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Teaching Insights from Adult Learning TheoryCassandra Volpe Horii

ABSTRACT

How do undergraduate and graduate students learn? How can we use what we know about the learning process to teach

more effectively? While the research has yet to connect fundamental processes in the brain to what we do as teachers with

certainty, the past half-century of work on adult learning has produced several useful theories that can inform instructional

choices. This article provides an overview of three learning models that yield insights into teaching practice—novice/expert

behaviors, cognitive development, and learning styles—along with ways in which instructors can draw on these models

in course planning and classroom teaching. Application of the theories toward refinement, reduction, and replacement of

live animals in the veterinary medical curriculum is also discussed.

Key words: adult learning theory; teaching strategy; cognitive development; novice–expert difference; learning style

INTRODUCTIONWorking at a center for teaching and learning, I frequentlyhear faculty members and other university instructors ask,‘‘What is the best way to teach my course?’’ It’s a reasonablequestion. In most of our scientific and scholarly pursuits, weanswer questions based on prevailing theories and currentexperimental evidence. But when our question is howto address the needs of adult learners, science comes upshort. Though neurobiologists, psychologists, and specia-lists in education continually make advances that enricheducational practice, it is still difficult to assemble anunbroken chain of logic or evidence from the science of thebrain to best teaching practices.1–3 The kind of controlledexperiments that are possible in physics and medicinesimply can’t be done well with human students in realisticacademic settings; likewise, we can’t construct an adequatemodel or simulated ‘‘student’’ to help us test teachingtechniques in a controlled way.

We might give up there and simply revert to the convenientlists of tips and tricks that are so often handed down on thetopic of teaching. I think we can do better. While we may notbe able to link up our teaching practices precisely to thefunctioning of our students’ neurons, we can use acombination of models of adult learning, each with particularstrengths and limitations, to understand our students’behaviors and needs more deeply, and we can design ourteaching strategies based on this understanding. We canproceed even in the absence of any Grand Unified Theoryof Teaching.

In this article, we will begin to build the kind of frameworkfor instructional choices that is based on educationaltheory while simultaneously recognizing its limitations.The three areas of adult learning theory that we willdiscuss—differences between novices and experts, modelsof cognitive development, and styles of learning—havea broad base of background literature spanning the pasthalf-century; they complement one another, and theyactually help in the classroom. I find over and over againthat these ideas provide insight into some of the mostcommon challenges that instructors face in their efforts to

promote deep and long-term learning in their students.Because these three bodies of research present differentperspectives on, or models of, the same thing—studentlearning—we can use them as complementary tools withwhich to reason about our students’ experience and solveteaching problems.

In the context of a topic of particular concern to veterinaryeducators—refinement, reduction, and replacement of ani-mals in veterinary medical teaching—the frameworkpresented here provides both an opportunity and amethod for faculty to transcend their own learning historiesand find new solutions to this concrete educationalchallenge.

DIFFERENCES BETWEEN NOVICES AND EXPERTSAs experts in our fields, we may have the impression thatwe have attained that status simply by knowing a lot.Indeed, the transformation from novice to expert tends tounfold so slowly and gradually that we are generallyunaware of the profound differences between how we usedto learn and how we learn now in our field of expertise.We conveniently experience a sort of expert amnesia abouthow difficult and impenetrable things used to be.

For those of us who regularly drive a car, it takes somediligent remembering to connect the effortless, automatedactions we now perform in the background of ourawareness (the precise clutch/gas-pedal action needed toshift gears; the exact calibration of degree of turn on theroad to degree of turn on the steering wheel) with our firstattempts behind the wheel. More importantly, we rarelypause to consider how much easier it now is to useour ability to drive for novel purposes—to follow ourpassenger’s directions to a new destination, hear detailsfrom a news broadcast, or sing along with pop tunes onthe radio—while successfully managing basic drivingoperations. And learning to drive is far simpler thanmastering the physical skills, information content, andproblem-solving capacities needed in the medicalprofessions.

