Problem solving and problem-solving education in dentistry

Eur j Dent Educ 1997; f: 18-24 Printed in Dentnark. All rights reserved

Copyright 0 Munksgnnrd 1997

K U R O P L A Y J 0 U R ) ; A L 01

Dental Education lSSN 1396-5883

Problem solving and problem-solving education in dentistry Jaap J. ten Bosch

Laborato y for Materia Technicu, School of Medical Sciences, University of Groningen, Groningen, The Netherlands

Academic education in dentistry should lead to the capability to solve complex open problems. The student entering university is only familiar with closed problems. Research into the making of a medical diagnosis has shown that novices do so differently than experts. These research results are summarised and are presumed to be transferable to dental diagnosing and treatment planning. Examples of this transfer are given. Until 1 or 2 decades ago, education in problem solving was done only by extensive teacher-student interaction in clinical and scientific work. Presently, this is no longer adequate because of reduction of curriculum time for these activities. Therefore other methods have been developed in many places. These methods include

the use of a problem-solving model (heuristic). Based on models in the literature, 3 new models are presented. These are inten- ded for novices that solve a dentally relevant open problem in basic sciences, for novices that solve a clinical dental problem, and for more expert students that solve such a problem. Dia- grams and examples are given.

Key words: problem-based learning; heuristics; curriculum design; education; teaching.

0 Munksgaard, 1997 Accepted for publication 7 7 February 7997

CADEMIC training should lead to the capability to A solve complex problems. This also holds for dentists, who are not only confronted with the task of composing a complete treatment plan from several treatment modalities, but also with the task of diagnosing the cause of an oral complaint in the context of general health and health behaviour.

Traditionally, this capability was acquired in a one- to-one contact between student and teacher. This system goes back to the guilds in the Middle Ages. It is still practiced, in particular where a high level of (cognitive or psycho-motoric) skill is needed by relatively few students. Examples are the practical parts of the training of specialties in surgery and the ap- prenticeships for future attorneys. It is considered un- suitable for larger groups of students because of the high cost and the strong dependence on the teaching capacities and professional opinions of the individual teachers, in combination with the fact that presently the diploma is awarded (and its quality quaranteed) by an institution rather than by a teacher, as was the case previously.

In an intermediate period, say from the beginning of this century to the seventies, the master-student system was still operative in the clinical parts of the medical (and later dental) curricula. Since then it has been realised that teaching problem solving needs to be more cost-effective and less dependent on the individual teacher. Research provided knowledge on how

novices and professionals solve problems. This research was the basis for the development of new types of curricula and for new methods of teaching problem solving.

The aim of this paper is to offer some background information on problem solving and to present some methods to aid the teaching of problem solving.

Entrance and exit conditions The student entering the university dental school has been confronted with problem solving intensively. TO him, the term “problem” will directly be associated with physics and chemistry problems. We, as teachers, have to realise that those form a rather different cat- egory of problems: closed problems which have only a single, unique solution. The process of solving those is restricted to finding an appropriate solution path towards the single solution. In this type of problem, the reality is modelled into a uniquely solvable form by the teacherlexaminer. For this purpose, all permitted assumptions and needed values are given together with the problem. Thus, reality is idealised as far as needed.

The academically trained dentist leaving the university has to solve open problems: reality is not idealised but in contrast is rather complex, and the solver has to make his own assumptions and estimates. Be- fore doing so, he has to structure in his mind the prob-

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Problem solving education in dentistry

How do novices sofve problems? (Memory contains only basic knowledge)

L \

Observe problem

Search In memory for cues

\ Compare problem

connected to cues ry other cues with knowledge

Executq solution r Complete relation between

problem and knowledge 4

Decide of! SOlUtlOn

/ Reason from knowledge

\ about possible solutlons

Fig. 1. Novice thinking in problem solving. The memory ofa novice contains inter-connected basic knowledge. The novice searches iii the problem for cues that connect the problem to basic knowledge, their reasons with the basic knowledge tofind other cues and so on until the problem is understood. ?%en, reasoning will solve it.

lem and the environment in which it presents itself. In fact, as it has been formulated by the College of Dutch Rectores Magnifici (1): “The graduating academic should be able to dissect a complex problem into specified partial problems, to solve these with existing or new knowledge, and when necessary to synthesise these partial solutions to a complete solution of the entire problem.”.

