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School Science Review, March 2004, 85(312) 113

Roberts Practicals within a problem-solving model

Using different types ofpractical within a

problem-solving modelof science

Ros Roberts

A framework for thinking about the role of practical work inscience teaching

Investigative work was introduced in 1989 as part ofthe English National Curriculum, science attainmenttarget 1 (Sc1) (DES, 1989), since when it has been acause of much concern amongst both teachers andresearchers. In some schools, Sc1 investigations seemto have become almost synonymous with practicalwork in science and in some textbooks almost anyactivity that uses apparatus seems to be called aninvestigation! The recent House of Commons Scienceand Technology Committee report (2002) castigatedpractical work in general as being something that:

students see little point in carrying out ... wherethey already know the result and are justexpected to follow instructions to reach thatend. (p. 20)

But practical work, which comes in many guises, canbe much more than the assessment-driven practicalsof the National Curriculum. The purpose of this articleis to show how different types of practical can be usedwithin a model of science as a problem-solving

activity, which will allow judgements to be madeabout what sorts of practical we should be considering.

Justifications for practical work

The first point to make is that practical work is a meansto an end; it is not an end in itself, any more thandiscussion or debate are ends. Practical work is justone of many ways of teaching science. So the mostbasic of questions is: What is to be learned and ispractical work a good teaching method for this?

There are numerous justifications for usingpractical work in our teaching, which recognise itsvalue as more than just a means of teaching ideas.Various classifications have been put forward overthe years. Kerr (1964) identified ten aims from a largesurvey of practice. Woolnough and Allsop (1985)reduced this to three fundamental aims:

■ developing practical skills and techniques;

■ being a problem-solving scientist;

■ getting a ‘feel for phenomena’.

Wellington (1998) considered the rationales presentedin the literature and grouped them under three broadheadings:

■ cognitive arguments (about understanding, visual-ising, illustrating/affirming theory);

■ affective arguments (to do with motivating, excit-ing, helping to remember);

■ skills arguments (learning manual dexterity, aswell as ‘higher level’ activities such as observ-ation, measurement, prediction, inference).

ABSTRACTThis article considers the role of different typesof practical work in school science. It illustrates amodel of science as a problem-solving activity,which requires an understanding of both thesubstantive and procedural ideas of science.Different types of practical are then locatedwithin the problem-solving model. The resultantanalysis can be used to inform decisions aboutthe appropriateness of different types of practicalwork.

Practicals within a problem-solving model Roberts

114 School Science Review, March 2004, 85(312)

As noted later in this article, there is not much goodevidence that practical work actually meets all theseaims. However, in my view, practical work canprovide much to interest and stimulate both pupilsand teachers. This article will consider how differenttypes of practical can be used to teach the ideas andskills required to solve problems in science. But whatkind of practical work could be used?

Types of practical

There are many ways of typifying different practicalactivities. For instance, Woolnough and Allsop (1985)considered there to be three main types: exercises,investigations and experiences. Millar (1989)criticised these as not including practicals that areillustrative of substantive ideas in science, used torefine or check the ‘theory’. Wellington (1994)included ways in which practicals could be organisedin the classroom in his typology; i.e. demonstrations,class experiments, circus of activities, simulations androle plays as well as investigations and problem-solving activities.

For the purposes of this analysis, five differenttypes of practical are considered, each of which has,or could be adjusted to have, a particular emphasis.These are obviously not hard and fast categories butare considered good enough for the purposes of thisanalysis.

Skill practicalsThese are practicals that teach and train pupils inscientific skills, which could range from simple onessuch as reading instruments or heating a test-tubesafely, to following complex protocols such aspreparing and staining a microscope slide, setting upa potometer or calibrating an oxygen probe. They aredistinguished as skills because they require practiceof a protocol. These skills are useful. In some science-based work, including nursing (Aikenhead, 2003) andlab-based contexts (Gott, Duggan and Johnson, 1999),the recall of skills and complex protocols seems to bean important part of the work.

Observation tasksDifferent forms of observation task can have differentdemands. Many used at school level require recall ofideas and basic skills and really act as illustrativepracticals (see below).

However, it is worth noting that observation ismore than seeing. It is a crucial window on the

everyday world through which science can be seen‘in action’. For instance, roads wear particularly badlyon corners. ‘Observing’ through a conceptual windowof force and friction, we can see that the centripetalforce needed for cars to corner results in stress on theroad surface and accelerated wear and tear. That is amuch richer ‘seeing’ that does not seem to beencompassed within most observation tasks andinvolves the application of substantive ideas to realcontexts. The observation task can be a way ofshowing how experimental science has its roots incareful, concept-driven viewing of the real world.