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It turns out that novices and experts, like new andexperienced drivers, actually learn differently. This is thebig surprise from the research on novice and expertbehaviors that teachers need to understand: fundamentally,knowing more changes the way we know.

Research on novice/expert behaviors has been going on in awide variety of contexts (e.g., playing chess, solving mathand physics problems, interpreting x-rays, writing, andteaching) since the 1960s. Here, rather than reviewingliterature that is discussed very well elsewhere,4 I willhighlight the common findings to emerge from this body ofresearch and connect them to teaching practice—our maingoal in this discussion.

To do that, let us begin by looking briefly at a common set ofskills and knowledge that veterinary students need toacquire: clinical procedures. Learning and organizingclinical procedures one at a time, as discrete operations, isa bit like the layout of a physics textbook (a key field inwhich novice/expert differences have been investigatedin depth)4—projectile motion in chapter 2, circular motion inchapter 3, springs in chapter 5, and so on. No doubt onewill encounter professional circumstances in which itis convenient to have information ‘‘chunked’’ in thisway—one will have to do a particular operation ona particular animal, much as a physicist may have to solvea problem about a mass attached to a spring. It seems like areasonable way for teachers to present the material, andfor students to begin organizing it.

But research on experts in many fields indicates that theirextensive prior knowledge and accumulated practice in realcontexts leads them toward a deeper way of organizingtheir knowledge. To return to our physics example, unlikethe textbook, and the novice students who use it, expertphysicists tend to think of the problems they encounternot as being about the projectiles and circles and springs(the obvious things involved) but, rather, as being aboutthe deeper underlying laws (far more abstract)—to anexpert, different problems about projectiles, circles, andsprings may all really be about conservation of energy, andtherefore will all be solved in much the same way.Experts work with concepts more than with appearances.

Along the same lines, expert veterinarians may see beyonddiscrete procedures and instead think about the commonpsychomotor skills (e.g., force regulation, tactile patternrecognition, instrument handling) involved in manydifferent procedures.5 The deeper organization of bothphysicists and veterinarians makes it much easier to learnnew information—one’s real weaknesses and areas forimprovement are apparent, and one’s learning can morereadily be applied to superficially novel situations involvingfamiliar deep concepts, such as developing a new clinicalprocedure, adapting to unexpected complications during anoperation, or, for the physicist, solving a research probleminvolving conservation of energy in a complex system.

Indeed, the existing research indicates that experts have aneasier time retrieving and using their knowledge. They tendto consider fewer solutions or approaches before settling onone and to carry out their work more effectively.4

We usually call this ‘‘intuition.’’

This fundamental difference between novices and expertsprobably seems reasonable, but for a teacher, the difficult

and crucial thing is to overcome one’s expert amnesia.‘‘Intuition’’ frequently leads instructors teaching in theirfields of expertise not only to skip but to forget entirely thecrucial steps of organizing knowledge, retrieving theneeded concepts and skills, and executing solutionsand procedures that novice students accomplish only withgreat concentration. It is not an easy thing for experts towake up to the fact that novice learners have notinternalized the same deep ways of knowing.

Once we do, we are in a good position to capitalize onnovice behaviors. We can stop trying to get rid of them,or being annoyed by them, and begin seeing them asimportant indicators of the diligent efforts of novices whoare in the process of learning. Strategies for teaching thatacknowledge and integrate novice/expert differences arediscussed below and summarized in Table 1.

First, as teachers, we can be aware that novice learners willcome in with a set of ideas and expectations about thesubject matter that are sometimes (often) wrong—theirmisconceptions.2, 6 Novices know a lot less than experts,but, as adult students, they are far from the blank slates wemay wish for. Rather than ignoring the existing scaffoldingof students’ knowledge, we can use it to help them learnnew material: in other words, correct its structural and otherflaws by finding out what and how students are thinking, sothat explanations can reveal the inadequacy of their currentviews. While we are helping students build a deeperorganization structure for what they are learning, remindingthem of what they already know and how it fits into thenew concepts being taught (touching on informal priorknowledge and concrete examples from students’backgrounds) can help them integrate new information.