Novice problem solving and expert problem solving We are interested in the solving of open dental problems, and how to acquire the capacity to do so. Much research has been done on the cognitive processes that Iead to a medical diagnosis. We feel that very similar processes occur when designing a treatment plan for a complex dental problem and also in making a dental diagnosis, although the latter problem is usually less complex than a medical diagnosis. We emphasise, however, that in other professions, similar, or different, principles are desirable. In engineering and in law, as in medicine, similar problems should lead to similar solutions, and thus the teaching models (2,3) are similar to the medical teaching model. In business economics, however, dissimilar rather than similar solutions for similar problems are required (4) and this fact has its influence on the solving process. We will transfer the results of medical diagnostic thinking to the designing of a dental treatment plan, as follows.

Novices have basic knowledge available in the form it has been taught to them (5). In the problem, they

Annex I The novice-approach to the diagnosis of fissure caries: data-driven reasoning (Fig, 1) A dental novice is asked to consider the mouth of a patient who regularly visits the dentist and to diagnose the molar fissures for dentinal caries.

The novice will recall the basic science related to caries: the caries process starts with demineralisation below an intact surface layer, i.e., the formation of a white spot, possibly fol- lowed by discoloration and, when the process continues, by cavitation. Hopefully, the novice will also recall that caries is caused by a combination of plaque and a diet containing many daily instances of intake of fermentable carbohydrates. He may also recall that extensive demineralisation of occlusal enamel and dentine may be discovered with X-rays, but that these in principle are harmful. He will also know that incipient lesions may remineralise when the caries-in- ducing conditions change to caries-unfavorable conditions, but that cavitated lesions will not remineralise.

A few cues connect this knowledge with the problem. The first are the seemingly simply observable situations that are directly related to the problem: cavitation and/or white or discolored spots. Second are the easily observable but in- directly related situations: the presence of plaque and the state of the dentition.

Thus, the novice will look for cavitation with mirror and light. To avoid damage to an intact but vulnerable surface covering a subsurface lesion, he will not use an explorer. When looking, he finds no cavitation but a spot that looks more opaque than the surrounding enamel, and the latter shows some discoloration in the depth. To the novice, this is alarming but not conclusive. Thus, he requests the making of a bitewing. This shows a large and relatively deep dark area under the fissure. On the basis of this, the novice con- cludes that dentinal caries is present.

When the novice subsequently opens the suspected area for treatment, he will explicitly evaluate his diagnosis, thereby starting to build a collection of cases in his memory that later will be used for predictive reasoning (see Annex 2).

search for cues that may connect the problem to the basic knowledge. This has been called ”data-driven reasoning” (6) . The better the basic knowledge is or- ganised in the mind of the student, the better this process of connecting is performed (7). This process is pictured in Fig. 1. An example from dental diagnosis is given in Annex 1.

Experts solve problems in a different way (5,8). The memory of experts contains a collection of previously solved problems with their solutions, in medicine called ”illness scripts”. We suggest the term “dental problem scripts” for dental problems. Connected to those scripts, basic knowledge is available in ”encap- sulated” form, i.e., it can be traced and activated when needed (5). To solve a new problem, the dentist recalls one or more similar old problems and adapts the solution thereof to the new problem (Fig. 2). At least one

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ten Bosch

How do experts solve problems?

\ \ (Memory contalns solved

cases and Internalized bask knowledge) observe pr blem

1 search In memory for related

problem better

refine search In memory

I evaluate

find most resembling problem

t execute solution

J adapt solution of previous problem to present problem

Fig. 2. Expert thinking in problem solving. An expert searches in the memory for a similar problem and compares that in iterative steps to the present problem until nearly complete congruence is found. Then, the previous solution is adapted to solve the present problem.

feedback-loop is used to ensure a proper solution. This has been called “predictive reasoning” (6). An example is given in Annex 2.

Teachers’ tasks The first task of teachers is to help the student to alter his problem solving capability from closed problems to open problems, This task meets an emotional bar- rier: the student has to accept that he becomes responsible for his choices of structuring and of making assumptions and estimates, as are needed in the solving of open problems, responsible for choices which deter- mine the outcome of the solving process. This can be very threatening.

The second task is to help the student to develop his capability to solve dental problems by data-driven reasoning. Curricula based on the principles of problem-based learning in particular are designed to fulfill this task. The students receive a problem to be solved, they define it, structure it and analyse it, formulate their learning goals in terms of basic science knowledge and understanding, and then solve the problem (9).

