Radiography (2006) 12, 34e44

REVIEW ARTICLE

Teaching for more effective learning: Sevenmaxims for practice

Tim McMahon*

The Centre for Teaching and Learning, Woodview, University College Dublin, Belfield, Dublin 4, Ireland

Received 24 November 2004; accepted 22 March 2005Available online 11 May 2005

KEYWORDSTeaching;Deep learning;Motivation;Assessment;Feedback;Learning outcomes

Abstract Starting from the assumption that deep learning, which seeks lastingmastery over a subject, is more desirable in professional education than shallowlearning, which is merely designed to pass academic assessments, this papersuggests ways in which teachers in higher education can encourage the former.Noting that students tend to adopt either a shallow or deep approach in response totheir experiences in the classroom and their understanding of what the assessmentregime requires, it argues that, as a consequence, it ought to be possible to promptmore students to adopt deep learning approaches by manipulating teaching andassessment strategies. The literature on teaching and learning is explored in orderto derive maxims of good practice which, if followed, can reasonably be expectedto promote deep learning and discourage surface learning. It is argued that this willlead to more effective preparation for the real world of professional practice.ª 2005 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.

Introduction

One of the major findings from research intoteaching and learning over the past 25 years hasbeen that the way a student approaches the taskof learning will usually fall into one of twocategories which can be broadly defined as ‘‘sur-face’’ and ‘‘deep’’.1e3 These terms relate to theway in which students seek to deal with thematerial from which they are expected to learn.

* Tel.: C353 1 716 2555.E-mail address: tim.mcmahon@ucd.ie

1078-8174/$ - see front matter ª 2005 The College of Radiographdoi:10.1016/j.radi.2005.03.009

Most often this material is in the form of writtentext or spoken lecture and is known in educationaljargon as the ‘discourse’.1 A surface approach tolearning is one where a student concentrates onremembering the facts and opinions set out in the‘discourse’ with a view, when assessed, to beingable to repeat them in words as close as possible inmeaning to the original.1 A deep approach tolearning is one where a student seeks to graspthe meaning behind the ‘discourse’ e the messagebehind the words.4

Students who take a deep approach to learninghave the intention of understanding, engaging with

ers. Published by Elsevier Ltd. All rights reserved.

Seven maxims 35

and valuing the subject. These are the studentswho will interact most vigorously with subjectcontent by relating new ideas to both existingknowledge and everyday experience. They willtend to read beyond the course requirementsand, most importantly, are more likely to basetheir learning on evidence rather than a passiveacceptance of authority. In contrast, students whotake a surface approach to learning will takea much narrower view of what needs to belearned. Their primary motivation being to learnthat which will enable them to achieve the mark,grade, or qualification they are after, they seek tomemorise what they are taught in order to repeatit when tested. In doing this they tend to concen-trate on detail and consequently risk failing to beable to distinguish principles from examples and,hence, miss the point of what they are trying tolearn simply because they are not looking for it.5

The relevance of this to those teaching onprofessionally-oriented programmes, such as thosewithin diagnostic imaging, is that there is a clearimplication that deep learners will be better ableto apply their knowledge in the complexity of thereal world, where textbook cases are few and farbetween. This conclusion is supported by the studyby Chi et al.6 into the different ways experts andnovices use factual scientific knowledge to solveproblems, which demonstrated that it is possiblefor some people to be able to apply scientificformulae to carry out set calculations in aneducational setting but not be able to carry thisthrough to real-life situations. It also revealed thatexperts initiated problem-solving by abstractingscientific principles while novices tended to focuson the surface features of the problem. Thisstrongly suggests that effective professional prac-tice requires knowledge to be organised aroundunderlying principles rather than surface facts.These finding have been replicated in many otherprofessional and vocational contexts.