Since novices are unlikely to put discrete bits of informationand skill sets together as coherently as experts do, we canalso help them ‘‘bootstrap’’ their way to more sensibleschemes. Teachers often overlook the basic step of sharingtheir own ‘‘deep structure’’ with students, sometimesbecause they have had no reason to articulate it or map itout for themselves (it is, after all, just part of their‘‘intuition’’) and sometimes because of a belief that studentsshould be able to figure it out for themselves. But whyshould one’s expert conceptual scheme be hidden fromstudents? In fact, creating and referring often to an outlineor concept map driven by an expert organizational scheme,throughout a course or curriculum (not just once atthe beginning), can help students avoid clinging to thesuperficial characteristics that may be a root cause of theirless efficient learning.6

We can also show students that their obvious way oforganizing things is not quite right by drawing analogiesbetween examples, cases, and problems with common deepconcepts or skills but different superficial features.Also known as ‘‘analogical encoding,’’ this approach helpsstudents focus on what the expert already knows to be mostimportant but the novice might otherwise overlook.7

By presenting at least two examples together and guidingstudents through the process of identifying the deepconcepts/skills involved, we are teaching them to becomeexperts.

Of course, one can also simply ask questions that requiredeeper organization from students.6 If we always ask

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questions about chapter 3 in a clump, separate fromchapter 4, and so on, there is little motivation for studentsto work on a more expert way of learning the material. Onesolution is to integrate throughout the course (not only on thefinal exam) concepts and skills that students first encounterseparately. It is also possible to change the context (thesuperficial appearance) of problems or questions that usethe same underlying concepts and skills. ‘‘How’’ and ‘‘why’’questions can also encourage deep-structure thinking.

The above strategies address what students are learning(the content), but recall that novices typically have notinternalized how to solve the problems (the process) either.In my experience, this is one of the most crucial placeswhere experts may forget to communicate the vitalinformation that novices need, precisely because they areexperts and have made the process automatic. It is possibleto make the process explicit, and to guide students throughit explicitly (far more times than seem necessary to mostteachers), so that they begin to internalize it. This mayrequire some careful thought by teachers: How is it that Imake a diagnosis? What information do I gather first?What additional questions or information do I need next?Do I form a working hypothesis to test? What if I have morethan one plausible diagnosis in mind? Since we know thatnovices are grappling with both the process and the content,giving them a leg up by naming the steps, perhaps using

memory aids (mnemonics, visuals) to help them rememberand apply it, can help. Finally, novices need to practice theprocess and receive feedback from their instructors.6

Novice/expert differences become crucial when weconsider the use of animals in veterinary medical education.Ideally, one would want students well on the path ofinternalizing the content and processes needed for diag-nosis, treatment, and any procedures to be practiced withanimals, so that they can focus on adapting to the novelaspects of the realistic context or on practicing thepsychomotor skills involved. Teaching in ways that helpstudents toward expertise makes it possible to implementrefinement, reduction, and replacement. If the goal is forstudents to get the most learning from fewer encounterswith live animals, it is important for them to spend less timeusing the inefficient, superficial ways they might organizetheir learning and approach problem-solving if left to sort itout for themselves. Rather than being a sign that today’sstudents are ‘‘soft’’ or less capable than the generation oftheir expert teachers, teaching in this way creates thepossibility of an efficient curriculum that teaches morewith fewer animals.

A final note about novice/expert differences: Developing anapproach to teaching that acknowledges the novice stateas natural, possibly even necessary, may allow faculty

Table 1: Summary of key differences between novices and experts, with related teaching strategies

Novice LearningCharacteristics

Expert Learning Characteristics Teaching Strategies That Capitalize on Novice/ExpertDifferences

Little prior subject knowledge Extensive prior knowledge � Remind students of what they already know, even

from informal or everyday learning.

� Use concrete examples from students’ own

backgrounds.