‘Technological’ tasksThere are some practicals that largely depend onlogical reasoning and recall of substantive ideas. Atthe simplest level these might include identifyingfaulty components within a simple electric circuit.More complex ideas and thinking are applied tosituations such as designing electronics solutions orlogical tasks such as identifying a chemical through aseries of tests. Construction of a pond could beconsidered to be a ‘technological’ task too: ecologicalideas are recalled and applied in a logical way.Technological tasks may require complex recall ofskills and protocols as well as the application of ideasto new contexts and logical thought.

Investigations and exploratory tasksThese are practicals which consider a problem forwhich there is no easily recalled solution. From myexperience, and that of the House of Commons report(2002), Sc1 ‘investigations’ seldom fit this definition!It could be argued that, for many pupils, Sc1 assess-ments have become little more than recalling complexroutines. In effect they could be thought of as skillspracticals: complex protocols to be applied to differentroutine contexts, such as enzymes, electric circuits orrates of reaction, that require little understanding orconsequent decision making.

Investigations and exploratory tasks differ inpractice largely in their scope and have been con-sidered together here because of their open-endednature. In recent National Curriculum contexts aninvestigation has been defined as a problem that isrestricted to considering relationships betweenvariables. Investigations are usually relatively short,focused tasks that could be completed in a couple oflessons. Exploratory tasks are also open-ended withno easily recalled solution; they explore moreextended problems and are not restricted to just therelationship between (usually) two variables (which



is how ‘an investigation’ has come to be defined).The task may be something like a series of linkedinvestigations within a topic, or a survey whichconsiders many variables at the same time. Suchpracticals are perhaps familiar to those working onNuffield A-level schemes. They involve the use ofboth substantive concepts and procedural ideas in acomplex task or series of tasks rather than being aparticular focused problem to solve.

There is evidence from work done by pupils inout-of-school contexts, such as CREST awards andscience clubs and competitions where pupils areallowed to carry out ‘free format’ explorations (Tytlerand Swatton, 1992; Woolnough, 1998), thatdemonstrates that the scope of genuine open-endedenquiry is within the capabilities of some pupils.Exploratory tasks allow pupils to be creative. Whilepupils recall skills and sometimes modify protocolswith which they are familiar, they also invent newways to get around practical problems, apply ideas tonew contexts and synthesise the substantive and

procedural ideas to solve the problem and analyse thedata to evaluate the evidence.

Illustrative experimentsAny of these types of practical can act as illustrations– either for pupils to do by following complexprotocols or for teachers to demonstrate. The emphasisis then at the discretion of the teacher who can bringout substantive or procedural ideas as they see fit inthat particular context.

What is to be learned?

Wellington’s (1998) ‘affective’ arguments for practicalwork are important and seem to apply, at least inprinciple, to any type of practical work. This dis-cussion is therefore going to focus on the ideas thatmight be learned in different types of practical work.The view is taken here that practical science,ultimately, is about solving problems, be that to createnew knowledge, to answer empirical questions, tomake something or to make that something work. Gottand Mashiter (1991) proposed a model for problem-solving in science which I have modified (Figure 1).The diagram presents a pared down model of thecontent-based demands of science. It is intended, quitedeliberately, to act as a skeleton for development andnot an all-encompassing model.

In this model, solving a problem in sciencerequires a synthesis of two sets of understandings,each with its own knowledge base: a substantiveunderstanding (such as the concept of a force or thetheory of natural selection) and an understanding ofthe ideas required to interpret and analyse evidence –a procedural understanding. The ‘mental processing’involved in putting the ideas together in the scientist’shead may vary according to the context of theproblem. For instance, sometimes the problem beingsolved is so similar to previous problems thatfamiliarity with the ideas and approach used meansthat minimal decision-making is required and in effectthe scientist is following a ‘protocol’. In solvingcompletely novel problems, the solution may bedesigned from scratch, drawing on skills, substantiveideas and procedural ideas in a far more complex way.In many situations, past experience enables thescientist to select and adapt previously conductedprotocols, which seems to be intermediate to theseother extremes.

An illustration of how the elements of the modelmight be used when solving a problem in science is

Figure 1 The content-based demands of aproblem-solving model for science.