The basic expert-novice result, that experts’knowledge is represented at a ‘deep’ level (how-ever, one characterizes ‘deep’), while novices’knowledge is represented at a more concretelevel, has been replicated in many domains,ranging from knowledge possessed by scientiststo (that possessed by) taxi drivers.7(p1)

From this perspective, deep learning is moreeffective than surface learning because, unlike thelatter, it can lead to the mastery necessary in real-world situations.

From the point of view of students facinga crucial examination, however, surface learningmay appear to be the most efficient way of

securing the best possible marks in a limited time-frame. In an age where

Education and related qualifications determine toa large extent the life chances of people.8(p4)

this is not an unreasonable choice for studentswhen faced with situations where the assessmentsystem actually rewards such behaviour. The prob-lem is that, in the case of professional trainingprogrammes, it is likely to be very bad for theprofessions they ultimately enter.

What is often not fully understood is that deepand surface approaches to learning are not, gen-erally speaking, stable traits in individuals. Rather,although some students have a natural tendency totake a deep or surface approach, most studentswill usually make strategic decisions about whichapproach to take depending on their experiencesin the classroom and their perceptions of what theassessment regime will reward.9e12

Logically, it follows that it should be possible toprompt more effective (deep) learning by manip-ulating teaching and assessment so as to maximisethe perceived rewards for taking such an ap-proach, and, conversely, to minimise those fortaking a surface approach. Further, there is con-vincing evidence of a reciprocal positive associa-tion between adopting a surface approach and theincidence of both learning and non-academicproblems in students.13 In turn,

Academic and non-academic problems can causefear of failure, lack of confidence and poorperception about their ability which leads to poorstudy approaches.13(p8)

This strongly suggests that those interventionsthat can be expected to prompt deep learning arelikely to be particularly helpful to students whohave some form of learning disadvantage. Theremainder of this paper presents seven maximsfor designing a teaching and assessment regimethat, on the basis of the best available research,should create a learning milieu particularly con-ducive to deep learning.

Main body

Most readers of Radiography, coming from a scien-tific background will, when conducting or review-ing research, be used to a level of evidence thatallows for definitive proof or, at the very least,overwhelming probability. Unfortunately, when itcomes to research into educational practice, suchlevels of surety do not exist. Like all research intohuman activity, educational research is heavily

36 T. McMahon

context-bound e i.e. the results are not univer-sally generalisable. Rather, educational researchcan document and suggest the likely causes andcorrelations of a particular set of events ina particular place and at a particular time.Whether these causes and correlations will berelevant to other situations will depend on thecloseness of context e and, of course, the greaterthe number of studies that show similar humanactions leading to similar results, the greater theprobability that these actions will lead to theexpected or intended results. Nonetheless, eachevent involving human actions is unique and, whendealing with the vagaries of human volition, it isalways possible that seemingly similar sequencesof events will culminate in very different out-comes. That educational research deals in proba-bilities and not certainties must never beforgotten. This axiom notwithstanding, there ex-ists a considerable body of research into howstudents learn and how this can be influenced bythe actions of those responsible for the design,management and delivery of courses. There is alsomuch research into human behaviour outside ofeducational settings which has an obvious orlogically inferable relevance to teaching andlearning theory. The following seven maxims werederived from the extant literature and, if fol-lowed, are more likely to prompt deep learningthan surface learning. How the maxims relate tothe literature is shown either by reference ordirect argument.

1. Present students with a workload thatthey can see is manageable within thetime constraints of the programme

Workload refers to the notional time an under-graduate of average ability should expect to taketo complete the required learning outcomes of thecourse or module. Under the European CreditTransfer System, the convention is that 60 creditsmeasure the workload of a full-time undergradu-ate during one academic year and that thisrepresents between 1500 and 1800 hours of work.Thus one credit should take between 25 and 30 hfor a student to complete.14 This means 25e30 h intotal, including all class contact, assignment workand private study. All undergraduate programmeswithin the EU should now conform to this workloadnorm.