Lack of awareness of their

misconceptions

Ability to correct their misconceptions � Uncover and address misconceptions directly by

getting feedback from students on their thought

process.

Knowledge tends to be

organized according to surface

features.

Knowledge is organized according to

deeper concepts.

� Draw analogies between examples, cases, and

problems with common deep concepts but different

superficial features.

New material is more difficult

to assimilate with superficial

organizational structure.

New material is easier to connect to

prior knowledge, thanks to deeper

organizational structure.

. Use outlines or concept maps that illustrate

important structural divisions.

. Ask questions that require deep organization

(‘‘how’’ and ‘‘why’’ questions).

Knowledge is considered a

bunch of facts—it lacks context.

Context is considered crucial to

knowledge—how it works in real

situations.

� Change the context of knowledge in class and in

assignments in order to encourage students to use

what they are learning in novel situations (e.g., tasks

from different skill areas combined in new ways;

familiar skills applied to new cases).

Lack of internalized

problem-solving process; steps

may seem arbitrary or rote.

Automatic problem-solving process

makes it easy to skip steps and often

involves big-picture thinking.

. Make the process explicit; include and highlight

all the steps.

. Use memory aids to help learners remember

important processes.

. Provide opportunities for practice, with feedback.

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members a more realistic view of their own developingexpertise in fields that are newer to them, includingeducation. Expert researchers and clinicians are notautomatically expert teachers; teaching requires the samedevelopment of skills and knowledge over time as anythingelse. Indeed, no one is a permanent expert; improving ourskills and knowledge constantly calls us to become novicesthroughout our careers. We can, however, become self-awarenovices, and we can teach our students to do the same. Wecan expect to ask the experts how they are organizing theirknowledge, and to follow their examples; we can search outthe problem-solving or other step-wise processes that theexperts use and adopt them ourselves (even if we have topress the experts to name all the steps they take, and even ifthe process seems rote or arbitrary at first); we can expectour own early efforts to take longer and be more difficult,and not become discouraged by that difficulty.With all these tools, it is possible to become ‘‘an expert atbeing a novice,’’8 whether in new areas of medical science orin teaching.

MODELS OF COGNITIVE DEVELOPMENTAs we explore models of cognitive development, whichtrace changes in how people think about and understandtheir own learning, my hope is that the congruence withour discussion of novice/expert behaviors will emergeclearly—as it should, since both ‘‘theories’’ offer perspec-tives on the same phenomenon, student learning. As withany model, we must keep in mind the limitations of viewinglearning through a ‘‘developmental’’ lens, which tends toreduce complex change to an apparently linear process.This flattening of the learning process comes about becausemodels of cognitive development are based on observations,via questionnaires, interviews, and other data, of theexperiences of students over time. The developmentalstages that emerge are descriptions of common traits andbehaviors observed in many individuals; they are more likean average than like any particular person’s complexlearning history. If we remember that a student’s cognitiveprocesses are neither constant in different situations norconfined to linear, stage-wise development, we will seethat models of cognitive development have a place inmaking sound instructional decisions. These modelscan help instructors understand how to provide a nextstep for students who need equal amounts of challengeand safety—something that research on novice/expertdifferences, investigated mainly by comparing populationsof beginners and specialists working on the same tasks,could not tell us, because of its own inherent limitations.

Research on cognitive development emerged in the 1960swith William Perry’s scheme of nine ‘‘forms’’ (stages) basedon male undergraduate students at Harvard College.9

Since then, similar studies have expanded their coverageto include women, minorities, non-traditional and adultstudents, and a wide variety of types of institutions,resulting in schemes with five ‘‘perspectives’’10 and four11

or seven12 stages, depending on the study. Once again, ourpurposes here are best served not by reviewing theinteresting variations between developmental schemes,which are summarized in detail elsewhere,13 but, rather,by painting a more general picture of their commonalities,

which we can then use to reveal something important aboutour students’ experiences and our frustrations as teachers.

In order to get a sense of what the main developmentalstages sound like, first read through the following ‘‘typical’’student statements. Do any ring true? Which have youheard a version of in your own teaching career? Whichare most frustrating?