Finally, the student may need help in learning to obtain a capability to solve dental problems by predictive reasoning. For the latter, the student has to build in his mind a set of clinical problems and solutions. This set develops with experience, but in only a few dental subjects, will enough experience be gained during clinical work in the curriculum. Teachers may support the development of a set of dental problem scripts by explaining their reasoning, as well as their

4nnex 2 The expert-approach to the diagnosis of fissure caries: pre- iictive reasoning (Fig. 2) me expert is confronted with the same problem: diagnosis 3f dentinal caries in molar fissures of a patient who regularly k i t s the dentist.

The expert will use his experience in diagnotics. At his first glance in the mouth, he will consider the approximate number of already present restorations. Then he will consider the presence of plaque. These two observations will give him an indication of the probability of a new caries Lesion and of the expected speed of progress of any lesion. Suppose he observes mediocre hygiene and rather many restorations for the age of the patient. Subsequently he will look at the fissures, considering not only the presence of cavi- tations and white or discoloured areas, but also considering the shape of the fissure in view of its relation to plaque reten- tion. Suppose he observes a suspect bell-shaped fissure. He then will (subconsciously) recall from his memory similarly looking and similarly shaped fissures together with his diagnosis on those. Since the general dental situation of the present patient is worse than average, he will reach a diagnosis of dentinal caries more easily than in a similarly looking fissure in his memory. Most probably, he will not make a bitewing radiograph.

Upon opening the suspected area for treatment, he will evaluate his diagnosis, thereby adding another case to the collection of cases in his memory.

solutions, during clinical teaching. Nevertheless, in most subjects, an adequate set will be obtained after graduation. Thus, in the first years of practice, data- driven reasoning is often used. This necessitates a thorough training therein in the dental school. Since the outcomes of such reasoning will be stored in memory for later use in predictive reasoning, it is very important that students are thoroughly trained to evaluate each problem-solving process and its outcome.

Curriculum design In relation to problem solving, 2 aspects of the curriculum are very important. These are the integration of the basic sciences and the phase of the curriculum in which the student has to switch from solving closed problems to open problems.

TraditionaIly, the basic sciences were taught within their own context, i.e., biochemistry as a part of chemistry, biophysics as a part of physics, anatomy as a part of biology, etc. This allowed integration of these fields with knowledge and expertise already present in the novice student, and also mutual integration. In- tegration with dentistry was done in extensive master-to-student teaching in the dental subjects, particu-

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larly in the clinic. Over the past decades, clinical curriculum time in any particular subject has been re- duced, due to the increase of the number of dental subjects the student has to become acquainted with and to the increased attention to human biology, medicine and behavioural sciences in the modern dental curriculum. In response to that development, some dental curricula have been redesigned (Harv- ard, Malm6) (11, 12) as problem-based, problem- oriented or similarly characterised curricula. Other schools are to follow: Oslo, Huddinge, Groningen. In such a curriculum, the basic sciences are taught in connection with, or integrated with, the dental problems for which they are relevant: biochemistry with plaque, crystallography with mineralised tissue, mechanics with bite forces and prosthetics, anatomy with orofacial functions. In other curricula, only some courses (13-15) were taught in a problem-based manner. Although this development towards integration of clinical and basic sciences in teaching is wide- spread, its effectiveness is also debated (16).

The start of practising the solving of open rather than closed problems marks another switch in the curriculum. Although in principle the type of problems solved is not related to the type of integration of the basic sciences, in practice, the solving of open problems starts at the same stage of the curriculum at which problem-based teaching starts.

The use of heuristics Heuristics or problem-solving models are used in the teaching of problem solving to reduce the use of more expensive methods for this purpose, such as the close, almost permanent, supervision as practiced in, e.g., clinical work or scientific work by students. Such a model provides a structure of the problem-solving process. It prescribes activities in specified steps, sep- arating logical reasoning in certain steps from the making of subjective choices and assumptions in other steps. Although the use of a heuristic is quite common, the advantage of its use has also been chal- lenged (16). A summary of models used in dentistry has been given before (17).

The teaching of solving open problems in basic sciences, the teaching of solving dental problems in a data-driven manner, and the guidance in solving problems by predictive reasoning, require 3 models. Of course, the transfer from one model to the next should be as simple as possible by using the same expressions whenever possible and by using similar lay-out. We have designed 3 models, heuristics, which are summarised in Figs. 3-5. They are based on the

original Groningen models (15, 18) but contain elements of all models described in an earlier paper (17).

