perceived multiple intelligences and learning preferences ... · building on past studies on...

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Perceived Multiple Intelligences and Learning Preferences Among Chinese

Gifted Students in Hong KongDavid W. Chan

this study examined the relationships between self-perceived multiple intelligences and five learning preferences among 604 chinese gifted students in Hong Kong. these students perceived their strengths in interpersonal, intrapersonal, and verbal-linguis-tic intelligences and their weaknesses in bodily-kinesthetic and naturalist intelligences. they also indicated greater preferences in learning activities related to discussion, lec-ture, and peer teaching, followed by projects and simulations. in predicting the five learning preferences, personal intelligences generally emerged as common and signifi-cant predictors, suggesting that reflection and interpersonal skills contributed substan-tially to these learning activities. Students who reported having a greater number of learning preferences also gave themselves higher ratings on personal intelligences and verbal-linguistic intelligence. implications of the findings in mapping learning prefer-ences on multiple intelligences for teaching and learning are discussed.

Ratherthansubscribingexclusivelytothenotionofageneraluni-taryintelligencethatcutsacrossallareasofhumancompetencetoexplainhumanperformance,manypsychologistsandeducatorsnowtendtoregardthateachindividualhasspecificstrengthsandweaknessesandcanbeconceptualizedtohavemultipleabilities(seeKarolyi,Ramos-Ford,&Gardner,2003;Guilford,1967;Sternberg,1986,1997,2000).Gardner(1983,1993,1999a),inparticular,con-ceptualizedtheseabilitiesasintelligencesandproposedinhistheoryofmultipleintelligences(MI)thatthereareseveralkindsofintelli-gences,whichmaybeaffectedbyculture,biology,andotherfactors.Sofar,Gardner(1999a)hasidentifiedeightintelligencesandiscon-sideringothercandidateintelligences.Theeightintelligencescanbedefinedandsummarizedasfollows.Verbal-linguisticintelligencerepresentsthecapacitytousewordseffectively,whetherorallyorinwriting.Musicalintelligencerepresentsthecapacitytoperceive,dis-

DavidW.ChanisaprofessorintheDepartmentofEducationalPsychologyattheChineseUniversityofHongKong

Journal for the Education of the Gifted.Vol.29,No.2,2005,pp.187–212.Copyright©2005PrufrockPressInc.,http://www.prufrock.com

Journal for the Education of the Gifted188

criminate,transform,andexpressmusicalforms.Logical-mathemati-calintelligencerepresentsthecapacitytousenumberseffectivelyandtoreasonwell.Visual-spatialintelligenceistheabilitytoperceivethevisual-spatialworldaccuratelyandtoperformtransformationsonthoseperceptions.Bodily-kinestheticintelligenceincludestheabilitytousethebodytoexpressideasandfeelingsandthefacilityinusingone’shandstoproduceortransformthings.Intrapersonalintelli-genceistheabilitytoactadaptivelyonthebasisofself-knowledge.Interpersonalintelligenceistheabilitytounderstandandinteracteffectivelywithothers.Naturalistintelligencerepresentstheabilityinobservingpatternsinnature,identifyingandclassifyingobjects,andunderstandingnaturalandhuman-madesystems.

Since its firstpublication,MItheoryhasbeenembracedbyeducatorswhofindtheperspectiveusefulinnotonlyexpandingtheirthinkingaboutabilitiesbutalsotheiravenuesforteaching(seeArmstrong,1994,1999;Campbell,Campbell,&Dickinson,2004;Kornhaber,Fierros,&Veenema,2004).However,thetheoryhasnotgoneunchallengedfromscholarsandresearcherswhonotonlyquestionedtheindependenceoftheeightintelligencesbutalsowhetherthesedomain-specificintelligencesshouldbecalledintelligences,castingdoubtsthatsomeoftheseintelligencescouldbeconsideredpersonalityfactorsratherthanabilities(e.g.,Delisle,1996; Gottfredson, 2003; White & Breen, 1998). Further, inapplications,itissaidthatsomeenthusiasticteachersmighthavemisusedormisappliedMItheory.Withasimplisticversion,theymight,forexample,attempttoincludeall intelligencesineverylesson,nomatterhowinappropriate(Gardner,1999b).Thus,despitetheclaimthattheMIapproachtoidentifyingandpromotingtalentsinstudentscouldenhancestudents’learning,therearedoubtsandskepticismsastothebenefitsoftheMIapproachinteachingandlearning.Indeed,Klein(2002)hasarguedthatMItheoryistoobroadastoinformteachershowtoteach.Hecitedtheexamplethatknowingthatplayingbasketballreliesonbodily-kinestheticintelligencedoesnotinformthecoachtheskillsthattheplayersneedtolearn.