Mental processingHigher order investigative skills

Designing from Selecting and Following ascratch adapting ‘protocol’

protocols

Problemsolving inscience

Substantiveunderstanding

concepts, laws andtheories;

understanding andapplication

Proceduralunderstanding

concepts associatedwith the understanding,

interpretation andanalysis of evidence

Facts Basic skills



given below, with reference to a particular fieldinvestigation in biology.

Freshwater shrimps – anexploration

This example of an extended exploration is intendedto illustrate the elements of practical problem-solvingin science in relation to the model (Figure 1). Theway in which elements from the model are used tosolve a problem are described in relation to a contextthat is not constrained by the current Sc1 assessmentcriteria: the distribution of freshwater shrimps in astream. The analysis is structured according to themain ideas being used at each stage of the exploration,which can then be located on the model.

Substantive understanding: observation,identification, adaptation and nicheImagine we are paddling in a stream. We might startto notice and identify many different animals: mayflyand caddis fly larvae, freshwater shrimp, variousworms, snails, freshwater mussels and limpets, littlefish, etc. We observe that different things are foundin different numbers in different places. It is throughideas about adaptation of organisms and the conceptof a niche that we might hypothesise that differentorganisms are likely to be found in different environ-mental conditions. It is a direct application ofsubstantive understanding and, what is more, a keylink between theory and the real world it attempts toexplain. We could ask ourselves: What factors mightaffect the number of freshwater shrimp? The answersto this would draw on our substantive knowledge baseand would inform the design of any experiment.

Procedural and substantive understanding:identification of independent variablesIt would then be time to turn the hypothesis intosomething that could be tested. We’d now need touse both procedural and substantive understandingrelated to the structure of an investigation. Theselection of which independent variables to considerwould largely be determined by our substantiveunderstanding: either ideas which are already in ourheads or by referring to what other scientists havealready found out. For instance, we probably wouldn’tconsider things like the political party in charge ofthe local council or the star sign of the farmer whoseland we are on, because we have no theory in ourheads to suggest that these have any influence on thenumber of shrimp in different places! But we might

also, wrongly, dismiss another variable that couldultimately be important because of our lack ofunderstanding.

A substantive understanding of shrimps’ need fordissolved oxygen and of adaptations that prevent thembeing washed downstream might suggest that waterspeed and depth are worth considering. Similarly,knowing that different organisms can tolerate differentsize of substrate, we might decide to record whetherthey are found in silty areas or where there areboulders. Since shrimps are largely detritivores, wecould decide that identifying the vegetationsurrounding or further up the stream might also beimportant. These decisions are determined largely byan understanding of biotic and abiotic factors affectinganimal distribution.

Procedural understanding: measurement validityand reliability – how to measure the number ofshrimpsHow might we find out how many shrimp there arein a given area – the dependent variable? The methodwe choose would affect the quality of the datacollected. We may recall a technique called ‘kicksampling’ – not the most environmentally friendlysampling method available but we might decide thattwo minutes should dislodge most of the shrimps togive a measure (a sample) in proportion to the totalpopulation. Our decision to do this for two minutes isa compromise between getting a reasonably goodestimate and dislodging every last shrimp. Since theshrimp are easy to identify and count, we work onthe assumption that our counts will be reliable enough.Judgements about the quality of the data collected bythis technique require an understanding of the conceptof reliability and its application in relation to the actualdata collected; in this case, this is the likelihood thatif we could repeat the measurement we woulddislodge more or less the same number.

Procedural understanding: selection of appropriateinstrumentsEach independent variable would need to bemeasured. Decision time! Would ‘Pooh sticks’ give agood enough measure of flow rate or should anelectronic flow meter be used? Is a metre rule markedin centimetres good enough to measure the depth ofthe water? Is the thermometer accurate enough? If apreviously followed protocol is not driving thedecisions we are making, then trials to evaluate theinstruments and determine which are ‘good enough’might be required. Again, the validity of theinstruments and the reliability of the data would need



to be judged in relation to the actual data that arecollected. In practice this means that, without priorexperience from similarly followed protocols, it isalmost impossible to plan an investigation withouttrying out the apparatus and techniques available –planning is part of an iterative process, whereevaluations are constantly being made in response tothe data collected.

Skills: practical techniques, following protocols andrecording skillsAt each site we might follow instructions or recallskills about how each measurement should be taken:How long should the thermometer be kept in the waterfor? Has the oxygen probe been set up and calibratedproperly? We might decide to record the number ofshrimp at each site and each site’s environmental datasystematically in a table, using skills we’ve developedin other practicals.