The relationship between workload and studentapproaches to learning, however, is complex, notleast because there are two variables at workrather than one. Actual workload can affect

whether students adopt a deep or surface ap-proach; but so too can the perception of theworkload.

As Kember15 has pointed out, much of theliterature suggests that students’ perception ofworkload affects their behaviour far more than anyobjective measure of hours spent in study. Howclosely this perception relates to actual number ofhours worked is debatable but appears likely to beminimal.15 There is some evidence that the re-lationship between perception of a high workloadand surface approaches is reciprocal rather thanone-way.15,16 It does, however, seem establishedbeyond reasonable doubt that when students’workload is perceived by them to be heavy, theywill often attempt to cope by adopting a surfaceapproach to learning.15,16 Discussing various stud-ies into this phenomenon, Kember concluded that

it would be reasonable to conclude that excessiveperceived workloads can have a negative influenceupon student learning through being associatedwith a tendency to encourage surface approachesto learning.15(p168)

Research also suggests that an actual heavyworkload can have a detrimental effect on thequality of learning, tending to encourage a surfaceapproach.17,18 Chambers19 went so far as to sug-gest that reasonable workload is a pre-condition ofgood studying and learning.

The research into both real and perceivedstudent workload leads to the conclusion that, indesigning and managing courses, academics inhigher education should:

a. Be scrupulous about keeping to the ECTSworkload guidelines (which may include in-vestigating the actual amount of time studentsspend both on out of class assignments and onstudying, and adjusting what is set accordingly)

and

b. Gather students’ feedback on their percep-tions of workload and make changes in theactual workload, the timing of set tasks or theway the workload is communicated to studentsas seems appropriate.

2. Design out Information overload

Information overload is related to, but differentfrom work overload. It results from a situationwhere a student feels that s/he has just too muchinformation to deal with. In such situations people

Seven maxims 37

often experience cognitive dissonance and seek tofilter incoming information by:

a. Reducing the amount of incoming informationdeemed to be relevant by, for example,uncritically deferring to known authorities(e.g. the lecturer).20

b. Valuing impressions over actual knowledge eespecially visual impressions so that, forexample, sources with effective images aretreated as superior to those without them.20

c. Allocating less time to each individual task ofinformation gathering and information process-ing.21

All three of these coping strategies are associ-ated, by definition, with a surface approach tolearning and, in today’s information-rich society,constitute an ever-present danger.

Buchanan and Kock22 examined the perceivedexistence of information overload and its effectson decision-making among MBA students from NewZealand and the USA. They concluded that there islittle doubt that the phenomenon exists and foundthat approximately 60% of the reasons given forinformation overload relate to task factors eprincipally information and time-pressure issues.The remaining 40% of reasons are attributable toindividual factors such as a lack of organizationalskills, decision-making style and preferred learningstyle.

Taken together, the research on informationoverload suggests a crucial need to ensure thatstudents are able to evaluate information con-sciously and critically in order to select that whichis most relevant. Academics can help their stu-dents develop such skills by:

a. Putting in place effective guidance as to whatsources of information might prove most usefulto students; this should include ranking in-formation sources in layers (e.g. required,recommended, useful).

b. Not overwhelming students with the amount ofinformation presented at any given time.Layering of information is, again, appropriate.

c. Ensuring that students have access to someform of information management training.

3. Ensure that the students have a clearunderstanding of what is required of them

The importance of making both the intendedlearning process (what the students can expectto have to do during the course) and the intended

results (what the students can hope to gain by theend of the course) explicit for students and staffalike has long been recognised.23e25 Further, thereis research to suggest that this is particularlyimportant for those students most at risk ofdropping out.26

As part of the strategy to achieve this, the useof ‘learning outcomes’, is recommended as thepreferred ‘‘middle ground’’27 between too loose orover-generalised ‘‘learning aims’’ and over-pre-scriptive ‘‘learning objectives’’. This distinction isimportant because, although the centrality oflearning outcomes in constructing curricula thatencourage effective learning is well estab-lished,9,28,29 there is still much debate over theissue of to what extent defining learning outcomesin advance limits both teacher and student crea-tivity and, hence, the potential depth and breadthof learning.30,31 This is as true in the medical andparamedical curriculum32 as elsewhere e a usefuloverview of this and other issues in relation tomedical and allied disciplines being given byRees.33

