The outcomes and process of learning - HR Management

Managers responsible for human resource development need to understand the nature of learning and development. This section will, therefore, first examine the outcomes of learning, such as skill, competence and tacit knowledge, and employability, an indirect outcome. It will then look at the process of learning, the various levels of cognitive and other skills, at models of learning, and finally at barriers to learning.

  • The outcomes of learning
  • Skill

These definitions are particularly appropriate to perceptual-motor skills, which involve physical, motor responses to perceived stimuli in the external world. Such skills are needed at every level of an organisation, from the senior manager’s ability to operate a desktop computer to the cleaner’s operation of a floor-scrubbing machine. High levels of such skills are particularly needed to operate complex and expensive technology.

There are many other kinds of skills needed in organisations, such as cognitive, linguistic, social and interpersonal skills, that could also be defined in these terms. However, their complexity suggests that various levels of skill have to be recognised, which is what a later subsection will do in presenting some hierarchies of skills.

  • Competence

Competence – also referred to as competency in the literature – has been defined as The core of the definition is an ability to apply knowledge and skills with understanding to a work activity.Competences are now a major element in the design of training and development in Britain (Cannell et al., 1999), and seem to fit well with what is happening in organisations. Martin (1995: 20) proposes that they are a means of ‘aligning what people can offer – their competencies – against the demands of customers rather than against the illfitting and ill-designed demands of jobs’.

Nevertheless, the notions of competence and competency are still matters of debate: from the confusion suggested by differences between the definitions above (Woodruffe, 1991) to the challenge of postmodern thinking (Brittain and Ryder, 1999). Despite considerable variation in the number of competences being used in competence frameworks (one study suggested between 21 and 30 and 300–400), and often a lack of validation of such frameworks, there is claimed to be a ‘dramatic increase’ in the number of companies using them.

‘Personnel professionals must stop dismissing competencies as fads’ (Walsh, 1998: 15). What needs to be noted at this point is that the concept of competence integratesknowledge and skill that are assessed via performance. This leads on to the distinction between formal knowledge and ‘know-how’, in which tacit knowledge has a significant part to play.

  • ‘Know-how’ and tacit knowledge

‘Knowing how to do something’ is a very different matter from knowing about ‘knowing how to do something’. This truism is captured in the everyday suspicion and disparagement of ‘the ivory tower’: ‘those who can, do; those who can’t, teach’. It is also apparent in the reluctance of British employers to value higher education, evidenced in the small proportion of managers with degrees, documented in the Handy (1987) and Constable and McCormick (1987) reports.

By contrast, ‘can do’ became a buzzword for pragmatic effectiveness in the 1980s. Gardner (1985) makes the distinction between ‘know-how’ and ‘know-that’. For him, ‘know-how’ is the tacit knowledge of how to execute something, whereas ‘know-that’ is the statement of formal thinking (propositional knowledge) about the actual set of procedures involved in the execution:

Tacit knowledge is an essential ingredient of ‘know-how’. Sternberg (1985) recognizes this in his definition of practical intelligence:

The example that he gives is of the tacit knowledge relevant to the management of one’s career. The individual also draws upon tacit knowledge in the fluent performance of perceptual- motor skills, as seen in the definition of skill above; indeed, Myers and Davids (1992) write of ‘tacit skills’. Moreover, as you will have seen earlier, one of the purposes of knowledge management is to capture the tacit knowledge that employees have. This tacit knowledge would appear to be acquired through experience rather than through instruction, and is embedded in the context in which this experience is taking place.

This can be seen in Stage 2 of the model of Dreyfus et al. (see below), in which the learner becomes independent of instruction through the recognition of the contextual elements of the task, and thereafter develops the ability to register and ‘read’ contextual cues. However, unlike the formal knowledge that it accompanies, this tacit knowledge never becomes explicit, although it remains very significant.

Myers and Davids (1992: 47) question whether ‘tacit skills’ can be taught, and identify that they are often transmitted in ‘an environment of intensive practical experience’ and in task performance: ‘We may yet be able to learn much from “sitting next to Nellie”!’ They also note the need to take account of both formal and tacit knowledge in selection. A later section will examine the concept of action learning, which contextualises learning and hence draws upon tacit knowledge.

