Children learn in a variety of ways

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It seems obvious that although we all learn something at some point in our lives, there are many different methods of going about that learning. Whether or not we can delineate children as being one ‘type’ of learner; whether success in learning is adequately measured in current educational circles; and whether or not our educators can be well served by knowledge of relative success of different learning methods are questions all linked to our main title.

While by no means exhaustive, this essay will cover a range of topics in the learning styles field, namely the purpose of learning and understanding in the primary classroom (using mathematics as an example); the link between assessment and learning; gender issues arising from different approaches to learning; Bruner’s extensions to Piagetian thought on learning; and finally on the visual, auditory and kinaesthetic learning styles. It will attempt to address the notion of the differences in success of children’s learning behaviour with analysis of articles from an eclectic range of sources.

We all know what happens if we are taught something using an inappropriate method. Driving instructors (hopefully) blend the theory of perfecting a parallel parking manoeuvre with its practical application, and rightly insist on both methods of learning to ensure success. A piano teacher may stop a pupil who plays a wrong note in a scale of D minor, and then remind them of some justification as to why pressing certain keys over others is correct.

These are examples of transparent (and chiefly practical) learning behaviours that are tantamount to acquiring skills, yet our question at hand should be more explicitly relevant to the established literature. Joyce, Calhoun & Hopkins (1997) make a good summary of the myriad models of learning, summarised further as: 1) The information processing family of models: inductive thinking; concept attainment; scientific enquiry; inquiry training; cognitive growth; advance organisers; mnemonics. ) The social family of models: group interrogation; social inquiry; jurisprudential inquiry; laboratory method; role playing; positive interdependence; structured social inquiry. 3) The personal family of models: non-directive learning; awareness training; classroom; self-actualisation; conceptual systems. 4) The behaviour systems family of models: social learning; mastery learning; programmed learning; simulation; direct teaching; anxiety reduction.

Further to this, despite the exhaustive list we are only scratching the surface of two fundamentals of education – both how to teach for successful learning, and why these different methods can be used so differently among different children. In terms of an overriding aetiology of a predisposed preference for a method of learning, a good starting point would be with studying how children in early education consolidate their grasp of number.

Muijs & Reynolds (2001) discuss the application of early practical problems (such as the perennial favourite of sharing sweets) to the cardinality and ordinality of abstract concepts of number, then linking these back to real life situations, as: “… A lot of students appear to have become disenchanted with maths, and often question the relevance of the large amount of time spent teaching mathematics” (Muijs & Reynolds, 2001: 168)

Their somewhat simplified strategy is to ensure that children are ‘making connections’ to other curriculum areas in their mathematical understanding, thus ensuring a homogeneity of underlying knowledge for the basics of numeracy, regardless of any bias for method in the learners’ minds. Meanwhile, Shaw & Hawes (2001: 1) note that ‘80% of what we understand about how the human brain functions has been learned in the past 10 years’. Is it fair to assume that all this newly acquired knowledge of the brain is reflected in new ideas about learning t6heories?

As an answer to our question of why some learning methods are more successful than others, we can look at these authors’ concept of levels of learning. These are unconscious incompetence, conscious incompetence, conscious competence and unconscious competence. The levels of learning can be seen in the teaching of a literacy lesson on extended writing. From the first scribbles of the pre-school child, where they are unconsciously incompetent in writing, the child then progresses to a conscious incompetence of their skills, realising that they cannot write every word they hear or see.

Then, while you want the child to be consciously competent of their creative writing skills in one sense, they must also maintain an unconscious competence of tasks such as forming letters, spelling and using capital letters and full stops correctly (ibid. : 33). The key to a good learning experience is first to establish the existence of your conscious competence (e. g. ‘I can use exclamation marks properly’), then the existence of your unconscious competence – then to move between the two states by being a reflective meta-analyst of your own skills and abilities.

Ideas such as Shaw & Hawes’ (1998) four levels of learning may not be far from the truth, but they rely heavily on a key notion in education – assessment. The only way we can know whether the competence (whether conscious or unconscious) is correctly assumed is if there is sufficient evidence from some form of assessment. In what can be seen as a historical source, written at a time of the many subsequently titled ‘crises in education’, Galton (1995) discusses the ‘mess that is assessment’.

This is with direct reference to the government’s presumption that the teaching profession is reluctant to raise national standards. This leaves to a ‘catch 22’ situation where complains about standardised tests being no more valuable than informal judgements of children are juxtaposed with a ‘universal’ (i. e. in the government’s eyes) agreement that national standards do need to be improved. The logician looks at this and notices that the assumption that national standards need to improve must have arisen from some assessment in the past – so is it possible this initial assessment was itself flawed?

By examining and accounting for the differences in learning styles among the children of today’s classrooms, we can hopefully highlight prevailing problems in assessment in order to improve the process. Idealistically, the learning process needs to assess its assessment as thoroughly as it should assess children. Returning to learning styles as studied through their teaching methods, Appelbaum’s (chapter in Kincheloe & Steinburg, 1998) ideas on teaching mathematics illustrate the fact that unless we exhaustively explore all strategies for teaching and learning, the methods and possible flaws in assessing their success are irrelevant.