None of the criticisms of using outcomes todefine the curriculum, however, seriously seek tochallenge the idea that students perform betterwhen they are clear in their own minds about whatis required of them by the assessment system. Astwo of the most persuasive critics of outcomes-based education put it,

We are not denying the need for educators toindicate to, or discuss with, their pupils what is tobe covered in a teaching session or what they areexpected to learn, but we are claiming that theuse of learning outcomes as currently understood,can be damaging to education.31(p222)

The phrase ‘‘as currently understood’’ is crucialbecause the authors (Hussey and Smith) who use itcome from a UK background and are reacting,quite understandably, to the over-prescriptivenature of learning outcomes as used in much ofthat country’s education system. Hussey and Smithdo, however, concede the point that,

Learning outcomes have their value whenproperly conceived and used in ways that respecttheir limitations and exploit their virtues. .31(p222)

Hussey and Smith’s criticism is not of learningoutcomes as an organising principle of curriculumdesign but of those who ascribe to them attributesand functions that they cannot possibly have;namely, the ability precisely, and in advance to,specify all the transactions that should take placewithin a given learning situation and the functionof providing a checklist which, if met, indicates

38 T. McMahon

Table 1 Comparison of outcomes and objectives

Learning outcome Equivalent learning objectives

At the end of this moduleyou will be able tominimise the effects ofvisual noise in clinicalimages.

At the end of this module you should be able to:Recognize visual noise in clinical images.Describe the general relationship of image noise to image detail.Describe the two major sources of visual noise in radiographic images.Explain how the random distribution of X-ray photons produces image noise.Describe the relationship of quantum noise to X-ray receptor exposure.Discuss the concept of the quantum sink.Discuss the disadvantages of using radiographic receptors with very highsensitivity or speeds.Explain why quantum noise might be more of a problem with digitalradiography than with film screen radiography.Explain how blurring reduces image noise.Describe the relationship between digital image pixel size and noise.Describe how digital image processing can be used to reduce image noise.Describe how the process of averaging a series of images can be used toreduce noise.Explain why image display contrast has an effect on the visibility of noise.

conclusively that these transactions have done allthey can or should.

It is in order to avoid such a misuse of learningoutcomes that the D’Andrea model of learningoutcomes is recommended.27 Following on fromWalker,34 she sees outcomes as wide statementsof intended learning occupying

.a middle ground between statements of learn-ing which are considered over-generalised (learn-ing aims) and those which are over-specified(learning objectives).27(p28)

Table 1 gives an example of the differencebetween learning outcomes and learning objec-tives in the D’Andrea model.

Adopting this usage, the over-prescriptivelearning outcomes complained of by Smith andHussey are appropriately re-defined as learningobjectives but, unfortunately, it is still common-place in the literature to use objectives and out-comes interchangeably or, indeed, to use thecompound phrase ‘outcomes/objectives’.

How best can such ‘‘learning outcomes’’ (in theD’Andrea sense) be expressed?

Dearing24 recommended that curriculum design-ers in Third Level Education write learning out-comes that can be categorised as being in one orother of the domains of ‘knowledge and under-standing’, ‘‘key skills’’, ‘‘cognitive skills’’, and‘‘subject specific skills’’ (sic).

In the search to encourage deep learning,however, Bloom’s taxonomy e as revised byAnderson et al.35 e probably remains a more usefultool for identifying higher-order outcomes andobjectives.

The list of verbs in Table 2 (below), adaptedfrom Anderson et al.35 can be used to frameoutcomes and objectives at the different levelsof Bloom’s (revised) taxonomy.