Traditionally, practical knowledge tends to feature at a lower level in any representation of the social hierarchy of skills, and is thereby institutionalised in lower-level occupations. In discussing the public’s understanding of science, Collins (1993) writes about:

Cooley (1987: 10–13) draws attention to the way in which practical knowledge, craft skill, is devalued in the face of technological progress. This is the starting point for his reflections upon the way ‘ordinary people’ could achieve something extraordinary. He believes that technological systems Myers and Davids (1992: 47) come to a similar conclusion after their discussion of the significance of ‘tacit skills’.

In contrast, it could be argued that knowledge management regards technology, rather than people, as the ‘appendages’. It is clear, however, that organisations need both ‘know-how’ and ‘know-that’: the concept of competence, therefore, as defined above is potentially a significant one for them. However, it can be argued that the institutionalised, transorganisational process of identifying and defining competences has wrenched them from their context and hence from the tacit knowledge that contributes so significantly to them.

  • Employability

An indirect outcome of learning and development is ‘employability’, a notion that became current because of the proliferation of flexible contracts of employment and insecurity in employment during the 1990s. According to Kanter (1989a), employability is the ‘new security’: if individuals have acquired and maintained their employability then, should their job come to an end, they would be able to find employment elsewhere.

Employability results from investment in the human capital of skills and reputation. This means that individuals must engage in continuous learning and development, update their skills and acquire others that might be needed in the future by their current or future employer (Fonda and Guile, 1999). It is also argued that, as part of the ‘new deal’ in employment, good employers will ensure that their employees remain employable (Herriot and Pemberton, 1995) by keeping them up to date through training and development.

  • The process of learning

The chapter started by identifying the significance of learning for today’s organisations. It will now first consider theories of the process of learning and of elements within it, and then examine that process in terms of various levels, stages, and cyclical models of learning. This is a very rich and complex field, to which justice cannot be done here, and you are recommended to read a text such as Atkinson et al. (1993) or Ribeaux and Poppleton (1978).

  • Theories of the process of learning

Behaviourist approach to learning

The behaviourist approach has been one of the most influential in the field of psychology. It proposes that learning is the process by which a particular stimulus (S), repeatedly associated with, or conditioned by, desirable or undesirable experiences, comes to evoke a particular response (R). This conditioning can be of two kinds. Classical conditioning occurs when a stimulus leads automatically to a response. Dogs, for example, salivate at the presentation of food; Pavlov demonstrated that they could also be conditioned to salivate at the sound of a bell rung before food is presented..

Operant conditioning (Skinner) takes place after a desired response, which is then reinforced, or rewarded, to increase the probability of the repetition of the same response when the stimulus recurs.

There has been much experimental research (including many animal studies) into such issues as the nature of the reinforcement (negative reinforcement, or punishment, is not as effective for learning as positive reward); the schedule of reinforcement (whether at fixed or variable intervals: intermittent reinforcement is more effective than continuous reinforcement).

This form of conditioning is also used to shape behaviour: that is, to continue to reinforce responses that approximate to the desired behaviour until that behaviour is finally achieved. We arefamiliar with this kind of approach to the encouragement of simple behaviours: we use it with small children, with animals, and in basic forms of training.

Cognitive learning theory

The S–R approach pays no attention to the cognitive processes whereby the stimulus comes to be associated with a particular response: it does not investigate what is in the‘black box’. Cognitive learning theory, however, offers a more complex understanding of learning, proposing, again originally on the basis of animal studies, that what is learned is not an association of stimulus with response (S–R), but of stimulus with stimulus (S–S).

The learner develops expectations that stimuli are linked; the result is a cognitive ‘map’ or latent learning. Hence insightful behaviour appropriate to a situation takes place without the strengthening association of S–R bonds. Social learning theory also addresses what is in the ‘black box’. It recognises the role in learning of the observation and imitation of the behaviour of others, but as seen in, say, the debates over the influence of the media upon young people’s behaviour, there are clearly many moderating variables.