Could you please just show me how to do it theright way?

It seems to me that everyone does this procedure[solves this problem; approaches this task] a littlebit differently, and I think that my way is just asvalid.

This case isn’t like the ones we’ve seen in class,but if I stick to the process we learned and do theprocedure, maybe it’ll work out.

The best diagnosis and treatment depend on a lotof factors; I’ll evaluate the possibilities basedon knowledge, uncertainties, and context to finda reasonable course of action.

I find that experts often recognize the fourth statement astrue for themselves (on a good day, at least), although theywould never actually say those words because their under-standing is thoroughly automated. Many experts alsoreport that the first statement sounds like something theywould think or say when learning something very new,as a first step. As we relate these four statements to thesimplified developmental stages they represent below,we should keep our initial reactions in mind in order torealize that cognitive development is ongoing and thatintellectual change may not proceed smoothly in onedirection.

Now let us look at descriptions of how students understandtheir knowledge and learning in commonly documentedstages of adult cognitive development. These four roughgroupings draw key terms (italicized) from the studiesmentioned above,9–13 rather than presenting any of theirschemes exactly.

1. Dualistic knowing. Knowledge is received from experts.Information is absolute—either right or wrong.There is low tolerance for ambiguity and uncertainty.

2. Multiplistic knowing. Knowledge is subjective.Information is difficult to evaluate as more or lesscorrect—all points of view seem equally valid. Thisstage is a form of transitional knowing.

3. Procedural knowing. Knowledge is more relative andcontextual. Information is discoverable but uncertain.Learning is more independent. Students may tendtoward one of two different approaches as theylearn processes for evaluating and analyzing data:

a Connected/believing: using empathy to adopt andtest new ideas (e.g., adopting a colleague’shypothesis and going with it to investigate howone could confirm it).

b Separate/doubting: using debate to challenge andtest new ideas (e.g., playing ‘‘devil’s advocate’’to a colleague’s hypothesis to uncover itsweaknesses).

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4. Reflective knowing. Knowledge is constructed.Complete evaluation requires personal commitment,in which one acknowledges uncertainty whilemaking informed choices between possible solutionsor courses of action.

Thinking about cognitive development often leads educa-tors to some fundamental and puzzling questions: Is it myjob to get students to the most ‘‘advanced’’ cognitive stage?To what extent can a teacher set the pace for a student’scognitive development? We have no definitive answers tothese questions, partly because of these models’ inherentlimitations. Even so, a reasonable approach for instructors isto aid students’ ‘‘development’’ as best we can whilerealizing that there are instances when the desired changemay not happen during a particular unit, semester, orprogram.

As we did with novice/expert differences, we are nowready to move beyond feeling annoyed with the dualists,multiplists, and even proceduralists in our classrooms andwork on how we can productively use cognitive develop-ment while teaching (as summarized in Table 2). A usefulconcept here is ‘‘cognitive dissonance,’’13 the point whenstudents realize the limitations of their current ways oflearning and knowing in a particular context. Dissonancefeels like a roadblock or detour; sometimes students see it asan avalanche that has wiped out the entire road. Without theright kind of support, students can think that dissonancemeans failure. In all cases, dissonance creates teachablemoments when something has to change for the student inorder to proceed with learning. We rarely experience the‘‘aha!’’ of learning without some dissonance.

But dissonance alone does not tend to promote deep andpermanent learning. For example, Ryan et al. found that asurface learning approach and fear of failure are relatedamong pre-clinical veterinary students.14 ‘‘Surface learn-ing,’’ as the name suggests, and in contrast to ‘‘deep’’ and‘‘strategic’’ learning approaches, describes behaviors thatyou would recognize among our descriptions of novices anddualists, pointing out once again that there aremanyways todescribe the same learning behaviors. Where there is fear offailure, the risk is not safe—there is no apparent way aroundthe roadblock, no way out from under the avalanche.