A considered rewording of outcome statements,so that they require the demonstration of compe-tence at the higher levels of the taxonomy, can helpprompt deep approaches to learning because,together with the additional strategies mentionedbelow, it can help focus the students’ minds onactivities associatedwith higher-order learning. It isimportant to note here that the argument is thatdeep learning must include demonstration of someachievements that can be categorised as being inthe higher levels of Bloom’s taxonomy; it does notmean that all learning must be so classified. Aglance at the verbs in Table 2 at the lowest levelof the taxonomy (Remembering) will illustrate just

Table 2 Verbs appropriate to different levels ofBloom’s (Revised) taxonomy35

Level Examples of appropriate verbs

Synthesis/Creation Hypothesise, Design,Construct, Plan, Invent, Devise

Evaluation Judge, Test, Monitor,Detect, Co-ordinate

Analysis Differentiate, Discriminate,Distinguish, Deconstruct

Application Apply, Use, DemonstrateUnderstanding Clarify, Illustrate, Categorise,

Predict, Compare, ContrastRemembering Recognise, Identify, Define,

Retrieve, Recall, Record

Seven maxims 39

how important learning located at such levels canbe in fields such asmedicine and allied disciples thatrequire extensive basic knowledge as a pre-requi-site of higher-order professional decision-making.

However useful learning outcomes are in help-ing to inform both teachers and students about theintentions of learning programmes, avoiding themistake of elevating them to a status beyond theirreal utility requires that defining and promulgatingthem is seen as only one aspect of the process ofinforming students of what they should be able toexpect. Other vital aspects are comprehensive andclearly written course documents (which the prin-ciple of inclusivity requires should be available in avariety of appropriate formats); regular teacherestudent contact sessions where both the learningoutcomes and assessment criteria are explained,explored and exemplified; and appropriately-timed formative assessment tasks which are usedto monitor the quality and extent of studentunderstanding of what is required to maximisetheir grades. Students also need extensive guid-ance on how to achieve these learning outcomes.It is one thing to know what is required; anotherto know how to achieve this. Actions by teachersto ensure that students know what to do andhow to do it, should form a more-or-less continu-ous dialogue between themselves and their stu-dents that defines, in practice, the inevitablyimprecise and context-bound learning outcomes.Hussey and Smiths’31 criticisms are, after all,essentially, a restatement and reapplication ofthe insightful critique of competence-based as-sessment produced by Wolf,36 who comprehensivelydemonstrated that specifications of what shouldbe learned are, inevitably, ambiguous and that themeanings of learning objectives have to be foundwithin the shared understandings of the peopleinvolved in the situation. In terms of teaching andlearning situations, this means that it is not enoughjust to write learning outcomes and give furtherexplanations of what they mean in a course hand-book; it means all courses should have provisionfor an exploration, in class, of the meanings andimplications of the intended learning outcomesand, in particular, how these determine what isrewarded by the assessment system. Establishingthis link in the minds of students is vital because,for students, assessment defines what is importantin the curriculum10(p187) and they will learn whatthey think will be assessed.9(p140) Thus, in practice,the need to align assessment practices with learn-ing outcomes (a key pillar of Biggs’ alignedcurriculum structure9), means that maxim 3 mightequally well be written as make sure studentsknow what the assessment regime will reward.

Further, the obvious corollary is e and, if you wantto encourage deep learning, make sure that theassessment regime rewards higher order learning.This, therefore, is maxim 4.

