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What do Practising Applied Scientistsdo and What are the Implications forScience Education?R. Gott a , S. Duggan a & P. Johnson aa University of Durham, School of Education , UKPublished online: 19 Aug 2006.

To cite this article: R. Gott , S. Duggan & P. Johnson (1999) What do Practising AppliedScientists do and What are the Implications for Science Education?, Research in Science &Technological Education, 17:1, 97-107, DOI: 10.1080/0263514990170108

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Research in Science & Technological Education, Vol. 17, No. 1, 1999 97

What do Practising Applied Scientistsdo and What are the Implications forScience Education?

R. G O T T , S. D U G G A N & P. J O H N S O N , School of Education, University ofDurham, UK

ABSTRACT If we are to match what is taught in science education with what is needed in employment in

science-based industry, we need to determine what applied scientists actually do in terms of generic science 'skills'.

The pilot study reported here suggests that procedural understanding or the 'know-how' of science is a key issue in

employment but one that is not easily identified. When questioned about the science they use in their work, employers

and employees tend to refer to traditional science concepts. They find it difficult to make explicit the procedural

understanding which our research found was also required in their work. If we accept the notion that procedural

understanding has a content which can be taught, then such teaching could make science education more efficient and

in the long term had to a more efficient workforce.

Introduction

If we are to provide a science education which is genuinely vocational, then we must beclear about what those who work in science or science-related employment actually do.While some of what they do will be specific to their work and so best taught by'on-the-job' training, there is also likely to be a core of generic skills which is commonto all science-based industries. We refer here not to the generic or 'key' skills which havealready been defined, namely communication, information technology, numeracy and'working with others', but to generic science skills.

Defining these generic science skills is not without its problems. For example, it istempting to associate 'what scientists do' with academic research scientists or with ascience-based industry with which one happens to be familiar. Neither can be taken tobe representative of what is needed in employment in science-based industries as awhole.

The new General National Vocational Qualifications (GNVQs) in the UK, launchedin 1992 were intended amongst other things, to:

• offer a broad preparation for employment as well as an accepted route to higherlevels of qualification; and

• require a demonstration of a range of skills and appreciation of knowledge andunderstanding relevant to the related occupations (DES/ED, 1991).

0263-5143/99/010097-11 © 1999 Taylor & Francis Ltd

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98 R. Gott et al.

But the new vocational qualifications have met with considerable criticism based largelyon their 'competence approach'. Smithers (1993), for example, writes that the com-petence approach assumes that:

... if students can show themselves capable of carrying out specific tasks, thenecessary knowledge and understanding must have been acquired also andneed not be separately assessed, (p. 9)

And again, Spours (1995) cites Sparkes of the Royal Academy of Engineers who writesregarding the lack of emphasis on understanding:

Competency and recorded achievements only reveal people's ability to dealwith yesterday's problems, but leave people helpless when faced with tomor-row's.

In this paper, we argue from previous research in science education and from the pilotstudy reported here, that there is a fundamental problem in science education as a wholewhich arises from a lack of recognition of the significance of procedural understandingin science and that this is particularly important in vocational qualifications. If thisunderstanding were to be explicitly taught in science education, it will go some waytowards addressing the criticisms of GNVQs and towards meeting employers' needs. Butfirst we take a brief look at existing research into answering the question 'what dopractising applied scientists do?'

Existing Research

Much existing research focuses on employers' requirements in general terms rather thanin terms of science. For example, in a recent report, Harvey et al. (1997) used in-depthinterview techniques to explore 'what employers think significant'. They conclude thatwhat employers want for the twenty-first century is recruits who are adaptive (e.g. withgood interpersonal skills), adaptable (e.g. able to respond well to change) and transformative(e.g. able to analyse and synthesise).

In terms of science and mathematics, a report by the Council of Science andTechnology Institutes (CSTI, 1993) focusing more specifically on employment whereknowledge of either or both of these subjects is required, sought the views of over 1000employers in industry. Their results showed that some 30% of the workforce uses scienceor mathematics in some aspect of their work. Of those, a relatively small fraction of theworkforce (4%) are engaged in 'pure science' compared with the rest who are employedin applied science and engineering.