RecognizingthedifficultiesinputtingMItheoryintopractice,Gardner(1991,1999c)hasdevisedtheentry-pointsframeworkasa

Multiple Intelligences and Learning Preferences 189

toolfordevelopingcurricula.Inthisframework,curricularunitsaredividedintomultipleentrypoints(narrative,logical-quantitative,esthetic,experiential,interpersonal,andexistential/foundational)suchthatstudentsareallowedtogaindifferentperspectivesonthesamesubstantivetopicwithdeepenedunderstanding,facilitatingtheapplicationandtransferofknowledgefromonecontexttoanother.Becausetheentrypointslargelymapontodifferentintelligences,differentstudentshavingdifferentprofilesofmultipleintelligenceswouldbedifferentiallyengagedbypursuingspecificentrypoints.Followingthesamelineofreasoningincurriculumdesign,itwouldbeofinteresttoextendthisconceptualizationintoteachingandlearningandmaplearningactivitiesontodifferentintelligences.

Ingiftededucation,MItheoryhasimplicationsforidentification,assessment and evaluation, and teaching and learning (Fasko,2001).Specifically,MItheoryenhanceseducationpractitioners’awarenessoftheneedsofgiftedstudentswhomighthaveunevenorasynchronousdevelopmentacrossdifferentabilities.Inaddition,MItheoryalsoalertseducatorsthattraditionalclassroomsmightidentifystudentswithwell-developedconventionalintelligences(e.g.,verbal-linguisticandlogical-mathematicalintelligences)asgifted,andmightoverlookandexcludestudentswithwell-developedintelligencesnotconventionallyassessedfromgiftedserviceprovisions.Indeed,MI theory has provided an alternative approach in identifyingunderrepresentedandculturallydiversegroupsofgiftedstudentsforparticipationingiftededucationprograms(seeMaker,Nielson,&Rogers,1994;Sarouphim,1999),andincurriculumdesignandteachingandlearningthroughmultipleentrypointsthatmapondifferentintelligences(Armstrong,1994,1999;Campbelletal.,2004).

InthedevelopmentofgiftededucationinHongKong,educators,liketheircounterpartsinWesternsocieties,havegraduallymovedawayfromrelyingonasingleIQmeasureandhavebroadenedthenotionofgiftednesstoincludedifferentfacetsofgiftednessandtalents(seeHongKongEducationCommission,1990;HongKongEducationDepartment,2000).Notably,MItheoryappealstoHongKongeducatorsasanalternativeandusefulapproachinassessingandidentifyinggiftednessinstudentsandinteachingandlearningthat


areinlinewiththeChineseeducationalidealsofpromotingstudents’all-rounddevelopmentinthefivedomainsofde, zhi, ti, qun,andmei(ethics,intellect,physique,socialskills,andesthetics).Inthisconnection,itwasdeemednecessarythateffortsshouldbedirectedatputtingMItheoryintoschoolpracticethroughthedevelopmentanduseofmeasuring instruments toassess students’profilesofintelligencesandthroughthedevelopmentandimplementationofcurriculawithmultipleentrypoints,aswellaslearningactivitiesthatmapondifferentintelligences(seeChan,2000;HongKongEducationDepartment).

Inassessingstudents’profilesof intelligences,Chan(2001a,2003)hasdevelopedtheStudentMultiple IntelligencesProfile(SMIP), a self-report measure that focuses on gifted students’activitiesorpreferencesthatreflecttheirself-perceivedmultipleabilitiesorintelligences.TheoriginalSMIPhadsevenscalesthatassessedtheseven(exceptnaturalist)intelligencesofstudents.Chan(2001a)has reported that these scaleshad soundpsychometricproperties,includingmoderateinternalconsistency(Cronbach’sα=.64to.76)andsignificantcorrelationswithexternalmeasuressuchasnonverbalreasoning(Raven,Raven,&Court,1998)andleadershipscores(Roets,1997),instudieswithChinesegiftedstudents.Anexploratoryitemfactoranalysisbasedon192studentssuggestedthattheseven-factororthogonalsolutioncorrespondingtothesevenintelligenceswasanadequaterepresentationofthedata,althoughtheconfirmatoryfactoranalysiswithacorrelatedfactormodelyieldedonlymediocretoatbestmoderatefitwithindicesaround.80.Thus,itisrecognizedthatanongoingefforttoimprovethescalesneedstobeemphasized.Inthecontinuousprocessofscaledevelopment,arevisedSMIP(SMIP-24)hasbeendevelopedwithslightrewritingofsomeoftheoriginalitemsandincorporatingnaturalistintelligenceasaneighthscale.WhileMItheorygenerallysupportstheuseofauthenticassessmentinvolvingperformanceratherthanself-reportmeasures(seeChen&Gardner,1997),itisalsobelievedthatthisself-reportmeasurecouldbeofgreatvalue,asself-perceptionreflectsgiftedstudents’ownrecognitionoftheirtalentsandcouldbetheirfirststepintalentdevelopment(seeAlbert,1994;Treffinger&Feldhusen,1996).Further,positiveself-perceptionscouldimpacton