Information-processing approach to learning

This approach regards learning as an information-processing system in which a signal, containing information, is transmitted along a communication channel of limited capacity and subject to interference and ‘noise’ (Stammers and Patrick, 1975). The signal has to be decoded before it can be received, and then encoded to pass it on. In learning, data received through the senses are filtered, recognised and decoded through the interpretative process of perception; this information is then translated into action through the selection of appropriate responses.

The effectiveness of learning depends on attention being paid only to the relevant parts of the stimuli, the rapid selection of appropriate responses, the efficient performance of them, and the feeding back of information about their effects into the system. Overload or breakdown of the system can occur at any of these stages. Gagné (1974, in Fontana, 1981: 73) expresses this as a chain of events, some internal and others external to the learner.

It begins with the learner’s readiness to receive information (motivation or expectancy), and continues as the learner perceives it, distinguishes it from other stimuli, makes sense of it and relates it to what is already known. The information is then stored in short- or long-term memory. Thereafter it can be retrieved from memory, generalised to, and put into practice in, new situations. Its final phase is feedback from knowledge of the results obtained from this practice.

Those concerned to facilitate learning in others can use their understanding of this chain to prevent failure to learn, which can take place at any one of these levels.

  • Elements in the process of learning

This subsection will deal briefly with other important elements in the process of learning that need to be taken into account when designing or facilitating learning.

Feedback (or knowledge of results)

The feedback to learners of the results of their performance is recognised as essential to their effective learning. This is discussed in Ribeaux and Poppleton (1978) and Stammers and Patrick (1975). Feedback will be either intrinsic or extrinsic (or augmented). Learners receive visual or kinaesthetic feedback (intrinsic) from their responses to stimuli in the learning situation; they need to be encouraged to ‘listen’ to such bodily cues in order to improve performance.

They may also receive feedback (extrinsic, augmented) from an external source while they are performing (concurrent feedback) or after it (terminal). Learners may also benefit from guidance given before their performance about what to look out for during it. The sources cited above set out the characteristics, advantages and disadvantages of these different kinds of feedback. The notion of feedback is frequently discussed in terms of learning perceptual-motor or similar skills.

It is also of considerable importance in the learning of the higher-order skills discussed in this chapter, but here it is very complex in nature, and difficult for the learner to be aware and make sense of it. However, by reflecting and engaging in the whole-loop learning discussed below, the learner will have opportunity to pay attention to both intrinsic and extrinsic feedback.

The choice of whole or part learning

Psychologists continue to debate the appropriateness of whole or part learning in learning to perform various tasks: that is, whether the task is learned as a whole, or in parts. Ribeaux and Poppleton (1978: 61) report on one approach that classifies tasks according to their ‘complexity’ (the difficulty of the component subtasks) and ‘organisation’ (the degree to which they are interrelated).

Where complexity and organisation are both high, whole methods appear superior; where either is low, part methods are superior in most cases; when both are low, part and whole methods are equally successful. Stammers and Patrick (1975: 85–88), however, report on research that appears to draw opposite conclusions: where the elements of a task are highly independent the task is best learned as a whole, but where they are interdependent, they should be learned in parts.

It tends to be the whole method in operation when learning takes place during the performance of a job, through action learning, or through observing others.

The role of memory in learning

Memory plays a significant role in learning, and some understanding of it can therefore be used to make learning more effective. Once again, it is not possible to do more than present an outline here, but texts such as Stammers and Patrick (1975), Ribeaux and Poppleton (1978), Fontana (1981) and Atkinson et al. (1993) give further information. Memory involves three kinds of information storage: the storage of sensory memories, short-term or primary memory, and long-term or secondary memory.

Unless transferred to short term memory, the sensory memory retains sense data for probably less than two seconds. Unless incoming information is paid particular attention or rehearsed, short-term memory holds it for up to 30 seconds and appears to have limited capacity, whereas long-term memory appears to have unlimited capacity and to hold information for years. What is therefore of concern for effective learning is the ability to transfer information to the long-term memory.

There are two aspects to such transfer. The first is ‘rehearsal’ – that is, paying attention to and repeating the information until it is coded and enters the long-term store; it is otherwise displaced by new incoming information. The second aspect of the transfer of information to long-term memory is coding: the translation of information into the codes that enable it to be ‘filed’ into the memory’s ‘filing system’. Information is largely coded according to meaning (a semantic code) or through visual images, but sometimes (where the meaning itself is unclear) according to sound.