4. Ensure that the assessment regimerewards evidence of higher orderthinking and learning

Research into student learning very strongly sug-gests that the way learning is assessed is probablythe single most important factor in influencingwhat and how students learn in general, andwhether they adopt a deep or surface approachto learning in particular.9e12 It follows that assess-ment regimes that encourage surface learning willundermine any and all other attempts to encour-age deep learning e or, as Biggs puts it

.students learn what they think they will betested on. This is backwash, when the assessmentdetermines what and how students learn morethan the curriculum does. In a poorly alignedsystem, where the test does not reflect theobjectives, this will result in inappropriate surfacelearning.9(p140)

It is not really necessary to draw upon extensiveresearch to work out what strategies one needsto adopt in order to ensure that an assessmentregime does not encourage surface learning. Look-ing at the definitions of surface and deep learningpresented earlier:

A surface approach to learning is one wherea student concentrates on remembering the factsand opinions set out in the ‘discourse’ with a view,when assessed, to being able to repeat them inwords as close as possible in meaning to theoriginal. A deep approach to learning is one wherea student seeks to grasp the meaning behind the‘discourse’ e the message behind the words.

One merely has to answer the question:

What would I do if I wanted to try to ensure thatmy students did not think for themselves butrather concentrated on uncritically repeatingwhat they learn from established authority?

and then do the opposite.Once it is realised that assessment is probably

the main, and certainly the key, determinant ofwhether or not students adopt a surface or deepapproach then, once one has asked oneselfthe above question, it becomes frighteninglyeasy to see why much conventional assessmentleads students into a situation where they areunable or unwilling to think for themselves or to

40 T. McMahon

transfer knowledge and skills from one area toanother.

An important further dimension is offered byBiggs.9

The effects of assessment on learning are usuallydeleterious. This is because assessment is treatedas a necessary evil, the bad news of teaching andlearning, to be conducted at the end of all thegood stuff. Students second-guess the assessmentand make that their syllabus, and will underesti-mate requirements if the assessments’ tasks letthem, so they get by with low-level learningstrategies.9(p164)

Biggs’ answer is to ensure, firstly, that theassessment regime is aligned with the learningoutcomes (and that teaching methods are alignedwith both); and, secondly, that the outcomesdescribe, and the assessment regime tests for,higher order learning. (Biggs has his own taxonomyof learning e but this can be aligned with that ofBloom as shown in Table 3.)

We now have two actions that academicscan take to help make the assessment regimemore likely to encourage a deep approach tolearning:

a. Answer the question How can I use assessmentto encourage students to adopt a surface ap-proach to learning? and then do the opposite.

b. Write assessment criteria that are located inthe higher levels of Bloom’s taxonomy, usingthe kinds of verbs shown in Tables 2 and 3.

Research also suggest other tactics:

c. Increase the amount of choice students haveover assessed tasks.Wlodkowski37 has convincingly argued thatchoice is a critical component of the motivationto learn. This is, at least partly, because somesense of control is seen as essential to self-esteem and self-efficacy38 e themselves keyfactors affecting motivation. It seems logical,

therefore, to infer that the total absenceof choice over what is assessed and how it isassessed is likely to detract from self-esteemand self-efficacy. This is not to say thatunlimited choice is good e this can lead toa kind of information overload where studentsdon’t know where to start; it also creates thepossibility that students will choose to avoidtasks that their teachers can see they need toaccomplish in order to move on to the nextstage in their learning. It seems probable,however, that some choice in the area ofassessment will bolster self-esteem and self-efficacy which, when combined with othertactics, ought to help encourage students inthe belief that deep learning is both possibleand likely to pay dividends. When studentshave a sense of ownership and control over howand when they are assessed, they are muchmore likely to adopt a deep approach tolearning.39,40

d. Reward doing something new/showing theability to be self-critical/showing the abilityto learn from mistakes/showing awareness ofdifferent ways of achieving the same thing e inother words, reward metacognition.Metacognition is the act of thinking aboutone’s own thinking and taking deliberatecontrol of one’s own learning; it necessarilyinvolves being able to analyse critically per-sonal decision-making. As this facility is de-veloped the quality of learning tends toincrease.41 Because this form of knowledge isimportant in terms of how it enables studentsto become self-directed learners, it is mucheasier to assess informally and formatively.42 Itis possible, and desirable, nonetheless, toinclude within the summative assessment re-gime instruments capable of allowing studentsto demonstrate a knowledge and understand-ing of how they came to conclusions or madechoices and that they have learned from ananalysis of, or reflection on, these actions. Port-folios and reflective diaries are two commonly