The report also examined what it is that industry requires of employees in all theseoccupations as identified by their employers. They identified three 'skills':

(1) a central core of skills concerned with the doing of science;(2) communication skills; and(3) management skills.

The first of these, which is of most relevance here, is defined in more detail as the abilityto:

• generate own ideas, hypotheses and theoretical models and/or utilise those postu-lated by others;

• design investigations, experiments, trials, tests, simulations and operations;• conduct investigations, experiments, trials, tests and operations; and

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What do Practising Applied Scientists do? 99

• evaluate data and results from the processes and outcomes of investigations,experiments, trials, tests and operations.

Another study by Coles (1997) involved interview rather than survey techniques. Colesinterviewed scientists employed in the private and public sector across a wide range ofscientific fields and at different professional levels. He came to broadly similar conclu-sions as the CSTI, which can be summarised as:

• an understanding of scientific evidence;• an understanding of major scientific concepts; and• personal and interpersonal skills.

Coles found that general capabilities were often expressed ahead of any specific scientificknowledge, understanding or skills. He defines general scientific capabilities as beingpractical techniques (including safety, reliability, good observation and accuracy), prob-lem-solving by experimentation, decision-making by weighing evidence and scientifichabits of mind (such as logical thinking, scepticism).

While the above research has gone some considerable way to defining the problem,its putative solutions are at too general a level as yet. It is all very well to exhort schoolsand colleges to 'encourage scientific habits of mind' but how is that to be done? Moreimportantly, what understandings must be taught so that pupils and students have a realisticchance of being able to do that which is required? Our research has attempted to delvebelow 'the things that scientists do' in a search for the understandings which arenecessary, if insufficient, preconditions. We suggest that, in order to tackle the so-called'skills gap' and to know what to teach, we need to define the specific skills that practisingapplied scientists need in order to make it possible to target teaching appropriately.

Research into the Doing of Science

Our previous research into the performance of practical tasks in science education inschools has led us to develop a 'performance model' (Fig. 1). Such a model could bebased on cognitive learning theory and/or in a motivational context. Rather than eitherof these, we chose a model which likens problem-solving to a computer. We suggest thatproblem-solving requires that the solver has a bank of facts and understandings at his orher disposal (in memory as it were) and that the quality of the solution depends on thequality of the mental processing of that bank in the context of each particular problem.We recognise that such a model is a very simplified account of the most complex ofhuman activities and one which fails to pay due attention to attitudinal and motivationalfactors. Our justification is that, as in science, the model you choose should be the mostparsimonious one that will account for the phenomenon under consideration which, inour case, is the establishment of an appropriate bank of facts and understandings.

Definitions in such a model are often problematic. We will define what we mean byskills, facts and concepts but acknowledge that such definitions are by no meanswatertight. Without such simplification, however, we cannot proceed. By conceptualunderstanding we mean a knowledge base of substantive concepts such as the laws ofmotion, solubility or photosynthesis which are underpinned by scientific facts, e.g. thatdistance can be measured in centimetres. By procedural understanding we mean 'thethinking behind the doing' of science and include concepts such as deciding how manymeasurements to take, over what range, how to interpret the pattern in the resulting dataand how to evaluate the whole task. These concepts are in turn underpinned by 'skills'.

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100 R.Gott ctal

Conceptual understanding- concepts, laws and theories

Procedural understanding- concepts related to

acquisition and validation of evidence

Facts Skil ls

FIG. 1 A performance model (based on Gott & Duggan (1995).

It should be noted that by skills we mean simple mechanical aspects of activities such asknowing how to use equipment (e.g. a microscope) or knowing how to construct a linegraph. We believe procedural understanding to be a knowledge base in its own rightequivalent to conceptual understanding. In practice, of course, these two types ofknowledge cannot be separated, since, in performing any task, there is always a degreeof interaction.

An example of a simple task will serve to illustrate how the model can be applied. Ina school laboratory setting, pupils are asked to carry out the following investigation:

Find out how the temperature of the water affects the time that sugar takes todissolve.