The ability to retrieve information from long-term memory depends in part upon how effectively it has been organised (‘filed’) in storage (for example, words may be stored according to sound and meaning), but also upon having the most appropriate retrieval cue. We experience this when we are searching for something that we have lost: we think systematically through what we were doing when we believe we last used the lost object.

Recognition is easier than recall from memory because it follows the presentation of clear retrieval cues. Difficulty in retrieving information, or forgetting, occurs for several reasons apart from those concerning the degree of organisation in storage. Interference from other information can disrupt long-term as well as short-term memory (where new items displace existing items in the limited capacity).

Interference may be retroactive, when new information interferes with the recall of older material, or proactive, when earlier learning seems to inhibit the recall of later information. Forgetting also takes place through anxiety or unhappy associations with the material to be learned, which might become repressed. Unhappy childhood experiences, for example, might be repressed for many years.

Finally, memory does not just operate as a camera recording what is experienced: it is an active and a constructive process. This is particularly so when learning the kind of complex material that constitutes the world of organisations and human resource management. As well as recording its data inputs, the process of memory draws inferences from the data and so elaborates upon them, filtering them through the individual’s stereotypes, mindset and world-view.

What is then stored is this enhanced and repackaged material. An understanding of the nature of memory suggests various ways in which it might be improved to make learning more effective. The transfer of new information to longterm memory is clearly crucial: attention, recitation, repetition and constant revision (known as overlearning) are needed.

The coding and organisation of material to be stored are also important: this is helped by associating the new information with what is already familiar, especially using visual imagery, by attending to the context giving rise to the information to be learned, and by making the effort to understand the information so that it can be stored in the appropriate ‘files’. Facilitators of learning need to ensure that the learning context or event does not provoke anxiety.

  • Levels of learning

An earlier section concluded that today’s organisations need their employees generally, and their managers in particular, to practise higher-order thinking skills. This implies that there are several types and levels of skill. This subsection presents several classifications, some of different types of skill, others of different levels or stages. Some are couched in terms of stages rather than levels: the individual can progress from the lower to the higher stages, but does not necessarily do so.

The lower levels are prerequisites for, and subsumed by, the higher. (See the section on development for a discussion of the concept of stages.) Organisations require several types and levels of skills, not only the higher-level thinking skills. The human resource manager can therefore use these classifications, first to identify the prior learning that needs to take place before skills of various levels can be attained, and then to plan ways of facilitating the learning of such skills.

Fitts’s stages of skills acquisition

Fitts (1962, in Stammers and Patrick, 1975) distinguished three stages of learning, in particular of perceptual-motor skills acquisition. It is recognised that they may overlap.

  • Cognitive stage. The learner has to understand what is required, its rules and concepts, and how to achieve it.
  • Associative stage. The learner has to establish through practice the stimulus–response links, the correct patterns of behaviour, gradually eliminating errors.
  • Autonomous stage. The learner refines the motor patterns of behaviour until external sources of information become redundant and the capacity simultaneously to perform secondary tasks increases.

Dreyfus et al.’s stage model of skills acquisition

Dreyfus et al. (1986, in Cooley, 1987: 13–15, and Quinn et al., 1990: 314–315) set out a more elaborate model of the acquisition of skills that is relevant to understanding the development of cognitive skills. Their five-stage model moves from the effective performance of lower- to higher-order skills.

  • Stage 1: the novice. Novices follow context-free rules, with relevant components of the situation defined for them: hence they lack any coherent sense of the overall task.
  • Stage 2: the advanced beginner. Through their practical experience in concrete situations learners begin to recognise the contextual elements of their task. They begin to perceive similarities between new and previous experiences.
  • Stage 3: competent. They begin to recognise a wider range of cues, and become able to select and focus upon the most important of them. Their reliance upon rules lessens; they experiment and go beyond the rules, using trial and error.
  • Stage 4: proficient. Those who arrive at this stage achieve the unconscious, fluid, effortless performance referred to in the definitions of skill given earlier. They still think analytically, but can now ‘read’ the evolving situation, picking up new cues and becoming aware of emerging patterns; they have an involved, intuitive and holistic grasp of the situation.
  • Stage 5: expert. At this stage, according to Cooley (1987), ‘Highly experienced people seem to be able to recognise whole scenarios without decomposing them into elements or separate features’.