Fig. 3 presents a model to be used for non-clinical problems. This model may be used by students in groups that are to solve open problems in fields they are familiar with in the first years of the dental curriculum: physics, physiology, biochemistry, behavioural sciences, and the like. An example is given in Annex 3.

In the first step, the global problem is presented. In the second step, the system in which the problem occurs is defined. It can be taken as very small, and then many influences from the outside have to be considered, or as very large, in which case many subsystems have to be considered, This choice will not influence the solution, but the student should learn to structure. Subsystems, aspects, and processes are defined and listed in general terms; also, this is a matter of choice. In the third step, all processes and other connections between subsystems and elements of the system are analysed; relevant laws, quantities, and known numerical values are collected. In step 4, the problem is specificied and the aim formulated. The latter is a matter of choice: it might, e.g., be the choice of desired accuracy of a numerical answer. At this point, it will appear that the system is far too complicated to solve the problem. In step 5, it is rigorously simplified by making assumptions and estimates to such an extent that simple methods can be used to solve the problem. This is one of the most crucial steps in the process. After this, in step 6, the simplified problem is solved straightforwardly. Finally, in the evaluation step, it is first asked whether or not the problem seems solved and the aim is reached. Then, the result is compared with experience or a ”first- hand” expectation. If the comparison is satisfactory, the problem is considered solved. When it is not, the process starts all over again.

Fig. 4 presents a model for clinical problem solving by novices. It serves the making and execution of a treatment plan. Annex 4 contains an example of its use. Since the model is aimed at a novice, the analysis comes first in agreement with the Maastricht model (9, 10). It closely resembles the basic-sciences model (Fig. 3). Here, the global problem (step 1) will generally be presented by the patient: “I want a check-up”, or: “I have a tooth-ache, lower left”, etc. The structuring of the problem (step 2) again comprises the defi- nition of the system: the dentition, a single tooth with or without the periodontium, the nervous system in- cluded, the entire body? The analysis (step 3) comprises an anamnesis, an oral exam, and a listing of all subjects of possibly relevant knowledge that the

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h e x 3 zxample of an open basic sciences problem and how it is reated (Fig. 3) 4s an example of solving an open basic sciences problem, :onsider pulp vitality testing with a cold cotton ball. The !allowing situation is given: a dentist tests vitality of a 2nd molar with an amalgam occusal filling in a 40-year old patient using a cotton ball with some ethyl-chloride.

The global problem (step 1) is to estimate by calculation ie time that it takes before the patient indicates pain, as- iming the tooth is vital. Structuring the problem (step 2) requires a crude drawing I at least the cotton ball with liquid, the tooth with filling, ie blood supply, the nerve, the air in the oral cavity, the rain and the voice. Students have to define the system, i.e., ) draw a boundary in the picture. Inside the system they ave to define subsystems, outside the system they have to Jnsider external effects. One possibility is to include and ave as subsystems the cotton ball, the liquid, and the tooth rith filling. The air in the mouth and the nerve, brain and oice are then external elements. However, other choices are ossible. As aspects, at least heat transport by conduction nd evaporation have to be considered. The system analysis (step 3) then includes a listing of pro-

esses of heat transport and where those occur, of the corre- ponding laws and formulae, and of the quantities involved. hese should at least be the conductivity and heat capacity If amalgam and dentine, heat transport by the blood flow, vaporation rate, heat of evaporation of the liquid, laws gov- ‘ming cooling by evaporation, transport by conductivity, varming up, and the like. Sizes of cotton ball, amalgam, and 00th constituents have to be considered as well as the imount of liquid. Some values of quantities are to be found n handbooks. Others might be left open until the step of ,implifica tion.

The specification of the problem (step 4) might be a phrase ike: “to calculate the time until a pain reaction occurs, when he pulp is cooled by applying a cotton ball with ethyl-chlor- de applied to the occlusal surface, using the laws governing ieat transport”. The aim might be a statement of accuracy, ?.g., a factor of 2.

The simplification (step 5 ) is a very important step. Stu- ients will have to realise that the physics of the problem is gery complicated transport during heating, other heat tram- port than by evaporation and conduction, complicated ge- ,metry, etc. So, they have to decide on rather rigorous assumptions such as: the tooth and filling are rectangular cylin- ders, the sideward heat transport is negligible, the heat transport by the blood flow is negligible, the total evaporated liquid determines the temperature of the filling, and thereafter conduction through the dentine from the pulp determines the cooling of the pulp with its heat capacity (or, different but simpler: the temperature of the pulp is equal to that of the filling), etc. In addition they have to make estimates which may or may not be supported by simple experiments: the amount of liquid on the cotton ball, its evaporation rate, the cross section of the filling-cylinder, the temperature de- crease of the pulp leading to pain, etc. In the end, the simplified problem is a simple cooling down by conduction and/ or evaporation.