In this task, pupils must (amongst many other things):

• decide what the question means—this requires a basic understanding of the relevantsubstantive ideas such as the notion of dissolving (cf. melting), the idea of saturationor the likely effect of temperature (conceptual understanding);

• choose the most appropriate equipment—the most suitable measuring cylinder,thermometer, scales, etc., to allow, for example, for appropriate sensitivity; decidewhat to measure; decide what repeat measurements could and should be taken andwhy; decide how to present and analyse the results; and decide how to evaluate thewhole task in terms of the reliability and validity of the ensuing evidence (proceduralunderstanding);

• know how to use a thermometer (a skill); and• then they must process all the above in the context of the task (mental processing).

Our research has shown that most pupils can carry out practical tasks in scienceadequately but that few can understand, interpret or evaluate their data (Foulds et ai,1992) . We believe that this is due to the fact that the understanding which underpinsthe 'doing of science', or procedural understanding, is, perhaps surprisingly, not explicitlytaught at any stage in the existing education system. Until now it has been assumed,consciously or otherwise, that pupils will pick up procedural understanding through

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What do Practising Applied Scientists do? 101

practice. Our research has shown that the brightest pupils do, but that the majority donot.

In 1994, we began to list and define concepts underpinning procedural understandingso that, subsequently, we have been able to design teaching materials to target theseunderstandings for schools [1] and also for colleges, where materials are currently beingtrialled within the new GNVQ_in science [2].

We believe that procedural understanding is a central issue in science which is essentialin problem-solving and so likely to be particularly relevant wherever science is used inthe workplace in applied science and engineering. If we are right, then such understand-ings may inject a degree of rigour into vocational education which, some would argue,has not yet been achieved satisfactorily. Procedural understanding may also be asignificant factor in the skills gap.

To test our hypothesis, we carried out a pilot study in a local company.

The Pilot Study

Method

A small, local biotechnology company was used for the pilot study. In selectingappropriate methodology, we were guided by the literature which points to methodolog-ical difficulties in interview and observation techniques. Eraut (1990) suggests ways ofidentifying the knowledge that underpins performance and writes:

... ordinary interviewing is rarely successful in identifying such knowledgebecause the expert performers are seldom explicitly aware of the knowledgethey are using.

He continues:

It is only when interviewing is conducted in ways informed by a tentativeperformance model and based on particular techniques that it is likely to yieldthe required results.

We decided, therefore, to use our model of problem-solving, described above, as the'tentative performance model' to inform the following approach:

(1) An initial interview—with a senior employee in the production team who supervisesand recruits employees to explore ways of accessing the information we required.

(2) Documentation—scrutiny of internal training schedules, work records and productprotocols and discussion by the multidisciplinary research team aimed at establish-ing the knowledge and skill requirement of the company. This exercise took aconsiderable time since the aim was for the researchers to fully understand thescience involved in all stages of the production of one of the company's typicalproducts.

(3) Discussion—with senior staff to validate and explore the issues arising from thefindings in steps (1) and (2) and to make sure that we had a secure base for furtherwork.

(4) Interviews—with staff at various levels within the company to establish the accuracyof the knowledge and skill requirements identified in the earlier stages and todiscriminate between the perspectives and requirements of different levels of thecompany. These interviews took place at the workstation so that the employeecould demonstrate the equipment and procedures used.

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102 RGott ctal

The Company

We give here a brief description of the company gleaned from steps (1), (2) and (3) of ourresearch plan. The company, established 20 years ago, develops, manufactures andmarkets a range of medical test kits that are used in hospital and research laboratoriesin the measurement of picogram quantities (1 million-millionth of a gram) of hormoneor related biomolecules in a single drop of blood or serum. The core technology isimmunoassay, a technique which relies on the ability of a specific antibody, or a mixtureof antibodies, to detect and bind to minute quantities of the substance under investiga-tion, a reaction which is both highly sensitive and specific. The degree of binding is thendetermined isotopically or by the enzymatic development of colour, and the resultquantified and expressed as the blood or serum concentration. Hence, if a doctor wantsto know the level of a particular hormone in the blood of a patient, the hospitallaboratory could use one of these test kits to provide the answer.

The company is a small, limited company employing 24 people in a variety of roleswith three levels of scientific/technical staff centred in production, quality control andresearch and development teams. The company works closely with university andhospital researchers to develop new products and processes and has gained a number ofawards for innovation. The qualifications of its workforce range from technicians withbasic qualifications (General Certificate of Secondary Education (GCSEs)) at the lowestlevel to research biochemists with postgraduate degrees at a high level. Products aredeveloped by the research and development team and then the new product istransferred to production and quality control teams. Standard operating procedures arecreated and quality control parameters set and agreed before routine production cancommence.

