Recently, one of us (Greg Yates) asked a group of university students if they knew how to change a flat tyre on their motor cars. Almost half the group raised an arm. When asked if anyone had ever changed a tyre, no arms were raised.
You may know how to change a flat tyre in verbal terms. But if you have never done this before, you would likely injure yourself in the attempt. In general, procedural knowledge is not subject to verbal instruction, although words can cue a learner to recall procedures mastered earlier.
Procedural knowledge implies a sequence of sub goals. A task is broken down into components where each component is a distinct step to be defined and mastered. This entails a series of if–then contingencies. If the tyre is flat, then move vehicle to a safe area. If you can get to the boot (or trunk), then check the air pressure in the spare wheel. If the pressure is adequate, unbolt the spare wheel.
If the spare wheel can be removed, then locate the car jack. If you locate the jack, then determine the jacking point on the chassis. And so on.
Knowledge of procedures also extends to cognitive skills, as well as physical or motor activity. For example, every student has to master key procedures in mathematics. This is the knowledge of how to achieve defined outcomes such as calculating area, working through steps in a computer program, or knowing how to determine how much stress is being placed on a critical wooden beam within a support structure.
In the early school years, knowing how to read and comprehend text implies appropriate procedural skills. For example, there are different procedures that apply when reading different types of genres, or in knowing when to read at a fast pace, and when to re-read difficult materials. Procedural knowledge allows a student to read strategically, and attune different strategies to different goals.
Researchers may refer to such awareness as conditional knowledge, i.e., the knowledge of which procedure to apply within which context.
How procedural knowledge acquisition takes place. Procedural learning implies hands-on experience where the person responds to actual problems. It is enhanced by social interaction with others to help clarify possible problem-solving strategies. People benefit enormously from opportunities to observe social models demonstrating appropriate skills.
Procedural skills are enhanced substantially by the opportunity to study partially worked examples. Controlled studies have shown that when a learner is provided with worked examples, then providing additional verbal instructions
Learning foundations
(e.g., from a teacher) is not needed. The learner needs high levels of corrective feedback, yes, and sometimes, words help to remind a learner which procedure to use. However, when quietly working your way through procedures what you do not need is an external voice telling you what you need to do next.
In an explicit teaching situation, the detailing of lock-step production systems is often called task analysis, which can be shown as an algorithm involving a series of steps and decisions. Such tools can be partly misleading if seen to imply quick or simple decision-making. The simple truth is that procedural learning (a) is slow and (b) requires much feedback and extended practice. Once mastered, procedural knowledge becomes the basis for action and expertise, and will implicate elements of behavioural chaining and automaticity.
Should the procedural task be one that is essentially manageable, but in danger of overloading a person’s immediate capacity, it can often be taught in reverse order (i.e., to ensure mastery of the final steps first before teaching the beginning). Backward training is often valuable in teaching difficult skills. Over successive trials the need for conscious guidance is reduced, the drain upon memory resources becomes lower, and the activity begins to flow readily rather than being seen as a series of separate but challenging steps.
IN PERSPECTIVE: Ways of knowing and thinking
We recognise the sheer amount of time and effort it takes for the full development of our learning facilities. Mature thinking requires decades of schemata refinement, and we never stop learning. Although we can solve simple problems as young children, the problem-solving skills of the mature adult are of an impressively different character employing complex models inside the head that define just how the world is believed to be constructed.
The present chapter reflects the analysis of knowledge emerging out of cognitive psychology. It is interesting to note how this approach differs from curriculum-based taxonomies developed in the 1950s. Within such taxonomies, knowledge was often portrayed as a basal step in a sequence that might be described as (a) knowledge, (b) comprehension, (c) application, (d) analysis, (e) synthesis, and (f) evaluation. On a logical basis, such taxonomies are useful in devising assessment items (although even this is questioned), but they do not account for how the mind actually works.
It is not valid to say that somehow the mind acquires knowledge, then strives towards its comprehension, then applies it, and so on. Although the taxonomies might supply possible frameworks for instructional planning and for devising assessments, you should be aware that the mental process underpinning learning does not align with such sequences.
A more convincing taxonomy was invented by two Australians, John Biggs and Kevin Collis, which they called the SOLO (structure of observed learning outcomes)
Study guide questions
1 Deciding when the dough mix is just at the right consistency is knowledge requiring sensory recognition. Can you think of any other examples in your life where you have learnt to make sensitive assessments based directly on gradations in sensory information? How can we teach these sensory discriminations?
2 One reality of this modern world is the need to learn multiple strings of information such as your visa number, passwords, or ID numbers. Can you recall any situation where failure to recall a string has occasioned disaster in your life? What are some good ways to commit such strings to memory?
