amee guide no. 6 evaluating multimedia applications for medical education
TRANSCRIPT
Medical Teacher, Vol. 17, No, 2, 1995 149
AMEE Medical Education Guide No. 6. Evaluating multimedia applications for medical education
M. J. ATKINS & C. O’HALLORAN, University of Newcastle, UK
Introduction
There is no shortage of conflicting views on the potential of multimedia applica- tions. For some people they seem revolutionary-a new and unique way to design powerful learning experiences which will change forever the inadequacies of conventional teaching. For others they seem so much hype-just another twist in the long road of educational technology that has always promised much and delivered little. Both supporters and sceptics can point to research findings to support their case.
So what is meant by multimedia applications? Here the term will be taken to mean courseware which integrates video, audio and graphical material with text and number operations. CD-ROMs, interactive videodiscs and learning packages produced from authoring software are examples of multimedia applications. The courseware runs on a computer workstation under the control of a program itself designed to make the learner interact with what is shown on the screen. The interaction can be achieved through a mouse, keyboard, joystick, by touching the screen and increasingly by voice command (see Figure 1).
The complexity and sophistication of multimedia applications vary greatly. In their more limited form multimedia applications may simply be text-based tutorials linked to a large database of images. By contrast, in their more elaborate form, multimedia applications can be designed to let users explore an environment as though they were in it, control a simulation of a dynamic system, and participate in apparently real-life situations making decisions and seeing the consequences immediately. Multimedia applications are also beginning to exploit the new telecommunication technologies of networks, fibre-optic cables and satellites. Students can therefore now be linked to teachers or experts who are geographically remote, or to distant databases, and not least to each other for asynchronous peer support in learning.
With this understanding of multimedia applications in place we can now return to the issue of how to evaluate their usefulness in medical education. The issue breaks down into three related questions:
0142-159)(/95/020149-12 0 1995 Journals Oxford Ltd
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.
150 M. J. Atkins & C. O’Halloran
FIG. 1. Interactive technologies.
Can multimedia courseware be designed for the type of knowledge we want medical students and practitioners to acquire or does the fact that it is based on the discipline of computer programming inevitably limit its usefulness? What is the nature of the differences between learning from a multimedia simulation and learning from real cases and events? Are the differences a help or a hindrance in the development of professional expertise? Can multimedia applications make us more aware and critical of our own practice? Or are they such an artificial experience that they have no power to change the way an established professional works?
These questions can be tackled by looking first at the nature of the knowledge that we expect medical professionals to acquire and relating that knowledge to what multimedia applications have to offer.
Second, one can take the current explanations of medical reasoning and of how medical expertise develops and link this analysis to the characteristics and capabil- ities of multimedia applications.
The nature of the knowledge to be acquired
Several writers have attempted to analyse the different types of knowledge that professionals use in their work. Eraut (1 994), for example, argues that profession- als use three types of knowledge: propositional, personal and process knowledge. For the purposes of this article, we will concentrate on just two of his categories: propositional knowledge and process knowledge.
Acquisition of propositional knowledge
This comprises the facts, theories and concepts derived from subjects and disci-
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.
Evaluating multimedia applications 15 I
Learning objectives explicit Assessment criteria explicit ‘Map’ of material Conceptual scaffolding Recalls relevant prior learning Steps from simple to complex Formative and diagnostic feedback Help, hints and advice facility Clear deductive explanations Use of dynamic visuals and graphics Branching and routeing on basis of user test scores
FIG. 2. Characteristics of courseware which facilitate acquisition of propositional howledge
plines. It is codified and made accessible to learners through textbooks and lectures. It grows and changes through research that has been accepted by the academic community. It is the stuff, of course, of the preclinical syllabuses in many medical schools. For example, the General Medical Council (GMC) for the UK in its recent publication Tomorrow’s Doctors (GMC, 1993) puts propositional knowledge at the heart of its new core curriculum:
The student should acquire a knowledge and understanding of health and its promotion, and of disease and its prevention and management in the context of the whole individual and his or her place in the family and in society.
This goal is broken down into knowledge objectives such as “a knowledge and understanding of the sciences basic to medicine” and recurs in the curriculum themes of ‘human biology’, ‘human disease’, ‘the public health’, and ‘man in society’.
Medical students are traditionally required to memorize propositional knowl- edge, replicate it and interpret it. Sometimes, though not very often, they are encouraged to use it to provide a critical perspective on current practices in the profession. Of course we would like students not just to acquire propositional knowledge but also to understand it at a personally meaningful level so that when they treat real patients they actually apply their propositional knowledge-but we know that such transfer is highly problematic and that experts appear not to use their subject knowledge base very much (Pate1 & Groen, 1986).