Results

Analysis of the knowledge and skin used by the company in terms of the performance model

Knowledge and skill required in the manufacture of a product. The documentation of one of thecompany's products, a kit to measure a hormone, was closely examined and each stagein its manufacture analysed in terms of the understanding required (a total of 30 stages)using the tentative performance model described earlier. This process took a considerabletime because it was necessary to understand the science involved at each stagethoroughly before the analysis could proceed. An example of the analysis of four of thestages is shown in Fig. 2.

The complete analysis of all stages could then be summarised in terms of theconceptual and procedural understanding required in the manufacture of this particularkit:

• The conceptual understanding involved includes the understanding of concepts such asbiological variation, homogeneity, antibodies, the kinetics of antibodies, cross-reac-tivity, radioactive decay, pH, dilution, concentration and the relationship of pico-moles to picograms.

• The procedural understanding involved includes skills such as the use of equipment (e.g.pipetting devices, a centrifuge, a gamma counter) and graphical interpolation.Concepts which need to be understood include repeatability, error in its various guises(instrument, human, inherent error, etc.), appropriate accuracy and the significanceof controlled conditions.

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What do Practising Applied Scientists do? 103

100uL quantities of each of the sixcalibrated hormone standards are putinto duplicated labeled tubes.

The tubes are vortexed and thenincubated overnight (16-24 hours) at 2-8°C.

The concentration of hormone in thepatient sample is then read off from thecurve by interpolation.

This is compared with expected values(ranges) of hormone for local population

Significance of duplications: to minimiseerror. Use of pipetting device.

Understanding the importance of controlledconditions: time and temperature. The skillof using a vortex mixer.

Understanding interpolation and itslimitations in terms of error.

Understanding the concept of normal rangefor a given population and the clinical

significance of falling outside that range.

FIG. 2 Example of the analysis of the manufacture of a product in terms of the model.

• Mathematical skills are also required in the calculation of percentages, dilutions,concentrations, etc., and some statistical knowledge is needed in computing andusing standard deviations, correlation coefficients, etc.

The analysis was shared and then discussed with senior members of the company toestablish face validity. They agreed that in the main, the analysis was a fair reflection ofthe procedures involved in the manufacture of this product and that the product typifiesthe nature of their work. Minor modifications to the analysis were made at this stage.

Knowledge and skill required on-the-job at two levels of employment. Having identified theunderstanding underlying the manufacture of the product, we then set out to determineby interview and observation what understanding was actually required on-the-job fortwo employees of the company working at the first (or most basic) and second levelswithin the structure of this particular company. The interviews took place at theirworkstations.

Discussion with an employee at the technician level in the production department, Ian,revealed that written protocols are followed which are checked by more senior staff atcritical stages and completed procedures are inspected by a supervisor. Detailed observa-tion and questioning, informed by our understanding of the work of the companygleaned from the documentation, enabled us to probe Ian's understanding.

In terms of our performance model, Ian demonstrated some conceptual understandingof dilution and homogeneity. He also demonstrated highly developed skills in the use ofequipment such as balances, pipetting and mixing devices and pH meters, all of whichhe used routinely. He also showed some procedural understanding of, for example, theneed for using the most appropriate instrument for the task:

Obviously if you are weighing out milligrams it is best to weigh out 200 thantry and weigh out 0.2 on a one decimal place balance. ... because 0-2 couldbe 0-24g.

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104 R. Gott et al.

Ian also understood how human error, inherent variation, instrument error, sensitivity,and accuracy are involved in his daily work and said he was able to use mathematicalskills in calculating dilutions, means, etc. However, Ian demonstrated that he had alimited understanding of the whole process, admitting that he did not know what thehormone kit was for (although he said that he had been told).

An employee in the quality control department, Kevin, who was working at a higherlevel (quality control supervisor) demonstrated a much better understanding of the wholeprocess:

... you are dealing with actual clinical results which are going to be interpretedto find out what is wrong with a person which will eventually influence thetreatment of that person.