In his writing, he makes distinction between procedural and concept learning, illustrated in a common example – that of vertical addition algorithms. The example in his chapter shows a child with good procedural knowledge, but no grasp of place value, seen in sums like this: (Adapted from Appelbaum’s chapter in Kincheloe & Steinburg, 1998: 200) The pupil has applied her knowledge of the procedure ‘add each number at the top to the number below it’, but has gone awry because of lack of place value concept.

Rephrased, the child has been an unsuccessful learner because she was unable to integrate fully the concept with the procedural algorithm to solve the problem – the ‘problem’ was actually a mere exercise in the procedure. The most logical argument to this (and one taken up by Appelbaum himself) is the commonsense notion that we should not give such technical conceptual terms to a problem in mathematics – e. g. telling a child of 6 that 3 + 4 is the same as 4 + 3 because addition is commutative – but rather expose the child to the concept through numerous learning paradigms.

Tom Lehrer, the renowned Harvard mathematician and entertainer expressed this duality in learning in a satirical way in the introduction to his song ‘New math’: “… In the new approach, as you know, the important thing is to understand what you’re doing – rather than to get the right answer. ” (Tom Lehrer, ‘New math’, 1958, from http://www. lyricsfreak. com/t/tom-lehrer/138395. html) To summarise, the essence of success in learning in this element of mathematics is to acknowledge that perhaps it is the mistake making itself that allows the higher conceptual construct to be integrated, regardless of the child’s learning style.

This essay must focus on the literature surrounding the interaction between learning styles and the reasons for their successes among children. A keyword search for ‘learning styles’ in most library catalogues, search engines or book index will inevitably reference the work of Kolb. Along with numerous collaborators, he developed an inventory for measuring participants’ learning styles, along two dimensions: the abstract conceptualisation – concrete experience continuum; and the reflective observation-active experimentation continuum (Kolb, 1985: cited in Riding & Rayner, 1998).

Although not the first (and certainly not the only) learning styles questionnaire/inventory, its design has influenced many subsequent inventories, all with the purpose of articulating the way we assimilate information and experience. Kolb’s Learning Styles Inventory may have come under continued criticism as to its reliability (see Riding & Rayner, 1998), yet the only outcome of any argument against attempts to label participants as ‘concrete abstract experimental’ or ‘abstract reflective’ learners is this: how else could we effectively glean this information?

Most philosophical and psychological concepts (e. g. personality, aesthetics, memory, and quality) have similar problems to the trouble of defining ‘learning style’ – while it appears obvious we all have a preferred method of learning, striving to measure it is challenging to say the least. Another significant problem in assessing the success of children’s learning styles comes from the empirical domain’s most fundamental flaw – the Heisenberg principle.

Instead of the observed elementary particle being uncontrollably affected by the observation itself, this time it is the method of learning being realised in a pupil being uncontrollably affected by the teacher’s own biases. A dissonance can be set up when a pupil’s preferred learning style is either incorrectly assessed post-hoc by their assessor, or by being presented in a contradictory way pre-hoc. As Vail reminds us: “Since people are multi-faceted, each person has more than one way of learning. But most have dominant clusters, preferred channels, and secondary, subordinate approaches. ” (Vail, 1999: cited in Ginnis, 2002)

This quote arises from a reaffirmation of the purpose of learning styles research – we must assume that patterns and consistencies will be formed across a population of learners that can be generalised for study and improvement. By evaluating the different styles and their impact upon a child’s overall learning curve, then we can get that extra few percent achievement from a primary school class. Nevertheless, a dialogue between the evaluation of styles from an unbiased assessor (who preferably has no obvious dominant learning style), and the assessed party is not helped by the unwillingness to stick to one category of learning on both sides.

In other words, will the learner make any use of the fact they are classified as an ‘enthusiastic’ learner under the Learning Styles Inventory (Kolb, 1984; cited in Riding & Rayner, 1998). Will the assessment method itself only have a vague surface reliability, or can we take the typology of proof of a more complex cognitive system at work? At the bare bones of learning theories, opposition to the Piagetian dominance in primary classrooms has taken various forms. One such idea is a theory of knowledge acquisition, supported by studies on domain specific learning.

In a landmark paper, Chi (1978: cited in Atkinson, Atkinson, Smith & Bem, 1993) showed that 10 year-old chess masters could recall positions of pieces in chess games significantly better than amateurs (college students of 18-21 years) – despite the fact the college students were significantly better at recalling lists of random numbers. This is explained by the 10 year-olds having a deeper grasp of the ‘underlying structures’ of chess, enabling ‘chunking’ of moves (such as ‘black king attacks white bishop’) into larger meaningful units.

In the primary school classroom, this equates to a simple adage – do not expect children to learn successfully unless the information is presented in a meaningful way that lends itself to engagement and purpose. The chess amateurs are more likely to be unaware of the implausible positions of pieces during a game, so irrelevant material is processed when it need not be. When a mathematical problem is presented with irrelevant information (just as in real life, where we seldom think of correcting our multiplication tables before our wallets when purchasing fruit in a market), the problem undoubtedly gets much harder to solve.

By chunking information relating to how number systems work, rather than expecting a distinct qualitative shift to being able to process problems of the Piagetian formal operational stage, our evaluation becomes less rigid and stilted. The assessment that moves a child up to the next rung of a Key Stage scale in primary school usually involves some reaffirmation of the skills that should be latent by this time.

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