So can multimedia applications help us to acquire and understand propositional knowledge? Yes, they certainly can. There are plenty of studies which show that courseware is an efficient as well as an effective way to learn this sort of knowledge. There are some useful congruences between the way computer programs work and good instructional design. So one could reasonably expect high quality, well-de- signed courseware to have the characteristics listed in Figure 2 . It should define the learning objectives explicitly for the learners, and give them a good idea of the criteria that will be applied to assess their knowledge.
It should help learners to make sense of the material they are working through by giving them, at the start, some idea or ‘map’ of the ground they are going to cover, and by clarifying any key concepts they will need to use to anchor the sense they make of the material. This is often referred to as providing the necessary mental ‘scaffolding’ for the new knowledge. Similarly, through an early revision
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.
152 M. J. Atkins G. C. O’lialloran
section, it should require the user to recall and rehearse any prior learning to which the new material needs to be related.
A computer program works by linking together small parcels of code into longer and more complex strings or units. So courseware can quite easily be designed to break down difficult topics into small, manageable steps, carefully sequenced so that they lead the learner from the simple to the complex, or from an understand- ing of how separate systems work to an understanding of how they interact with one another.
Multimedia applications should be programmed to provide formative feedback to the student both to motivate and to build up the learner’s understanding of the strengths and weaknesses in his or her knowledge acquisition so far. It can also incorporate summative assessments and gradings which can contribute towards course marks.
It is normal for applications to incorporate features to help the students when they get stuck, and suggestions on ‘study skills’ that will enable the learner to get the most out of the application.
One of the more powerful features of instructional design, the teaching of concepts deductively, is also worth looking for. Multimedia applications should present learners with a clear definition of the new rule, category, principle or concept and then show many examples of that rule to reinforce its acquisition in the mind of the learner. It is even better if the program also shows the learner negative examples of what the category does not cover so that its boundaries are firmly established. If deductive instruction is followed with an exercise using an ‘observe and identify’ format, or a ‘compare and contrast’ format, the acquisition of the concept is more likely to be consolidated. (The visual databases behind multimedia applications are large enough to hold the many examples needed for this approach.)
If the courseware includes knowledge of complex objects then one would expect the application to allow the learner to see a three-dimensional representation of that object and to rotate or manipulate the image. Similarly, with knowledge of systems, one would look for a combination of computer graphics and video to explain how the system functions dynamically. It is very difficult to do this with conventional media and it certainly helps learners if they can visualise the phenom- enon as well as read or hear about its properties.
Finally, one would look for the inclusion of diagnostic tests at certain points in the learning sequence. Performance at these points should then determine how the user is routed through the next stage of the application or indeed back through a remedial loop if that is appropriate. The same approach can be taken with a problem-solving application using the profile of a user’s decisions at certain points as the basis for branching and routeing. (The if-then logic of programming languages is exactly what is needed to achieve this design feature.)
While there are clearly some useful design features which can be incorporated in multimedia courseware to improve the acquisition of propositional knowledge, there are also some limitations which evaluators need to be aware of (see Figure 3).
First, the testing-routeing feature is not truly intelligent. Although different users will be routed in different ways through the material, the underlying program will not react dynamically, on the fly, to the needs of a particular learner. All routeing decisions and feedback messages will have been pre-programmed, and if
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.
Evaluating multimedia applications 1 53
Branching and routeing not genuinely ‘intelligent’ Explanations/expositions not unique to user Predominance of one learning style Lack of differentiation in pace and degree of user control
FIG. 3. Common limitations of courseware for acquisition of propositional knowledge
a particular user’s response has not been anticipated, or if the user has an unforeseen profile of decisions on a simulation, the program will not be able to create a new category of feedback or find a unique route through the material for the learner.
This leads to a deeper point. It is much easier to plan a structure for the material to be covered than to design good interactivity for the learner. By interactivity one does not mean the number of mouse clicks per minute required by the simulation. One means the quality of the dialogue that is possible between the individual learner and the application. Good interactivity requires the designers to model the learner in some way. And modelling a learner is enormously difficult. It means taking into account partially correct knowledge, misconceptions, inadequately differentiated concepts, half-understood theories and deeply held intuitive under- standings of phenomena which happen to be wrong. This is where, of course, good human teachers come into their own precisely because they are able to elicit what a learner knows uniquely, and can differentiate their explanations in individually tailored ways.