They have ‘multidimensional maps of the territory’; they ‘frame and reframe strategies as they read changing cues’ (Quinn et al., 1990: 315). With this intuitive understanding of the implications of a situation, they can cope with uncertainty and unforeseen situations. Managers’ levels of learning (Burgoyne and Hodgson)

A similar hierarchy has been proposed specifically for the learning of managers. Burgoyne and Hodgson (1983) suggest that managers have a gradual build-up of experience created out of specific learning incidents, internalise this experience, and use it, both consciously and unconsciously, to guide their future action and decision-making. They identify three levels of this learning process:

  • Level 1 learning, which occurs when managers simply take in some factual information or data that is immediately relevant but does not change their views of the world.
  • Level 2 learning, which occurs at an unconscious or tacit level. Managers gradually build up a body of personal ‘case law’ that enables them to deal with future events.
  • Level 3 learning, when managers consciously reflect on their conception of the world, how it is formed, and how they might change it.

Perry’s continuum of intellectual and ethical development

Perry’s (1968) schema (see Daloz, 1986) emerged from his research into his students’ experiences. He interpreted their intellectual and ethical development as a continuum, and mapped out the way in which individuals develop multiple perspectives while at the same time becoming able to commit themselves to their own personal interpretation. At one extreme is basic dualism, where everything is seen as good or bad.

This moves through the perception of the diversity of opinion; of extensive legitimate uncertainty; through perception that all knowledge and values are contextual and relativistic; to the recognition of the need to make a commitment to a viewpoint; the making of the commitment; experiencing its implications; and, finally, to the affirmation of identity as this commitment is expressed through lifestyle.

Bloom et al.’s taxonomy of cognitive skills

Bloom et al. (1956) offer a classification, rather than a hierarchy, of skills:

  • knowledge (simple knowledge of facts, of terms, of theories, etc.);
  • comprehension (an understanding of the meaning of this knowledge);
  • application (the ability to apply this knowledge and comprehension in new concrete situations);
  • analysis (the ability to break the material down into its constituent parts and to see the relationship between them);
  • synthesis (the ability to reassemble these parts into a new and meaningful relationship, thus forming a new whole);
  • evaluation (the ability to judge the value of material using explicit and coherent criteria, either of one’s own devising or derived from the work of others) (Fontana, 1981: 71).

Single- and double-loop learning

Another useful classification of learning is found in the concept of two different types of learning: single- and double-loop learning. Individuals do not necessarily progress from single- to double-loop learning, nor is the former an essential prerequisite for the latter. Single-loop learning refers to the detection and correction of deviances in performance from established (organisational or other) norms, whereas double-loop learning (Argyris and Schön, 1978) refers to the questioning of those very norms that define effective performance. Learning how to learn calls for double-loop learning.

Gagné’s classification of learning

Gagné (1970, in Stammers and Patrick, 1975) studied both the process of learning and the most effective modes of instruction, and has made several classifications of types of learning. For example, he identified the ability to make a general response to a signal; to develop a chain of two or more stimulus–response links, including verbal chains and associations; to make different responses to similar though different stimuli; to achieve concept learning and identify a class of objects or events; to learn rules through the acquisition of a chain of two or more concepts; and, finally, to combine rules and so achieve problem-solving.

Gagné’s classification allows us to identify the processes whereby skills of all levels are acquired, and hence suggests how to facilitate learning and prevent failure to learn at the various levels.

  • Cyclical models of learning and learning styles

Another, and more dynamic, way of conceptualising the learning process is to see it as cyclical. The process has different identifiable phases, and individual learners have preferred learning styles. If methods appropriate to the various phases and individual styles are used, more effective learning will result. (The assumptions about phases echo those underlying the concept of development, which is to be discussed in the next section.)

