or other forms of rewards and reinforcements. The success of these individuals can mean failures for others. There are more winners in a cooperative team because all members reap from the success of an achievement.

One of the essential elements of cooperative learning is the development of social skills. Students learn to take risks and are praised for their contribution. They are able to see points of view other than their own. Such benefits contribute to the overall satisfaction of learning. Students work with others who may have different learning skills, cultural background, attitudes, and personalities. These differences force them to deal with conflicts and enrich learning.

Systems thinking involves several shifts from mechanistic, reductionistic thinking:

Shift from the parts to the whole.

According to the systems view a living system has essential properties which none of the parts have. They arise from the interactions and relationships between the parts. These properties are destroyed when the system is dissected, either physically or theoretically, into isolated elements.

For example, energy and matter move in cycles through an ecosystem; all substances are continually recycled. The food chains that ecologists originally talked about are really food webs. They are networks, and there are cycles within those networks, which are feedback loops. All these are properties that can only be understood if you observe the whole ecosystem. If you split it into a number of species and make a list of those, you will never discover that there are these cyclical patterns that interconnect them.

Shift from analysis to context.

The shift from the parts to the whole is not easy because we have all been conditioned by our upbringing, our education, to think in terms of parts. The whole enterprise of Western philosophical thought has been mechanistic and reductionist, concentrating on the parts.

The great shock of twentieth century science has been that living systems cannot be understood by this method of analysis. This doesn't mean that we have to give up analysis. It's still very useful in many ways, but it is limited.

In the systems approach, the properties of the parts can be understood only from the organisation of the whole. In order to understand something, you don't take it apart; you put it into a larger context.

Only then will you understand, for example, why a bird has certain colours. If you know something about evolution, you will know how these colours originated and evolved. You will understand the properties within the context of the environment of this animal and within its evolutionary context.

So, systems thinking is 'contextual', and this is the opposite of analytical thinking. Analysis means taking something apart in order to understand it; systems thinking means putting it into the context of a larger whole.

Shift from objects to relationships.

In the l920s physicists discovered that ultimately there are no parts at all. What we call a 'part' is merely a pattern in an inseparable web of relationships. It is of course very useful to define parts, but this definition is often arbitrary and approximate and needs to be flexible.

Therefore, the shift from the parts to the whole can also be seen as a shift from objects to relationships. In the mechanistic view, the world is seen as a collection of objects, and the relationships between them are secondary. In the systems view, we realise that the objects themselves - the organisms in an ecosystem or the people in a community -are networks of relationships, embedded in larger networks. For the systems thinker, the relationships are primary, the objects are secondary.

Shift from hierarchies to networks.

A striking property of living systems is their tendency to form multileveled structures of systems within systems. Let's take our own organism as an example. At the smallest level we have cells, and each cell is a living system. These cells combine to form tissues, the tissues form organs. The whole organism is a network of all these relationships. Then the organism as a whole exists within societal relationships, within social systems, and within ecosystems.

At each level, we have systems that are integrated wholes while at the same time being parts of larger wholes. Throughout the living world, we find living systems within other living systems.

Since the early days of ecology, these multileveled arrangements have been called hierarchies, a misleading term derived from human hierarchies with a fairly rigid structure of domination and control - quite unlike the multileveled order found in nature.

Since living systems at all levels are networks, we must visualise the web of life as living systems (networks) interacting in network fashion with other systems (networks).

In other words, the web of life consists of networks within networks.

Shift from structure to process.

All the systems concepts discussed so far can be seen as different aspects of one great strand of systemic thinking, which we may call contextual thinking. Contextual thinking means thinking in terms of connectedness, context and relationships.

There is another strand in systems thinking that is of equal importance. This second strand is process thinking. In the mechanistic framework of Cartesian science, there are fundamental structures, and then there are forces and mechanisms through which these interact, thus giving rise to processes.

In systems science every structure is seen as the manifestation of underlying processes. Structure and process always go together; they are two sides of the same coin. Systems thinking is always process thinking.