Table 3 Alignment of Bloom’s35 and Biggs’9 taxonomies

Level Examples of appropriate verbs Level

Synthesis/Creation Hypothesise, Design, Construct, Plan, Invent, Devise Extended abstractEvaluation Judge, Test, Monitor, Detect, Co-ordinateAnalysis Differentiate, Discriminate, Distinguish, Deconstruct RelationalApplication Apply, Use, Demonstrate MultistructuralUnderstanding Clarify, Illustrate, Categorise, Predict, Compare, ContrastRemembering Recognise, Identify, Define, Retrieve, Recall, Record Unistructural(Not Knowing) (Misses the Point) Prestructural

Seven maxims 41

used tools but the author’s experience overmany years suggests that both of these requirestudents to have a significant amount ofmetacognitive skills before they are able touse them effectively. It is often better to beginassessing metacognition through short pieces ofwriting (200e300 words) which are discussedeither in class or in an on-line forum, so that theskills of critical reflection (an essential compo-nent of metacognition) are developed throughpractice and immediate feedback. The use oflearning contracts in a process which requiresstudents to plan how they will approach anassessed task and have some input into decidinghow it will be assessed also tends to promotemetacognition.43 Rewarding such activitieswithmarks that count towards the final gradeencourages more serious participation e andhas the bonus of making the assessment overtlypart of the learning.

e. Involve students in the assessment processusing peer- and/or self-assessment.That both peer- and self-assessment canencourage deep learning is reasonably wellestablished44,45; but it is probably best usedwithin a reflective framework that includeslearning contracts that require students toproduce evidence of learning in reasonablysmall stages.46(p83e87) Peer- and self-assess-ment are particularly valuable in promptingdeep learning where the instruments aredesigned to cause them to evaluate their owncognitive processes.47,48 This would suggestthat students be asked to justify their deci-sions and conclusions as well as make them.This is something that already exists in mostdiagnostic imaging courses, so extending thepractice should not be too much of a problem.Lowe47 suggests students be set tasks wherethey analyse their performance after beinggiven the results of tests, something equallyeasily incorporated into the diagnostic imagingcurriculum. The known benefits of a sense ofownership and choice37,38 suggest that studentinvolvement in assessment extends to beinginvolved in setting assessed tasks and thecriteria by which they will be marked (bothof these can be done within the frameworkprovided by learning contracts).

5. Require active participation

Active learning is much more likely to encouragea deep approach to learning than that whichrequires the passive reception of knowledge.49 It

has also been found to promote academic achieve-ment and enhance motivation.50 The work ofKolb51 suggests strongly that when a requirementto engage in critical reflection is part of thelearning process, the resultant learning is deeper,more effective, more transferable and greaterautonomy is encouraged. Johnston et al.52 foundthat active learning in collaborative groups en-hanced critical thinking.

Active learning can be prompted by the regularuse of collaborative learning groups and throughindividual projects linked to learning contracts.43

An example of a whole-curriculum approach toactive learning is the adoption of problem-basedlearning.53

Similarly, exercises requiring the writing (andreading) of comments aimed at sharing conceptualknowledge with their peers and teachers promotecognitive and metacognitive skills.54 Having towrite narratives that communicate ideas effec-tively encourages students to develop and displaythe depth of their knowledge, their organizationalskills and their reflective insights, thus promotingthe ability to explore new ideas and concepts.55

6. Ensure that students have as muchchoice as possible

That students will be more likely to adopt a deepapproach to learning a subject if there is, at least,some element of choice available to them is asapplicable when considering learning activities ingeneral as it is when considering assessment asabove. Choice may be particularly important foradult learners, for whom the ability to makemeaningful choices in learning tends to be associ-ated with a sense of self-esteem and self-worth.37,38,56

7. Give ‘‘smart’’ feedback to students

The link between feedback on student work(formative assessment) and subsequent perfor-mance is hard to pin down.

