In this regard, cognitive psychology has produced a variety of methods that can be sources of a new set of measurement methodologies. During the past 2 years, we have been applying these techniques in developing a cognitive task analysis procedure for technical occupations in the Air Force. The conclusions in this paper are based on our experience in this project.
The three sections that follow present a cognitive account of the components of skill, discuss the specific measurement procedures we have employed, and then consider which aspects of measurement in the Services can best use these approaches. The contents of technical skills: the procedures of which they are composed. The context in which technical skills are exercised: the declarative knowledge needed to assure that skill is applied appropriately and with successful effect. The mental models or intermediate representations that serve as an interface between procedural and declarative knowledge.
These three essential aspects of job proficiency are emphasized throughout this paper.
A starting point for specifying the procedural content of a task is the GOMS model proposed by Card et al. This model has been further elaborated in work on formal procedures for representing the complexity of machine interfaces for users Kieras and Polson, The GOMS model splits technical knowledge into four components: goals, operators, methods, and selection rules. We have adopted a similar split with a few differences from GOMS.
Each component is not only a subset of the total knowledge a technical expert must have, but is also a reminder to consider certain issues in attempting to understand the expertise. Any task can be represented as a hierarchy of subgoals, and experts usually think of tasks this way. Generally, such goal structures become. The overall goal is decomposed into several nearly independent subgoals, and they in turn are subdivided repeatedly.
In many cases, the particular subdivision of a goal depends on tests that are performed and the decisions that are made as part of the procedure that accomplishes the goal. Something like the following example 1 is quite common:. Because of this sort of contingent branching, an overall subgoal structure for a particular goal may not exist explicitly. Rather, it may be assembled as the goal is being achieved—it is implicit in part. Without substantial prior experience, it can be difficult to separate a complex task into subgoals that are readily achieved in a coherent manner and that do not interact.
A novice, even if intelligent, may separate a task into pieces that cannot be done independently. Consider the following example of a goal structure:. Suppose that a person adopts this goal structure. Some problems could develop. For example, he may have a budget limit. We say the subgoals interact. Also, it is possible that a package deal can solve all three initial subgoals, so subdividing them as shown is unnatural and may divert the novice from a successful approach.
There is another form of novice goal setting that is almost the opposite. This is the use of subgoals that are defined by the methods the novice knows rather than the overall goal to be achieved. Again, this is often a case of intelligent behavior by those not completely trained. UPS [uninterruptible power source] without the batteries. However, he knew nothing about how, in general, clean power can economically be provided. By looking only at designs for UPS systems, he missed some cost-effective designs that work fine except when battery back-up is also required. In analyzing a technical specialty, it is necessary to establish what goal structures are held by experts.
Sessions in which the expert describes how a task is carried out are very helpful for this purpose, and we have made heavy use of them. Expert-novice comparisons are also helpful. Also, information about novice goal structures can sometimes reveal training problems that could be corrected by specifically targeted instruction. Card et al.
Cognitive Science (Stanford Encyclopedia of Philosophy)
We have unpacked this idea a bit. For any given technical specialty, there are certain basic capabilities that the novice is assumed to have prior to beginning training.
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For example, one might assume that the ability to use a ruler at least with partial success might be a prerequisite for work as an engine mechanic. Ruler use is hardly a primitive mental operation in any general sense, but with respect to subsequent training in an engine specialty, it could be considered as such. Similarly, the ability to torque a bolt correctly might be thought of as a basic entering ability for new three-level airmen coming to their first operational assignments.
What is important is that every training approach makes these assumptions concerning prerequisites and that some such assumptions are incorrect. Often, for example, a training approach will assume a highly automated capability but will only pretest for the bare presence of that capability. In essence, we are asserting that the components of a skill should be subdivided into two relevant parts: those that are prerequisite to acquisition of a particular level of skill and those that are part of the target level.