The work of the quality control department includes ensuring that new batches of keycomponents of products perform effectively and give results within strict limits and if not,to indicate likely cause of failure in the event that performance is outside these limits.Kevin was also able to express a clear understanding of the concepts relevant to his work,such as antibodies, buffers, binding and radioactive labelling. He had acquired similarskills to Ian in the use of equipment. In terms of procedural understanding, Kevin, likeIan, expressed an understanding of human error, instrument error, sensitivity, accuracyand instrument choice, but he also demonstrated an understanding of control variablessuch as time and temperature which are crucial to his work:

Temperature is important. A lot of our stuff is stored at cold temperatures. Ifan assay is running at room temperature you can't take the reagents straightout of the fridge and put them into assays ... the reactions depend ontemperature so we always have to make sure that the reagents are at the righttemperature.

He also had an understanding of the notion of precision and the importance ofinterpreting graphical output. In terms of mathematical skills, Kevin used standarddeviation and the coefficient of variation routinely and understood their significance.

The company's perceptions of the knowledge and skill used by employees

Knowledge and skill required in the manufacture of a product. In discussions with the senior staffabout their perceptions of the scientific understanding required in the work of the companyas a whole, they referred to facts and concepts such as dilution, buffers, pH, i.e.traditional science concepts, and mathematical knowledge such as standard deviation,covariance, etc. On further questioning, they also talked about requiring 'common sense'in their employees, by which, it transpired, they meant such things as choosing the rightinstrument for the job in order to maximise accuracy or measuring to a meaningfuldegree of accuracy. They did not associate these ideas with science. Perhaps this is not surprisinggiven that this understanding is neither taught within science now nor indeed at the timewhen these employers were themselves being educated either in school or in theiruniversity degree programmes.

Knowledge and skill required on-the-job al two levels of employment. At the technician level,when Ian was asked if what he was doing was scientific, he said that following procedureswas 'pretty much common sense' and that it was 'not exactly taxing scientifically'. Butin response to the question 'would you say that what you are doing involves science?',

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What do Practising Applied Scientists do? 105

he responded positively. When asked to be more specific, he associated science with'research and stuff ... sac cells, chemical reactions and antibodies'.

When asked if his school education was useful to him in his job, Ian's perception was:

Not really—most of it at school was all biology—plants and things.

As a prompt, when asked if he had done anything about calibration or sensitivity, herecalled calibration graphs 'mainly in physics'. He is now studying for a HNCqualification but again he does not regard his studies as being useful in his everydaywork.

Kevin, working at the next level, was also asked if what he was doing could bedescribed as scientific, he said:

I would say more analytic, it's more routine analytical work rather than actualscientific approach. We've got a proposal for a problem and go ahead andcome up with a hypothesis and test that hypothesis.

But when questioned further, he explained that although lie felt that routine procedureswere not scientific, he thought that the development of the procedures (in a differentdepartment):

... would be scientific. For example, you would be testing setting up anexperiment to determine what is the ideal antibody volume going to be. Youhave to try a number of different volumes and concentrations ...

When asked whether what he was doing involved science he referred to 'antibodies,analytes, radioimmunoassay'.

Although Kevin had a background in immunology, in response to the question 'howmuch do you think you use what you learned in school/college in this job?' he replied'very little ... it was useful but I wouldn't say essential'. He believed that training forspecialist equipment could not be done at university and has to be done on-the-job. Herecalled being taught about errors and variability in statistics and biology, but that atcollege:

... to tell the truth it tended to go in one ear and out the other ... it's justnumbers—you sometimes can't relate those numbers to any practical appli-cation ... whereas here you can see it.

When asked about instrument sensitivity and inaccuracy, Kevin could not recall anyspecific teaching about these issues in his formal education.

Discussion

Our pilot study has highlighted one of the difficulties of finding out what appliedscientists actually do in the workplace. What emerged clearly and very early on in thestudy was that there was a problem in accessing the type of information we were seeking.The difference in approach and language between the employer or employee and theresearcher was marked. Simply asking an employer or employee what sort of sciencewould be useful in this kind of work would have resulted in a list of scientific facts andconcepts such as dilution, antibody, pH, buffer, etc. Hence, surveys or 'ordinary'interviewing are unlikely to succeed beyond this level.