The only escape route is backwards, toward simpler andmore basic ways of construing knowledge. As teachers, wecan set the stage for moments of dissonance and providesome safety by showing students procedures they canemploy to ease the transition from one stage to another. Wecan put up the roadblock, show them the map, and explainhow to use it.

As teachers, we create dissonance when we challenge thelimitations of students’ understanding by designing ques-tions, problems, and exams that go beyond right/wronganswers, as well as when we ask students to support theiranswers with analysis and to confront the uncertainty intheir results.6 In academic and medical practice, thesequalities are commonplace, but this ‘‘expert’’ way ofquestioning the data is also a teaching tool that encouragesmore cognitively complex learning. Using a comprehensivelist of objectives, such as the well-known Bloom’s Taxonomyof Educational Objectives,15 as a cheat-sheet while buildingsyllabi and concept maps can help instructors remember toinclude goals that move toward later stages of cognitivedevelopment: not only memory, comprehension, andapplication but also analysis, synthesis, and evaluation.a

Compared to dissonance, the need to create safety mayseem less familiar, and this idea may generate concerns forteachers about being too easy on students. However, likeour understanding of novices, acknowledging the utility ofall these phases of cognitive development is a crucial step.Dualism is not necessarily wrong, but it has its limitations:the ability to write an executive summary, to state the‘‘bottom line’’ answer, is a useful skill to have in somecircumstances. Instructors can recognize this kind of limitedutility and also model the next step in complexity byshowing students how to come up with more than oneanswer or method (even requiring them to do so, whenappropriate), and by offering specific proceduresfor analyzing and evaluating data, along the lines of theproblem-solving processes that our familiar novices needto learn and practice explicitly on their path towardexpertise.

Safety is also embedded in a syllabus or course sequencethat prepares students for more challenging and complextasks along the way. Preparation takes on even greater

Table 2: Summary of developmental stages, with related teaching strategies that establish safetyand dissonance, both important factors in cognitive development

Common Stages of CognitiveDevelopment

Strategies for Creating Cognitive Safety Strategies for Creating Cognitive Dissonance

1. Dualism, absolutism, received

knowing

1. Understand and acknowledge the utility

of each developmental phase.

1. Ask questions, give assignments, and set

exams that require complex thinking.

2. Multiplicity, subjective

knowing

2. Teach procedures for analysis that use

both separate and connected modes.

2. Use Bloom’s Taxonomy to make sure you

ask for advanced analytical thought.

3. Relative, contextual,

procedural knowing

(connected and separate)

3. Build syllabi that prepare students step

by step for new skills.

3. Model scientific questioning of data.

4. Reflective, constructed

knowing, requiring

commitment

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significance when we consider the use of animals in theveterinary curriculum: Have students gone through all theimportant steps, and received feedback on each, beforeworking with the animal? Do they have procedures in placeto help them get to the more cognitively advanced placeneeded for effective diagnosis and treatment in the faceof uncertainty? It becomes crucial to consider whether astudent’s experience with the animal is where one wouldchoose more dissonance or more safety, or specific forms ofeach, related to the learning goals involved.

STYLES OF LEARNINGWhen I discuss learning styles with instructors, I often askthem first if they know what theirs is—a fundamentallymisleading question with a great variety of answers, sincewe have not yet defined the term. But I find that the varietyitself, along with most people’s intuitive sense of theirpreferred styles of learning, leads directly to the key insightsfor teaching from this area of work. That is, people’spreferences for how they like to learn vary a great deal;there is no guarantee that students’ preferred learning styleswill be aligned with their teachers’. Although this maysound like bad news, it is actually a very good thing for bothteachers and learners, because full competency (sometimescalled deep learning) requires the use of multiple styles ormodes.1, 2, 6

These insights sound simple, but they are very important:time and again I talk with teachers who have made theirinstructional choices based on how they learned the conceptat hand, focusing in particular on whatever led to their own‘‘aha!’’ moment, and are baffled when the same teachingtechnique fails to produce the same learning effect for theirstudents. As we consider using learning styles, in all theirvariety, as tools to move beyond our own limited set ofpreferences and experiences, the use of animals in theveterinary curriculum is a perfect example. Faculty mustnow transcend the role that animals played in their owneducation in order to find new solutions. They mustsystematically look for new perspectives and ideas, andlearning styles are a useful place to begin this kind ofsystematic investigation.