The execution of the solution (step 6) involves the insertion of the values of quantities and making of the calculation.

Evaluation (step 7) involves the comparison with the expected value: it should be something of the order of 1-10 s as some students will know from experience.

I - solution

adequate’

(1)

(2)

(3)

no lbs’ probrn’ structure probletn and -system

1 f c 5

analys system and problem

(4) spccify problem, formulate aim

sirtrplif problern, tnatch to nretttod of solution ( 5 )

(6)

(7)

xecute solution I evaluate

Fig. 3. Heuristicfor the training of solving open problems in the basic sciences. In italics are the steps that require decisions (assumptions, choices, estimates)froni the students, steps in regular lettering can be perfornred without personal cfroices. Generally, from the analysis, there bcconres apparent only a single method of solution. The step “evaluate” requires from the student a conrparisott of the results of the solving process with a “jirst-hand“ or “experience” result. In case the problem appears solved, the nrodel is left towards the lejl side; in case it is not solvcd, a new global pro6lenr is present and the solving proccss itiay start agnin.

student has available (Fig. 1). Step 4 specifies the problem and, in agreement with the patient, formu- lates an aim. The latter might be “maintaining the dentition at a price below ...”. Step 5 lists the methods of solution, i.e., possibilities of treatment (including no treatment), that emerged. The assumptions that are needed are made in step 6, as well as the choice of method from the methods list that the student then has to make, Execution (step 7) comprises the treatment, hygiene instruction, etc., as decided. Of course, the evaluation step (step 8) is also present. It comprises a comparison of the result with the problem and aim, and also a retrospective look at the process: did any mistakes occur, did the patient suffer unnes- sessary or unexpected pain and discomfort, was more time needed than expected, etc? If yes, how can it be done better next time? The diagram also shows a few external influences taken from the Amsterdam model (18), such as consultation of specialists, the (financial) wishes of the patient, and. the like. In the actual model, more might be present. Also, feedback loops (18) are left out for clarity.

Fig. 5 schematises the problem solving by a more experienced student. It is based upon the expert thinking, which uses dental problem scripts. There- fore, the listing of possible solutions comes immedi- ately after the structuring of the problem and the system. This is similar to the Malma model (11) and the

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(1)

structure problem and -system ( 2 )

(3)

(4)

( 5 )

problem, choose method of solurion (6)

i analys system and problem I specify problem, formulate aim

consultations

- patient's wishes

t yecute solution

t rvduafe

(7)

Fig. 4. Model for solving a clinical problem by novices. As before, steps requiring decisions are italicised. Note that analysis comes before choice of treatment method. The cues that are extractedfrom the annly- sis and spec$cation of the problem are indicated, and also the niost imporfanf external factors. The list of possible solutions follows straightforwardly from the analysis. This step is therefore not italicised.

engineering model (2) in which potential methods of solution are listed before the structuring to prevent loss of creativity. Feedback loops are indicated in the diagram because expert thinking includes many iterative steps in each of which some information is collected which strengthens or weakens listed hypotheses. Of course, external influences are also present here, but for the sake of clarity, not presented. Other steps outside the feedback loop are similar to those in Fig. 4.

Conclusions and recommendations We have presented models suitable for the teaching of the different types of problem solving that have to be used in a dental curriculum leading to an academically educated dentist. These models are derived from models available in the Iiterature, they are adapted to fit the results of research on problem solving thinking. The underlying models have been practiced in several European dental curricula.

It is recommended that these models are used in 2 ways. One way is the use for solving "paper" problems of non-clinical and clinical nature in groups of students, guided by a non-teaching tutor. Another way is their use by clinical teachers when they discuss clinical problems with students. This will provide a coherent method of guidance for all clinical teachers, a method that stimulates the thinking by the student rather than the following of the teacher's advice on

Annex 4 Example of a dental problem and how it is solved by a novice (Fig. 4)

A 51-year-old new patient presents himself at the dental of- fice. He reports that, when biting on a hard object in the food, a tooth (the 14) broke off. He mentions that he has neglected his dentition for several years because of a busy life and fear of the dentist, but now he had to come because "it looks so ugly" and being here, he might as well have his dentition checked and fixed. This is the global problem, step 1.