Procedural understanding: variable types, rangeand interval of the independent variables, samplingissues and the number of readingsRecognising that we need to have readings from sitesthat include a range of independent variable valuesdraws on a procedural understanding as well asdecisions about safety – it might be too fast flowingor deep to get all the readings we might want safely.A decision also has to be made about how many sitesshould be sampled (to account for the inherentlyvaried distribution of the shrimp population as wellas to ensure that sufficient data have been collectedin places where there are different values of theindependent variables) and whether repeated readingsare necessary at each site. Again, these decisions aremade iteratively – we can’t pre-judge the number ofrepeats we’ll have to do since we don’t know howthe shrimp numbers differ at each site (if at all) andtherefore how much data is required to be confidentabout seeing differences if they exist.

Procedural understanding: looking forrelationships between variables, validity of variablestructure, determining the strength of therelationship and Skills: graphing and statisticaltechniquesHaving collected sufficient data, we need to considereach independent variable in turn, to see whether thereis a relationship between it and the number of shrimp.The most valid relationships are determined byconsidering associations where values of other keyvariables have a similar value. So, we might want toconsider the relationship between shrimp numbers and

flow rate only in areas where the predominantsurrounding vegetation is, say, mountain ash trees.Deciding on the appropriate way to present the dataand selecting a valid statistical test for analysing thedata will draw on our procedural understanding, whileactually plotting the data and using the statisticaltechniques will involve recall of skills and procedurespractised before.

Substantive and procedural understanding:drawing conclusionsDetermining what we might conclude from the datacollected requires an evaluation of the whole task withrespect to the procedural knowledge base: Howreliable are the data? Is the overall investigation valid?Conclusions also need to be checked against what isalready known about shrimps and factors affectingorganisms’ distribution – the substantive knowledgebase.

SummaryThe shrimp scenario illustrates how different elementsof an extended problem-solving task draw on all thedifferent ideas within the model (Figure 1) and withdifferent levels of demand, depending on exactly howthe problem is solved. The procedural ideas used inthis task comprise a knowledge base, ideas that canbe learned and applied in the exploration. The skillsand ideas, both procedural and substantive, within themodel could then legitimately be considered as thebasis for the curriculum. This leads us back to thequestion of how.

Types of practical related to theproblem-solving model

Opportunities to carry out this sort of extended task,which incorporates all the elements from the problem-solving model, are unfortunately all too rare. And theyplace considerable demands on all concerned. But,even if it is unrealistic to enable all pupils to carry outextended explorations it is still possible to teach allelements from the model using smaller, moremanageable practical tasks, as shown in Figure 2.Within each of the different types of practicals wecan focus on different elements of the model. Pupilscan be taught all of the elements required for problem-solving through these smaller targeted practicals. Thatdoes not of course guarantee that they can put all theseideas together successfully in a longer exploration.For instance, pupils could carry out parts of aninvestigation, such as determining an appropriate



sampling method for estimating the number of daisieson the school field; or they could do an illustrativeexperiment that follows a protocol involving feedingpreferences and choice chambers, with the aim oflearning both the procedural ideas of sample size aswell as the substantive ideas of feeding relationshipsand niche. These ideas could then be used within thecontext of a full investigation or exploration.

Since illustrative practicals could be used as ateaching activity for any of these ideas, they are notincluded specifically on the figure. Exploratorypracticals are represented by the figure as a whole.Looked at in this way, we can see that these differenttypes of practical are not in any sense in competition.They form part of a coherent holistic problem-solvingapproach to practical work in science which, if keptin mind, can act as a guide to choosing tasks suchthat the end-point is a rounded understanding ofpractical problem-solving.

We can see that different practicals can be used toteach different things. What teaching approach wechoose, and for whom, then becomes an empirical

question. What works for the aims required?Illustrative practicals have been researched and

evidence largely points to them being ineffective, atleast at teaching understanding of the substantive ideas(Watson, Prieto and Dillon, 1995); this is also evidentfrom some of the comments in the House of Commonsreport (2002). There is a view (Duveen, Scott andSolomon, 1993; Wellington, 1998) that if experimentssimply ‘go wrong’ their educative value is diminishedif not negative. We know very little about the effective-ness, at least in terms of pupils’ understanding, of othertypes of practical. However, most would agree thatpupils enjoy practical work (Campbell and Wilson,1998; Wilkinson and Ward, 1997).