The lack of flexibility in the design of a multimedia application can be a hindrance in other ways. For example, sometimes the designers have, without knowing it, favoured one type of learning style or problem-solving strategy in the way that the material has been structured. Learners with a different predominant style may be disadvantaged and will learn sub-optimally. By no means everyone, for example, likes to learn in the step-by-step, serial manner. Some prefer to get a holistic feel for the topic. Then again, if you are an able learner you may get frustrated if you cannot control your own pacing and sequencing through the material. On the other hand, if you are a weak learner you need much more structuring from the program so that you cannot skip over the conceptually difficult sections. It is rare, however, to find an application which starts by diagnosing a user’s preferred learning style, or optimum level of control, and then presents the material on an appropriately differentiated basis.
Now that we have looked at what multimedia applications can and cannot do for acquiring propositional knowledge, we need to examine their role in helping students achieve that deeper level of understanding in which the knowledge becomes personally meaningful and can be applied in practice. Here too course- ware has something to offer and there are features which evaluators should look for (see Figure 4). For example, evaluators should expect to find analogue or digitized video segments which allow students to see in practice what they have just learned as propositional knowledge.
Nascent understanding can also be reinforced if the user is placed in a realistic simulation of practice, based on video segments, and required to make decisions or solve problems, drawing on subject knowledge and integrating it to do so.
Deep learning can be facilitated if the courseware has been designed so that learners are required to generate their own explanations, definitions or categories inductively, through observation and analysis of several cases presented on the
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.
154 M. J. Atkins & C. O’Halloran
Visual demonstration Realistic simulation of practice User-generated explanations Microworlds, interactive models, simulations of systems Learner control of variables Projects, investigations
FIG. 4. Characteristics of courseware facilitating deep understanding.
screen. The learner should then be asked to check hisher understanding against the views of others, including experts, which have been recorded on the database.
Even more powerfully, multimedia applications can be designed as what are called microworlds which model the interaction of variables in a system or situation. In high-quality courseware, learners are given control over the variables, their strength, duration, valency etc., so that they can predict, test and refine their understanding of causal relationships. This is a good way of challenging learners’ misconceptions, forcing them to construct new mental models which better account for the results they see in the microworld simulation.
Finally, it is possible to use multimedia applications as databases and ask students to design appropriate research projects or investigations using particular methodologies to do so. This can teach the student quite a lot about the characteristic ways that a discipline creates new knowledge, its tests for truth, and the way it handles evidence. As an added bonus, exposure to such applications may also allow a student to get some idea of the essential complementarity of the characteristics of human thought-its hunches, intuition, common-sense checks on possible explanations of results and so on-and the characteristics of the powerful computational tools now available on computer platforms-for example the performance of complex and sophisticated statistical calculations, low error rate on repeated operations, or three-dimensional modelling (Macfarlane, 1990).
Once again, though, there are limitations to what multimedia applications can accomplish in facilitating personally meaningful learning (see Figure 5) . Two in particular stand out.
First, because of the underlying structure of computer programming, one often finds that multimedia simulations of ‘real’ practice seem too neat and tidy. There is a profound sense in which they are contrived. Users cannot opt for solutions that have not occurred to the designers; the range of decisions is strictly predetermined. This may limit the extent to which users can transfer their learning from the multimedia application to the ward, clinic or surgery.
The same criticism can sometimes be applied to multimedia applications designed for student research projects. For example, knowledge creation in the sciences is often messy, ambiguous, full of dead-ends and U-turns. Yet pro- grammers seem unable to resist the temptation of designing the application to follow the order of research publications, an order which tends artificially to tidy up the research process.
The second reservation is this. As one would expect from a computer platform,
Contrived, artificial nature of practice simulation ‘Tidying up’ of research process Difficulty in coping with non-algorithmic material
FIG. 5 . Common limitations of courseware for deep understanding.
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.
Evaluating multimedia applications 155
microworlds and simulations work best when the subject-matter has an underlying mathematical or algorithmic base to it. In other words, when the relationship between variables can be precisely described and predicted. This is non-problem- atic for some parts of the medical curriculum. But it is not so good for others. For example, when it comes to modelling the interaction of ethical and moral issues, or historical causation, there are rather few underlying, consistent and therefore programmable ‘rules’.
Acquisition of process knowledge
Based on Eraut’s (1994) work, the following can be seen as part of process knowledge:
knowing how to collect information, data and evidence; knowing how to do things (skilled behaviour); being able to plan, evaluate, make decisions and solve problems; being able to communicate appropriately in different professional contexts.