Our performance model and, in particular, our tentative definition of proceduralunderstanding together with detailed analysis of the work of the company (in thisinstance, largely from the documentation) meant that we could then begin to communi-cate effectively and probe the scientific understanding used in the workplace. This, in

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106 R. Gott et al.

turn, encouraged both employees and employers to make explicit the understanding theyused, even though they did not recognise it as 'science'. This approach resulted in abetter description of the requirements for employment in the company than wouldotherwise have been possible.

Not surprisingly, we found that aspects of conceptual and procedural understandingare important in the work of the company as a whole and to varying degrees at the twolevels of employment studied. Our hypothesis, that procedural understanding is a keyissue in science-based employment, was confirmed by the pilot study. While a lot of theprocedural understanding emerging from the detailed analysis of the work of thiscompany equates with concepts we had already identified, the results have also led us toamend and extend our list of ideas. Our findings showed that both employers andemployees readily identified the conceptual science element of their work but tended todescribe procedural understanding in terms of 'common sense', 'procedures' or 'analyti-cal work' They did not regard it as 'science'. It is interesting to note that Kevin, whosejob in quality control was largely problem-solving, did not regard this aspect of his workas being scientific. In our terms, his everyday work lies at the heart of our model—it isindeed science in action.

Clearly, however, we cannot generalise from this one company. Sampling a range ofother industries will allow us to validate our ideas and may result in the addition of otherconcepts to our list. Studying a wide range of industries would also enable us to identifywhich of the concepts underlying procedural understanding are common to all.

The definition of procedural understanding and the establishment of which aspects ofit are used to a greater and lesser extent in science-based industry will allow for betterteaching/training schemes. Currently, science education encourages pupils to carry outinvestigative problem-solving. Through such practice pupils are expected to arrive at anunderstanding of the concepts we have outlined. Our definition provides a content forteachers and a means of analysing what their pupils do and do not understand. It followsthat teachers can then target their teaching appropriately on any particular concept withwhich their students are having difficulty. For example, if students are unsure aboutrepeatability, then activities and exercises can be used to develop their understanding ofthat issue. Such an approach makes learning more efficient.

A better understanding of these ideas is also likely to result in a more efficientworkforce. At present, in the company studied here, mistakes, which result in financialloss due to the waste of costly materials, are attributed to human error or a lack of'common sense'. Some of these mistakes suggest a lack of understanding of the processas a whole and of the impact of an error in one stage of the process on the validity ofthe final product. If employees had a better understanding of the ideas underpinningprocedural understanding, mistakes will still occur but errors should be spotted earlierand their significance recognised. After some discussion of these issues, senior staff in thecompany recognised the long-term benefits of such understanding and thought thatemployees with such training would be more likely to be a positive asset to the companyin terms of diagnosing problems, making constructive suggestions, etc. From theemployee's point of view, such understanding would mean diat s/he would be likely tofind the work more interesting and perhaps gain promotion sooner without the need togain qualifications unrelated to their everyday work which, at the moment, are seen asthe principal means of progression.

The curricula for GNVO_ science do include some of the ideas we have identified, suchas specificity or precision. However, the criticisms that have been made of GNVQssuggest that the competence focus has led to students completing a procedure or a task

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What do Practising Applied Scientists do? 107

but not gaining the understanding which underpins that performance and without whichprogress with subsequent tasks must surely be slower than might otherwise be. Wesuggest that this fundamental problem is due to a lack of recognition by both curriculumdevelopers and teachers of the significance of procedural understanding and the notionthat it is something that can and, we believe, should be taught. If we adhere to thesomewhat narrow 'one-sided' view of science by teaching only one arm of our perform-ance model, namely the arm which is traditionally associated with science education,then we are in danger of omitting the generic science skills which our pilot study suggestsare also of considerable significance in science-based employment.

Acknowledgments

The authors would like to acknowledge the management and staff of ImmunodiagnosticSystems Ltd, Tyne and Wear for providing the opportunity to carry out the pilot studydescribed in this paper.

Correspondence: Sandra Duggan, School of Education, University of Durham, LeazesRoad, Durham DH1 1TA, UK. E-mail: Sandra.Duggan@durham.ac.uk

NOTES[1] For example, Gott el al. (1997).[2] Pilot project on skills, competence and capability in GNVQ, science funded by Nuflield

Science in Practice.

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