3 However, in terms of your mental organisation, Perth’s standing as a beautiful city actually is not an information string. Instead, it is a proposition or an idea.
What is the difference between a string and an idea?
4 Schemata are more abstract things than ideas. Ideas can be taught quite directly (as facts to acquire). Schemata can be taught, yes, but just why is such resultant learning so much more difficult to achieve?
5 Problem solving results from people becoming able to run strong mental models in their heads. This view says that problems are solved by activating the right knowledge, at the right time, in the right place. How does this mental model view contrast with the view that solving problems depends upon one’s divergent thinking or creativity?
6 Explain just how the notion of ‘evolution’ could be acquired initially as an idea, but later as a schema used to organise coherent knowledge, and finally as a fully developed mental model able to be ‘run’ in the head. Are there any other examples you can think of in your area where simple ideas are progressively built on over many years, to create deeply elaborated models of functioning?
7 One interesting finding is that although verbal cueing certainly helps a person recall which procedure to use, in fact verbal instruction, within itself, does taxonomy. It posits four levels: one idea, many ideas, relate the ideas, and extend the ideas. The first two levels are about surface and the latter two about deeper knowing. This taxonomy highlights the importance of basing deeper knowing on surface information, which is often forgotten when teachers try and teach critical or enquiry learning as a generic tool. Given the premise, that you must have something to think about before you can relate, extend, critique, and enquire, it is perhaps not too surprising that enquiry and critical thinking needs to be embedded in a subject domain. It is tough to teach these thinking skills generically. Indeed, transfer of learning is one of the hardest things to accomplish.
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not help procedural learning (and may even interfere). If that is true, then how does a person actually achieve secure procedural learning? Just what has to take place, and what helps the process?
8 Is it fair to say that knowledge is acquired, which leads to comprehension, then application, and analysis? What is wrong with such logical sequences?
Reference notes
■ Research into learning has been accumulating for 150 years. These two chapters draw on this body of information, as generally found in basic textbooks in the field. However, at a more advanced level, and focusing more on learning in school, the following are high quality resources: Bruning, Schraw, and Norby, 2011; Gagne, Yekovich, and Yekovich, 1993; Pressley and McCormick, 1995.
■ Quotation from David Ausabel, who was a significant figure in introducing the principles of cognitive science into educational psychology research (Ausabel, 1968, p. vi).
■ The work of Dr Elizabeth Loftus on false memory effects (Bernstein & Loftus, 2009). For details on the Innocence Project: http://en.wikipedia.org/wiki/
Innocence_Project.
■ One version of the social brain hypothesis was advanced by British anthropologist Robin Dunbar (Dunbar, 2009, 2010). He drew attention to the role of pair bonding in brain evolution across species. However, primates specifically evolved ever-larger brains to manage their complex social systems.
The typical Homo sapiens has close relationships (or ‘support cliques’) with about five to seven individuals, and can cope daily with perhaps up to around 40. Beyond this, our brain size enables acquaintance relationships with up to 150 individuals. Groups can work well under 150. But over this, things often come unstuck with issues of management, fractionalism, security, and moral discipline. Hence, 150 became known as Dunbar’s number. High school teachers often are expected to manage student numbers well exceeding this figure.
■ The notion that procedural knowledge is acquired through studying models and examples, but barely at all through verbal instruction (Wittwer & Renkl, 2010).
■ The SOLO taxonomy model (Biggs & Collis, 1982).
Consider the following strange calculations. Through counting neural connec -tions, it has been estimated that 11,000,000 signals, or units of information, could be sent to the brain from sensory receptors at any one moment in time. Such is the complexity of the visual system that the eyes alone account for around 10 of the 11 million possible units of information. To function adaptively, we can actively filter out massive amounts of potential input information, to the point where our conscious mind, or our focal attention, might zoom in (just like a camera zoom lens) to allow in about 40 units of information per second. So what happens to the other 10,999,960 informational units potentially available to the mind within such an acutely focused one-second period?
The inevitable answer is that we do not pay attention to the vast bulk of information that could be available. We are highly selective in what we focus upon. We have to be! The vast bulk of information, as apprehended by our senses, simply has no effect on our conscious mind because of this remarkable capacity for selective attention. With our minds we can focus on minute details, and shut out all other inputs.
While selectivity is well recognised, making such an observation itself leads onto another curious issue. Is it the case that more input information is transferred into the brain’s systems than the conscious mind itself actually knows about? To what extent have we really shut out things that apparently are of little interest? This is not a trivial issue. Can we learn without actually knowing that we are learning?
There are no definitive answers to such questions. However, given the current state of knowledge, the only sensible response to such queries is, ‘Yes, your brain does take in considerably more information than can be indexed by the immediate content of your conscious mind.’ An experience may still affect you, and result in learning, even though you did not intend to learn, and you C H A P T E R