Multimedia applications can offer some assistance here. There are clearly some particular ways of collecting information in medicine,
such as taking and recording comprehensive case histories which can be assisted by practice on (interactive) video sequences of real and simulated patients. Mean- while the existence of large audio and visual databases can increase the com- petence of users in very specific ways such as listening to heartbeats or observing micro-organisms under magnification. Exposure to multiple examples can help students to develop pattern recognition which in turn should help them in diagnosis of real patients.
Meanwhile real-time footage of events in casualty or in the operating theatre can be replayed many times and under slow motion or frame-by-frame commands to build students’ competence in spotting and noting significant features, changes or events. Training in how to observe should thereby be facilitated.
This brings us to the subcategory of skilled behaviour which would encompass the GMC’s list of basic clinical procedures such as basic and advanced life support and venepuncture. Skilled behaviour is quite difficult to define-not least because experienced practitioners act intuitively, processing much information from the situation they are dealing with at a smart, pre-conscious level. So skilled behaviour is usually associated with complex sequences of action that have become spon- taneous and automatic, beyond the influence of conscious, explicit thought and control (Broadbent, 1993; Boreham, 1994). It is clearly necessary for the prac- titioner to develop this routinized way of working in order to cope with the pressures of professional life, but modelling such behaviour to the novice through a multimedia application may not help the novice that much. There may be intermediate developmental stages through which the novice has to pass which do not allow short cuts. It is, however, worth flagging here that the next generation of multimedia applications-applications which make use of of intelligent man- nequins or of virtual reality-may be able to speed up the process of acquiring basic clinical procedures by giving students concentrated preliminary practice, with high-quality feedback, before they ‘have a go’ on real patients.
That brings us to deliberative thinking: the conscious planning, forward reason- ing, evaluating and decision making which lie at the heart of some at least of a
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.
156 M. J. Atkins & C. O’Halloran
doctor’s work. We know that deliberative thinking does not occur from proposi- tional knowledge alone. It depends on a knowledge of contexts and past cases that is built up from experience (Patel & Groen, 1986; Allen & Bordage, 1987). Indeed, two studies (Patel et al., 1988; Patel et al., 1990) concluded that (propo- sitional) knowledge of the basic sciences and the more practical clinical knowledge form two separate domains in memory with their own individual structures and that the clinical knowledge cannot be embedded into the basic science knowledge structure (although the reverse may be possible). Further, Eraut (1994) among others makes the point that what characterizes deliberative thinking in a profession such as medicine is that there is frequently no one correct answer or a guaranteed road to success: propositional knowledge usually provides only partial help. Problem solving in medicine may not follow a neat, rational and logical pattern and indeed the hypothetico-deductive approach to clinical reasoning has been shown to be a weak model to adopt (Feltovich & Barrows, 1984; Groen & Patel, 1985). More frequently the professional is dealing with uncertainty about out- comes, insufficient information from the context, time constraints, and the requirement to involve others in the decision-making process.
Can multimedia applications help here? As described above, multimedia simula- tions can put the learners into an apparently real situation using video footage, requiring them to make decisions against time constraints and see immediately the consequences of those decisions. The narrative can unfold in different ways according to the decisions made at the branching points. T o this extent multime- dia courseware can be a good practising ground for ‘real-life’ deliberative thinking. It should also be possible to design a simulation that forces users to draw on their basic science knowledge in order to tackle the problem presented. Multimedia courseware may in fact be better able to forge such links systematically and thoroughly than the rather serendipitous experience of on-the-ward teaching.
But the danger, as also identified before, is that because of the demands of programming, the simulation is built on an implicit hypothesis-test-hypothesis- test chain which imposes an artificial dynamic on the learning experience and unfortunately represents the weakest of the models of clinical reasoning. There is often a further irritation in simulations, also caused by the requirements of program branching. It is this. The scenario stops periodically and gives the user a predetermined list of alternative actions to take. However, in real life, part of the skill of the practitioner is precisely in knowing when to intervene and what the realistic choices are. In real life, there is no deus ex machina to say ‘decide now; this is your list of options’.