This sounds negative but it is not intended to be.There is much to interest and stimulate pupils andteachers in good varied practical work. To limitourselves to one type would be nonsensical: all ofthem have something to offer but we need to be clearas to what we are hoping pupils will learn from theexperience and choose accordingly.

Figure 2 Types of practical work related to the problem-solving model.

Complete open-ended practicalinvestigations – do shrimpsprefer still to running water?

Mental processingHigher order investigative skills

Designing from Selecting and Following ascratch adapting ‘protocol’

protocols

Problemsolving inscience

Substantiveunderstanding

concepts, laws andtheories;

understanding andapplication

Proceduralunderstanding

concepts associatedwith the understanding,

interpretation andanalysis of evidence

Facts Basic skillsSkill circuses – read athermometer or set up a pHmeter.

Observation – notice thatshrimps prefer places nearoverhanging vegetation and linkthis to feeding relationships.

‘Technological’ tasks – designfreshwater aquarium to supportshrimps.

Parts of investigations –choose apparatus to make areliable measurement of the flowof water, repeating until happywith answer.

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Conclusion

If we think that extended explorations, open-endedproblems with no easily recalled solutions, are the‘ideal goal’ of practical work, then it is obvious thatpupils will not be able to do many of these in anovercrowded curriculum. We therefore need to breakthem down into manageable chunks. The illustrationof the shrimp scenario shows how each of the elementsof an exploration can be located within the problem-solving model and Figure 2 shows how these elementscan be taught using different types of practical.

Different types of practical are shown to be in nosense in competition with each other: one type is nobetter than another. They complement each other inthis broader problem-solving view.

Teachers are in the best position to decide thesequencing of ideas, as well as when and how theyshould be taught in their class. As we can see, there’sa place for all sorts of practical activity in the class(and outside!).

ReferencesAikenhead, G. S. (2003) Concepts of evidence used in

science-based occupations: acute-care nursing researchreport. Saskatchewan: University of Saskatchewan.

Campbell, B. and Wilson, F. (1998) Teachers’ and pupils’perspectives on practical work in school science. Paperpresented in Copenhagen, May 1998, at conference onPractical Work in Science Education: the face of science inschools.

DES and Welsh Office (1989) Science in the NationalCurriculum. London: HMSO.

Duveen, J., Scott, L. and Solomon, J. (1993) Pupils’understanding of science: description of experiments or‘A passion to explain’? School Science Review, 75(271),19–27.

Gott, R., Duggan, S. and Johnson, P. (1999) What dopractising applied scientists do and what are theimplications for science education? Journal of Research inScience and Technology Education, 17(1), 97–107.

Gott, R. and Mashiter, J. (1991) Practical work in science – atask-based approach? In Practical science, ed. Woolnough,B. E. Buckingham: Open University Press.

House of Commons, Science and Technology Committee(2002) Science education from 14 to 19. Third report ofsession 2001–2, 1. London: The Stationery Office.

Kerr, J. F. (1964) Practical work in school science – anaccount of an inquiry into the nature and purpose ofpractical work in school science teaching. Leicester:Leicester University Press.

Millar, R. (1989) Doing science: images of science in scienceeducation. London: Falmer.

Tytler, R. and Swatton, P. (1992) A critique of attainmenttarget 1 based on case studies of students’ investigations.School Science Review, 74(266), 21–35.

Watson, R., Prieto, T. and Dillon, J. (1995) The effect ofpractical work on students’ understanding of combustion.Journal of Research in Science Teaching, 32(5), 487–502.

Wellington, J. (1994) Secondary science: contemporaryissues and practical approaches. London: Routledge.

Wellington, J. (ed.) (1998) Practical work in school science:which way now? London: Routledge.

Wilkinson, J. and Ward, M. (1997) The purpose andperceived effectiveness of laboratory work in secondaryschools. Australian Science Teachers’ Journal, 43, 49–55.

Woolnough, B. E. (1998) Authentic science in schools todevelop personal knowledge. In Practical work in schoolscience: which way now? ed. Wellington, J. London:Routledge.

Woolnough, B. E. and Allsop, T. (1985) Practical work inscience. Cambridge: Cambridge University Press.

Ros Roberts is a lecturer in science education at the School of Education, University of Durham, Leazes Road,Durham DH1 1TA.E-mail: [email protected]

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