There are many learning-style schemes to choose from.Though learning styles have traditionally been considered

relatively stable and inherent characteristics, more recentwork suggests that our learning preferences do changebased on context and over time.6, 13, 16 The various schemestend to contain descriptions of sets of distinct learningqualities or characteristics that complement one another.Some are based on questionnaires of various lengths thatreveal one’s particular distribution of style preferences.Well-known examples include Fleming and Mills’s VARK(Visual, Auditory, Read/Write, and Kinesthetic) model16

and Kolb’s Learning Styles Inventory (LSI),17, 13 in which theimportant categories are Concrete Experience, ReflectiveObservation, Abstract Conceptualization, and ActiveExperimentation.b Other delineations of learning styles,preferences, and approaches include performance andmastery; extroversion and introversion; and the surface,strategic, and deep approaches to learning mentionedabove.14 For our purposes, we will assume that thedescriptive names of various styles of learning are familiarenough (although a great deal more depth is available in thereferences cited, as well as through the use of diagnostictools and inventories) and move on to the instructionalstrategies we can glean from this diverse body of work.

A fundamental and common misunderstanding of learningstyles is that teachers must somehow know (either byhaving students fill out diagnostic tests or through theirown intuition) the learning styles of all their students—animpossibility in a lecture hall with hundreds of students,and likely not very useful even for a smaller group of a fewdozen. Instructors also worry that a familiarity withlearning styles will require them to prepare four (or eight,or 16 or more, depending on how many schemes they use)different lessons in order to accommodate each of theirstudents. Indeed, although some studies indicate subtletrends toward certain learning preferences within disci-plines and populations, most any group of studentswill exhibit a fairly wide variety, and rarely if ever willthey all match their teacher.16 How, then, can learning stylespossibly work to our advantage as teachers, rather thanposing an insoluble problem?

To answer this question, we need to look closely at whatsystems of learning styles actually represent. These schemesrepresent preferences, ways of learning that generally feelmore comfortable, familiar, or easy. If given a choice, we willgravitate toward what we like, but that does not mean that

Table 3: Examples of learning style categorization schemes, with related teaching strategies that employdifferences in learning styles to reach students and to enhance learning

Learning Styles: Example Schemes Strategies for Reaching Students Strategies for Enhancing Learning

1. VARK: Visual, Auditory,

Read/Write, Kinesthetic

1. Incorporate a variety of styles

of presentation and explana-

tion in classes.

1. Encourage students to employ

alternative learning strategies for

deeper learning.

2. Kolb’s LSI: Concrete Experience, Reflective

Observation, Abstract Conceptualization,

Active Experimentation

2. Recognize when problems

come from style

contrasts.

2. Consider different ways students

might fulfill course requirements

to employ more learning styles.

3. Performance/

Mastery

3. Pay more attention to the

styles you prefer less as a

teacher.

4. Extroversion/Introversion

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what we like is the best or only way for us to learn. Like oneof our insights from cognitive development theory—thatboth dissonance and safety are important—learning-stylepreferences give us two incredibly useful ways to help ourstudents. First, they provide the means to reach students byappealing to how they like to learn. We do this by includinga variety of learning modes in every possible chunkof teaching—in a lecture, in an assignment, in an exam.You might think of this strategy as another form of safety.Second, learning styles enhance learning by encouraging allstudents to move beyond their comfort zone and experiencethe material in new ways—a key to improving coding andretrieval of information.1, 2

For example, Van Genneken and Vanthornout worked toredefine the learning outcomes and related assessmentcriteria for a gross anatomy course and came up with thefollowing: ‘‘knowledge, orientation and palpation, use ofdissection equipment, exposition of the structures, commu-nication, hygiene, and precision.’’18 We can take differentlearning-style schemes and see goals that are likely alignedwith different qualities: knowledge with AbstractConceptualization and the Read/Write mode, orientationwith Reflective Observation and the Visual mode, palpationwith Active Experimentation and the Kinesthetic mode,exposition with Concrete Experience and the Auditorymode. These improved anatomy-learning goals imply thatfor a deep understanding of the subject, multiple styles areneeded. Working with a variety of learning styles is notonly about accommodating different learners but also aboutdeveloping full competency.