There is one further major problem with this type of application which impacts on its effectiveness in developing appropriate communication skills: the user is not truly a variable in the scenario that is unfolding. The user interacts through a mouse click, text entry, touch screen or occasionally by voice command. But the program cannot take into account the non-verbal aspects of communication, the way that a patient will react quite differently to medical staff with different personalities, tones of voice, appearance, age, gender, race and so on, let alone to doctors or nurses of different perceived status. Similarly, the courseware can only show fellow professionals reacting to the results of a user’s multiple-choice decision. One can argue that this is a very crude approximation of how communi- cation in a team or group actually occurs and that it does not give a feel for how communication shapes behaviour continuously. So there is not a true modelling in
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.
Evaluating multimedia applications 15 7
Dreyfus & Dreyfus
Novice
Advanced beginner
Competent performer
Proficient
Expert
Schmidt et al.
Elaborated causal networks
Abridged networks and causal models
Illness scripts and case memories
FIG. 6. Two stage models of novice-expert progression.
these simulations of the two-way or multiple dynamics of real interactions on the ward, in the clinic or in the surgery,
Developing the expert
The question here is whether multimedia applications can assist, speed up or improve the process by which the novice moves through stages of increasing competence to something that we would recognize as expertise.
There are various models in the literature which try to pin down what distin- guishes the novice from the expert and explain how the development from one to the other occurs (Feltovitch & Barrows, 1984; Coughlin & Patel, 1986; Dreyfus & Dreyfus, 1986; Schmidt et al., 1990). Some of the models have better empirical support than others. Several suggest that there are distinct stages to the process. All are hampered to some extent by the fact that it is difficult to get inside the mind of the expert performer to see what is actually determining that individual’s behaviour. And since many of hisker apparent decisions will have been taken at the routinized level (i.e. beyond conscious control) it may be difficult for himher to explain having acted in a particular way.
One of the best known stage models (see Figure 6) is that of the brothers Dreyfus. They suggest that there are five developmental stages: novice, advanced beginner, competent, proficient, expert. During the first three stages behaviour follows rules, then guidelines but is under conscious planning. After the competent stage, they argue, a holistic approach emerges in which flexible interpretation of the context takes over from the application of rules or guidelines, and a deliberate, analytical approach is used only when a novel problem presents itself. “When things are going normally”, they say, “experts don’t solve problems and don’t make decisions; they do what normally works.”
A rather different stage model can be found in the work of Schmidt and his co-authors. In their model subject knowledge and analytical thinking also domi- nate the early stages but are gradually supplemented (but not submerged) by causal models based on experience. These causal models are gradually reformed in the light of the contextual factors under which diseases emerge. This increasingly experientially based knowledge finally takes the form of illness ‘scripts’ and case memories, the latter stored vividly in episodic memory and linked to the appropri- ate illness scripts for quick access.
What emerges strongly from their study is the very idiosyncratic nature of expert
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.
158 M. J. Atkins & C. O’Halloran
knowledge and performance, determined as it is by the particular cases that a doctor has encountered in his or her career.
One could argue from these descriptions of stage models that multimedia applications may have considerable use in the early stages-typically for students in medical school-and in the early years on the wards, but that beyond a certain stage, for example the stage of competent performer in the Dreyfus model, their usefulness would decline. Courseware is good for direct instruction of proposi- tional knowledge but that is not appropriate for the proficient or expert performer. In the latter two stages deliberative decision making and problem solving are of decreasing importance and analytical thinking is apparently needed for only a small minority of cases where a significant novel feature is encountered.
So does that limit the role of multimedia applications? The answer will depend on three factors:
First, the extent to which learning from simulated cases is as good at building up experience as learning from real life. If it is comparable, then working on databases of cases may speed up the process of acquiring expertise and could form an important part of Continuing Medical Education. Second, the extent to which the Dreyfuses are right about the absence of propositional knowledge and analytical thinking in expert performance. Eraut (1994) has argued that just because such knowledge is not explicit does not mean that it is not implicated in the apparently spontaneous decisions. And Schmidt et al. (1990) argue that propositional knowledge remains available to the expert and is used when he or she encounters a case for which hisiher previous experience of patients provides no guide. Third, if indeed experts do not rely on formal knowledge, whether this is a state of affairs we are happy about or whether we feel that experts should be more analytical and deliberative in their performance? Intuition, as we know, can be horribly fallible. It is at least possible that multimedia applications could ‘round out’ a doctor’s partial experience of real cases. And just as airline pilots are required regularly to go through refresher training on a flight simulator in which they experience non-routine events and the unforeseen combinations of factors which have led pilots into making mistakes so too, perhaps, professionals in the medical field should be encouraged to spend time on multimedia case simula- tions. These simulations could require them to think analytically as well as intuitively, to review the adequacy of their underlying propositional knowledge, to reflect critically on taken-for-granted practices, to be assessed and given feedback on how they handled non-routine problems. In this way the full continuum of professional expertise might be addressed and not just the intuitive dimension.