What is prerequisite at one level may be a target component at a lower level. In doing a cognitive task analysis, one must examine the performance of successful novices to determine what the real skill prerequisites are and what new procedures are being acquired. Such an analysis must take account not only of nominal capability but also of the speed and efficiency of prerequisite performance capabilities.
Care must be taken to avoid declaring too many skills as prerequisites. A common fault of educational and training systems is to declare many aspects of a skill to be prerequisite and then. At the core of any task analysis is an analysis of the procedures that are carried out in doing the task. This remains the case with cognitive task analyses. A major part of our current work on cognitive task analysis of avionics equipment repair skills is to catalog the procedures that an airman must know in order to perform tasks within the job specialty being studied.
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This is done in a variety of ways and is probably the aspect of cognitive task analysis that is closest to traditional rational task analysis approaches. We examine technical orders, expert and novice descriptions of tasks, and other similar data. While the resulting procedural descriptions are likely to be similar to those achieved by earlier approaches, they are distinguished by the following new components: 1 we are attending explicitly to the enabling conditions, such as conceptual support see below , for successful procedure execution, and 2 separate attention is paid to goal structures and selection rules.
Further, we use a variety of techniques to verify our analyses empirically. Procedural descriptions of cognitive tasks will tend to emphasize the domain-specific aspects of performance. However, it will sometimes be appropriate to include certain self-regulatory skills of a more general character in the analyses.
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- Cognitive psychology and its implications (1980).
It is critical to avoid basing cognitive analyses on the ability of people with strong self-regulatory and other meta-cognitive skills to handle many novel tasks. When a task requires these more general skills in addition to easily trainable specific capabilities, then this should be noted.
Issues in cognitive psychology: implications for professional education.
In general, it can be assumed that a continual supply of trainees with strong self-regulatory skills or other high levels of aptitude cannot be guaranteed, and such skills are not quickly taught. The progress of modern psychology has been marked by a slow movement from concern with stimulus-response mappings to a concern with mappings between mental events including both perceptions and the products of prior mental activity and mental operations or physical actions.
To the extent that they stuck to the earlier methodologies evolved from stimulus-response approaches, trainers knew only that certain physical responses must be tied to certain stimuli. They had only indirect ability to teach by rewarding correct responses and punishing errors. Now, new methodologies are being developed for verifying mappings between internal mental events and mental operations.
While they still have pitfalls for the unwary, they make possible an interpretation of tasks that comes closer to being useful for instruction. In particular, we know that the knowledge of experts is highly procedural. Facts, routines, and job concepts are bound to rules for their application, and to conditions under which this knowledge is useful.
As indicated, the functional knowledge of experts is related strongly to their knowledge of the goal structure of a problem. Experts and novices may be equally competent at recalling small specific items of domain-related information, but proficient people are much better at relating these events in cause-and-effect sequences that reflect the goal structures of task performance and problem solution. When we assume that some training can be accomplished by telling people things, we are, in essence, assuming that what the student really needs to know are procedures and selection rules for deciding when to invoke those procedures.
In conducting a cognitive task analysis, it is important to attend specifically to a trainee's knowledge of the conditions under which specific procedures should be performed. Combined with goal structure knowledge, selection rules are an important part of what is cognitive about cognitive task analyses. In performing cognitive task analyses, it is important to consider how deep or superficial the knowledge and performance are. Many skills have the property that they can be learned either in a relatively rote manner or can be heavily supported by conceptual knowledge. For example, one can perform addition without understanding the nature of the number system, so long as one knows the exact algorithm needed.
Similarly, one can repair electronics hardware without deep electronics knowledge, so long as the diagnostic software that tells one which board to swap can completely handle the fault at hand. However, it appears that the ability to handle unpredicted problems, which is a form of transfer, depends on conceptual support for procedural knowledge. The difficult issue as with automation of skill will be the separation of conceptual knowledge evidenced by experts and quick learners into that which is a mere correlate of their experience and that which is necessary to their experience.
This is still very much an issue of basic research, but one on which some good starting points can be specified. The sections below detail three types of conceptual supporting knowledge to which cognitive task analyses should be sensitive.