This leads us to consider how to integrate learning styleswith teaching in a realistic way. Considering both usesof learning styles is a helpful way to sort possible strategiesdiscussed below and summarized above in Table 3.

We can use learning styles to reach students by

. Using a variety of styles of presenting and explainingin every class, so that more students find a connectionto the material that feels ‘‘natural’’ and familiar tothem.

. Recognizing when problems come from stylecontrasts. Ask students how they are trying to learnand understand so that you can see when they arestuck in their own preferences, and when you arestuck in your own.

. Paying particular attention to the styles you preferless, from any of the schemes discussed here, orothers you may know.

We can use them to enhance learning by

. Using a variety of styles of presenting and explainingin every class, so that students see that you know thematerial deeply enough to vary your approach.

. Encouraging students to employ alternative strategiesfor deeper, more flexible learning.

. Considering different ways students might fulfillcourse requirements, within boundaries that youdetermine as appropriate for your learning goals.For example, it may be possible for students to choosebetween written or oral delivery of their research—or,better yet, to do both, as deeper learning will result.

With respect to animal use in veterinary education, learningstyles may prompt consideration of whether preparationand practice are strictly separated by style and whether theyshould be redesigned for optimum learning. It might betempting, for example, to rely exclusively on the Read/Write mode as students get ready for an encounter with alive animal, and then rely primarily on Visual andKinesthetic learning during practice with the animal.But could a Visual or Kinesthetic component be part of thepreparation—perhaps as simple as making a diagram ofthe structures to be located and palpated, or laying out aphysical flow-chart of the instruments to be used in theprocedure on a table? Could a Read/Write component beincluded in the experience with the animal, such as havingstudents write answers to one or two reflective feedbackquestions indicating what they learned and what questionsthey still have at the end of the practice session (often calleda ‘‘minute paper,’’ a useful instrument for both feedbackto instructors and for students to solidify their ownlearning19)?

A key goal of veterinary medical education seems to befostering students’ abilities as independent, self-directedlearners over the long term. By modeling and using avariety of ‘‘styles’’ as a teacher, one helps students escapethe limitations of their preferences, which becomes evenmore useful as they approach learning tasks that areincreasingly challenging after their formal training is over.

CONCLUSIONThese three perspectives on learning—novice/expert beha-viors, cognitive development, and learning styles—can allbe considered tools for focusing on the student and his orher process. But beyond that, they help teachers reflect ontheir own teaching and learning processes, facilitatingthe expansion of teaching repertoires beyond individualintuition and preference into new solutions to teachingchallenges, including that of refinement, reduction, andreplacement of animals in veterinary teaching.

NOTES

a Many adaptations of Bloom’s Taxonomy are nowavailable online from university teaching centers andother sources. Those that include question stems—keywords associated with educational objectives(e.g., define, explain, solve, analyze, predict) that canbe used directly in designing assignments andexams—are particularly useful.

b The two main learning-style resources mentioned hereare available online: Fleming’s VARK at<http://www.vark-learn.com> and Kolb’s LSI at<http://www.hayresourcesdirect.haygroup.com>.

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AUTHOR INFORMATION

Cassandra Volpe Horii, BA, PhD, is Associate Director of the

Derek Bok Center for Teaching and Learning, Harvard

University, 1 Oxford Street, Cambridge, MA 02138 USA.

E-mail: cvolpe@fas.harvard.edu. Her research interests include

science and writing pedagogy as well as disciplinary aspects of

university teaching and learning.

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