The various models below offer a number of important insights to the human resource manager concerned to facilitate higher-order skills in the organisation. They draw attention to the significance of learning through action and reflection, as well as through the traditional channels of teaching/learning. They recognise that individuals might prefer different phases of the cycle and have different styles: they offer means to identify those preferences; to engage in dialogue with individuals about their preferences; and to identify means of helping individuals to complete the whole cycle.

Kolb’s learning cycle

The best-known learning cycle in the field in which we are interested is that of Kolb. There are two dimensions to learning (Kolb et al., 1984): concrete/abstract (involvement/ detachment) and active/reflective (actor/observer). Learning is an integrated cognitive and affective process moving in a cyclical manner from concrete experience (CE) through reflective observation (RO) and abstract conceptualisation (AC) to active experimentation (AE) and so on (Kolb, 1983).

Effective learning calls for learners:

  • to become fully involved in concrete, new experiences (CE);
  • to observe and reflect on these experiences from many perspectives (RO);
  • to use concepts and theories to integrate their observations (AC);
  • to use those theories for decision-making and problem-solving (AE).

However, many people have a preference for a particular phase and so do not complete the cycle: thus they do not learn as effectively or as comprehensively as they could. Kolb’s Learning Styles Inventory identifies these preferences (Mumford, 1988: 27). The ‘converger’ (AC and AE) prefers the practical and specific; the ‘diverger’ (CE and RO) looks from different points of view and observes rather than acts; the ‘assimilator’ (AC and RO) is comfortable with concepts and abstract ideas; and the ‘accommodator’ (CE and AE) prefers to learn primarily from doing.

Honey and Mumford’s learning styles

Honey and Mumford (1992) also identify four learning styles, based on the individual’s preference for one element in the learning cycle, and have developed norms based on the results of those who have completed their Learning Styles Questionnaire. Their activistslearn best when they are actively involved in concrete tasks; reflectors learn best through reviewing and reflecting upon what has happened and what they have done; theoristslearn best when they can relate new information to concepts or theory; pragmatistslearn best when they see relevance between new information and real-life issues or problems (Mumford, 1988: 28).

This information can be used to design effective learning events, including, Mumford (2002) reminds us, e-learning. Individuals, too, can use it to build on their strengths and reduce their weaknesses in learning.

The Lancaster cycle of learning

The Lancaster cycle of learning

A cyclical model said to represent ‘all forms of learning including cognitive, skill development and affective, by any process’ (Binsted, 1980: 22) is the Lancaster model. This identifies three different forms of learning: receipt of input/generation of output, discovery and reflection. As Figure shows, they take place in both the inner and outer world of the individual. The receipt of input results from being taught or told information, or reading it in books.

Learners follow the discovery loop (action and feedback) through action and experimentation, opening themselves to the new experiences generated, and becoming aware of the consequences of their actions. They follow the reflection loop (conceptualising and hypothesising) when making sense of the information they receive and the actions they undertake, and when, on the basis of this, theorising about past or future situations.

Each form of learning is cyclical, and the cycles can be linked in various ways (for example, learning in formal classroom settings links the receipt of input with reflection), but in effective learning the learner will complete the overall cycle.

The learning curve

Though not a cyclical model, the learning curve is included here for convenience. The notion of a curve is based on the recognition that there is a relationship between the rate of learning and the passage of time. Managers working on the introduction of a new system, for example, might say ‘we are on a learning curve’.

This curve is often represented as being S-shaped, that is, proficiency in a new skill begins slowly at first, increases steadily over time, and then remains on a plateau. However, since the shape of the curve must clearly depend on the nature and circumstances of the learning, this notion of a learning curve perhaps adds little of value to the understanding of learning.

The Lancaster model of the learning cycle

  • Barriers to learning

Although learning is a natural process, people can experience significant barriers, particularly to their learning in formal settings such as school and work. Some of these barriers can be internal: the learner might have poor learning skills or limited learning styles; poor communications skills; unwillingness to take risks; fear or insecurity. Anxiety and lack of confidence are frequently emphasised as significant impediments to learning. Other barriers can be thrown up by the situation: lack of learning opportunities or of support, inappropriate time or place (see Mumford, 1988).

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