Conclusions
Returning to the three questions posed in the introduction we are now in a position to put forward some answers.
(1) The fact that multimedia applications are based on the discipline of computer programming can be a strength in the teaching of propositional knowledge, but imposes some limits on their value in developing process knowledge.
(2) There is a plausible case for using multimedia applications to speed up the
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.
Evaluating multimedia applications 15 9
development of expertise and to balance the over-reliance on case memories as the basis for decision making. But the nature of the relationship between formal knowledge and experientially based knowledge in expert performance is still poorly understood and therefore the guidelines which can be given to courseware developers remain rather tentative.
( 3 ) We still do not know the extent to which there is transfer from the experience of a multimedia simulation to the way that professionals go about their work in real wards, surgeries or operating theatres. Some aspects of current simula- tions are probably sufficiently ‘lifelike’ to result in transfer; others are not. Training on simulations seems to work for pilots but the dynamics of decision making in medial contexts are very different. We need many more stringent studies of transfer, including research on the new generation of virtual reality applications, before investing too heavily in this approach to continuing medical education.
Notes on contributors
MADELEINE J. ATKINS is Head of the Department of Education, University of
C. O’HALLORAN is in the Regional Postgraduate Institute for Medicine and Newcastle, Newcastle-upon-Tyne.
Dentistry at the University of Newcastle, Newcastle-upon-Tyne.
Correspondence: Dr M. J. Atkins, Department of Education, St Thomas Street, Newcastle upon Tyne NE1 7RU, UK. E-mail: [email protected]
REFERENCES
ALLEN, T. & BORDAGE, G . (1987) Diagnostic errors in emergency medicine: a consequence of inadequate knowledge, faulty data interpretation or case type? Annals of Emergency Medicine, 16(4), p. 506.
BOREHAM, N.C. (1994) The dangerous practice of thinking. Medical Education, 28, pp. 172-179. BROADBENT, D. (1993) Planning and opportunism, The Psychologist, 6, pp. 54-60. COUGHLIN, L.D. & PATEL, V.L. (1986) Text comprehension and expertise in the domain of medicine,
cited in: H. G. SCHMIDT, G. R. NORMAN & H. P. A. BOSHUIZEN (1990) A cognitive perspective on medical expertise: theory and implications, Academic Medicine, 65(10), pp. 61 1-621.
DREYFUS, H.L. & DREYFUS, S.E. (1986) Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer (New York, Free Press).
ERAUT, M. (1994) Developing Professional Knowledge and Competence (London, Falmer Press). FELTOVICH, P.J. & BARROWS, H.S. (1984) Issues in generality in medical problem solving, in: H. G.
SCHMIDT & M. L. DE VOLDER (eds) Tutorials in Problem-based Learning: new directions in training for the health professionals (The Netherlands, Van Gorcum) .
GENERAL MEDICAL COUNCIL (1 993) Tomorrow’s Doctors: Recommendations on Undergraduate Medical Education (London, GMC).
GROEN, G.J. & PATEL, V.L. (1985) Medical problem-solving: some questionable assumptions, Medical Education, 19, pp. 95-100.
MACFARLANE, A.G.J. (1 990) Interactive computing: a revolutionary medium for teaching and design, Computing and Control Engineering Journal (July), pp. 149-1 58.
PATEL, V.L., EVAN, D.A. & KAUFMAN, D.R. (1990) Reasoning strategies and the use of biomedical knowledge by medical students, Medical Education, 24, pp. 129-136.
PAEL, V.L. & GROEN, G.J. (1986) Knowledge based solution strategies in medical reasoning, Cognitive Science, 10, pp. 91-116.
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.
160 M. J. Atkins G. C. O’Halloran
PATEL, V.L., GROEN, G.J. & Scorn, H.M. (1988) Biomedical knowledge in explanations of clinical
SCHMIDT, H.G., NOW, G.R. & BOSHUIZEN, H.P.A. (1990) A cognitive perspective on medical problems by medical students, Medical Education, 22, pp. 398-406.
expertise: theory and implications, Academic Medicine, 65(10), pp. 61 1-621.
Med
Tea
ch D
ownl
oade
d fr
om in
form
ahea
lthca
re.c
om b
y M
onas
h U
nive
rsity
on
11/0
8/14
For
pers
onal
use
onl
y.