Timely feedback has been found to be particu-larly important in motivating students to partici-pate in virtual learning environments57 and givingfeedback early in a course has been shown to havea beneficial effect on retention rates.26 It alsoseems that feedback is most effective (especiallyfor weaker students) if it encourages the adoptionof specific and realistic learning goalse i.e. enablesrecipients to focus on specific personal improve-ments necessary to improve their performancein the future26 and which are within their grasp.58

42 T. McMahon

Apart from this, the relationship is anything butclear e not least because the conceptof ‘‘feedback’’ is somewhat under-developedwithin the literature59,60; most of the advice con-tained therein being ‘‘deduced’’ from the psycho-logical principle that feedback is.

.most effective when it is timely, perceived asrelevant, meaningful, encouraging and offers sug-gestions for improvement that are within a stu-dent’s grasp.58(p51)

Any internet search will easily find several sitesoffering advice on feedback but most of them arederived or deduced from the above.

Hodgson and Bermingham’s61 recent (2004)study of law students in two UK Universitiesgoes a little way beyond this and offers oneof the best checklists for designing feedbackpractice.61(p36e38) This checklist is highly recom-mended for those looking for evidence-basedguidelines on how to give effective feedback.

A current (2005) and ongoing research projectinto effective feedback is being conducted by theUK Higher Education Academy62,63 which, as wellas giving an up-to-date theoretical frameworkwithin which feedback (formative assessment)can be studied and improved, proposes a set ofseven principles for good feedback.63(p6),64 Theseprinciples can be used as a shorter but broaderchecklist than that of Hodgson and Berminghamand, are again, highly recommended. Teachers inHigher Education would find it worth studying bothand selecting whichever best fits their way ofworking.

‘‘Smart’’ feedback can be defined as that whichis given in student-friendly language and is tightlyfocussed on achieving at least one (but preferablymore than one) of the following things:

a. Informing students of essential or useful know-ledge about their subject which their worksuggests they do not know, do not fully under-stand or could make good use of in future work.

b. Informing students of essential or useful pre-sentational practices that would improve thequality of their work (e.g. proper use ofreferencing).

c. Encouraging students to pursue a particularavenue of study or produce a particular pieceof work that will benefit their future perfor-mance.

d. Encouraging students to engage in dialoguewithteachers or fellow students (peers) that will bemutually beneficial to the parties concerned.

e. Developing within students an understandingof what constitutes good performance.

f. Developing within students the skills to criti-cally evaluate their own work.

g. Encouraging within students a sense of self-esteem and positive motivational attitude.

Conclusion

Deep and surface approaches to learning are notstable traits in individuals but are influenced, atleast in part, by students’ experiences of learningand their perceptions of the requirements of theassessment system. It is, therefore, possible todesign into courses factors that will have a positiveinfluence on the choice of learning approach.Similarly, it is possible to adopt teaching strategiesthat promote deep learning and discourage surfacelearning. Despite the fact that research intohuman behaviour can only produce evidence thatis context-bound, the literature contains a suffi-cient body of knowledge to enable us to bereasonably certain of what some of these factorsare and what some of the teaching strategiesshould be. The seven maxims presented in thispaper are derived from the literature and providea theoretically sound starting point for academicswho wish to take the lessons from research intoaccount when designing their courses and planningtheir teaching. They should, however, be used asa guide not a definitive set of rules. Given theunique set of people and circumstances that existson any programme of learning, the experience andknowledge that teachers have of their own con-texts must be used to mediate design and planningdecisions. Similarly, there is no substitute forprofessional experience when making decisions-in-action in the classroom or lecture theatre.Nonetheless, the maxims and the associated rec-ommendations for practice are offered as an up-to-date distillation of research-backed knowledgethat can be used as a reliable guide to practice inteaching and learning.

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