The student structures the problem and the system (step 2). The problem is at least twofold: the acute problem of the vacancy and the general problem of having the dentition fixed. The system comprises the entire dentition. Had there been only the acute problem, the 14, its neighbours, and the antagonist might have been chosen as system, but then the rest of the dentition had been an important part of the system environment.

In the analysis (step 3) the student inquires about the dental history (last check-up and treatment, pain and other problems) and asks for smoking, drinking and dietary habits and for problems with the general health. He also asks about the interest of the patient in maintaining and keeping his dentition and his opinions about the financial aspects thereof. Then, the oral cavity is examined. The crown of the 14 is broken away just above the gingiva and shows s ips of secondary caries under a previously present large filling. The student makes a status of the dentition, including vacancies and restorations and primary and secondary caries lesions. This includes bitewings. Of the 14 root, a peri-apical radiograph is made, which shows some signs of peri-apical bone loss and mesial and distal pockets of 7 mm with 2 mm bone loss. Ten teeth show primary or secondary caries, there are some overhanging fillings. The general periodontal situation is characterised by determining the CPITN index. It appears that the value is 3 12/1 1 1. The plaque score is 100%.

The problem is specified (step 4) as a paticnt with some fear for the dentist, but a positive attitude, and an oral cavity with mediocre hygiene and mediocre periodontal problems, a broken 14 with a non-vital root and possibly some peri- apical scar tissue, and several teeth with caries that will require the remaking of 8 fillings and the making of 2 new ones. For these,no special problems are to be expected. The aim is to obtain a reasonable appearance and dental fitness.

So far, there seem to be many options for treatment (step 5): a pulpal pin with a composite crown, a pulpal pin with a metal-porcelain crown, extraction and an implant, a bridge, or a partial prosthesis.

To simplify the problem (step 6), assumptions are necessary. It is assumed that the risk, that the perio/endodontal problem will not be removed by endodontic treatment, is so large that using the root is not appropriate. It is further assumed that the patient is willing to improve his oral hygiene $0 far that the periodontal problems will not increase. That hygiene will improve far enough to merit an implant, is considered unlikely This implies that a bridge and a partial Frame prosthesis become professionally responsible soh- :ions. This and budgets for treatments, are explained to the 2atient. In discussion with him, taking his financial situation nto account, a treatment plan is made that includes oral hy- :iene and diet instruction, the further necessary restorations, ind a frame prosthesis to replace the 14.

In step 7 this plan is carried out. In step 8, the treatment process is evaluated. No problems

Nere encountered and, thanks to the use of local anaesthesia, he patient experienced little pain which decreased his fear. f i e patient is rather happy with the result, although he re- 7orts difficulties in getting accustomed to having a prosthesis.

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global probicm (1)

I Y struct re problem and -system (2) - dental problem scripts list PO ible solutions lrrr

I I Lnnalysf system and problem

(3)

(4)

(5 )

(6)

L +

I

specify problem, formulate aim

choose method of solution i 4

xecute solution

evaluate I t Fig. 5. Modelfor solving a dental problem by experts. Dental problem scripts in the mind of the expcrt are uscd to generate o list of possible solutions. In this procedure, this is a rather subjective step, therefore it is italicised. It comes bdore the analysis of the problem, which is the done in an iterative manner: hypotheses are rejected or confirmed 011 the basis of acquircd pieces of information. This is pictured by feedback loops.

the choice of treatment. Preferably, to stimulate their use by students and teachers, these models should also be used in examinations. In clinical exams, e.g., in making a complicated treatment plan, they should be used as the process guideline.

Only a consistent approach by all teachers towards stimulating the student to think for himself before asking advice will truly enforce a problem-solving attitude that is based on probelm-solving capacity rather than on knowledge of possible methods of solution. Such an attitude will later enable the former student to modernise his methods of practising and to explore new methods of solving the problems in his profession.

Acknowledgements The author is much indebted to Dr. H.W. Kersten (ACTA, Amsterdam) and Professor R. Attstr6m (Mal- mu) for help with providing cited papers, and to I. ten Bosch for helpful discussions about the annexes.

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1990 24: 137-147.

Address: 1.1. ten Bosch Laboratory for Materia Technica Bloemsingel 10 9712 KZ Groningen The Netherlands Tel.: +31-50-3633138 Fax: +31-50-3633159 e-mail: JJten. [email protected]

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Problem solving and problem-solving education in dentistry