variousaspectsofstudents’schoollife,leadingtopositivesocialandemotionaldevelopment(Colangelo,2003;Neihart,1999),andself-narrativescouldopenspacefornewopportunitiesandtherapeuticchanges(White&Epston,1990).

InpromotingteachingandlearningthroughtheMIapproach,Chan (2001b) has done some initial work on delineating thelearningactivitiesorstylesofgiftedstudentsusingtheLearningStylesInventory(LSI)byRenzulliandhiscolleagues(Renzulli&Smith,1978;Renzulli,Smith,&Rizza,1998).ThedevelopmentofLSIwasbasedontherationalethatifstudents’learningactivitiesorpreferencescouldbe identifiedandstudentswerepermittedtolearnthroughthemethodsoftheirchoice,theirachievement,motivation,andinterestinschoolsubjectswouldbeenhanced(seealsoDunn,Beaudry,&Klavas,1989;Griggs,1984;Griggs&Dunn,1984;Grigorenko&Sternberg,1997).SomesupportingevidencecouldbegleanedfromtheworkofRenzulliandReis(2003)ontheirSchoolwideEnrichmentModelandSternberg’s(2002)workonteachingforsuccessfulintelligencetoraisestudents’academicachievement.Specifically,LSIassessesstudents’preferencesfornineteachingmodes:Discussion,Drill-and-Recitation,IndependentStudy,Lecture,PeerTeaching,ProgrammedInstruction,Projects,Simulations,andTeachingGames.Byassessingstudentpreferencesforteachingstrategies,theconcreteteacher-centeredapproachofLSIavoidsanalysisofunderlyingexplanationsforstudentlearningpreferences,andhastheadvantageofallowingteacherstotranslatestudentpreferencesreadily intopractice.IntheMIframework,students’preferencesforspecificlearningstylescouldbereinterpretedasthelearningpreferencesthatwouldengagetheirspecificwell-developedintelligencesforenhancedandoptimallearning.

InusingLSIwithChinesegiftedandnongiftedstudents,Chan(2001b)identifiedthreemajordimensionsoflearningactivities,whichincludedadimensionoflearningthroughverbalinteractionsthat encompasses Discussion, Peer Teaching , and Lecture; adimensionoflearningbyrole-playorSimulations;andadimensionoflearningbydoingorProjects.Basedontheitemfactoranalysisofthestudy,ashortenedLSI-20wassubsequentlydevelopedbyconsideringthesubstantivecontentoftheitemsandbyselectingthe


best20itemsthatloadedsalientlyonthethreefactors.TheresultingfivescalesareDiscussion,PeerTeaching,Lecture,Simulations,andProjects,eachbeingrepresentedby four items.Withfive scalesrepresentingfivelearningpreferencesofChinesegiftedstudents,itwouldbeofinteresttomaptheselearningpreferencesontothespecificintelligences.Theexplicationoftherelationshipbetweenlearningpreferencesandmultipleintelligenceswouldallowteachersto infer students’ profiles of intelligences from their learningpreferences,orconversely,topredicttheirlearningpreferencesbasedontheknowledgeofstudents’profilesofintelligences.

Buildingonpaststudiesonmultipleintelligences(Chan,2001a,2003)andlearningpreferences(Chan,2001b)withChinesegiftedstudents,thisstudyaimedtoexaminetherelationshipsbetweenmultipleintelligencesandlearningpreferencesinasampleofgiftedstudentsnominatedbytheirschoolstoparticipateinuniversitygiftedprograms.Students’perceivedmultipleintelligenceswereassessedbyusingthe24-itemChineseSMIP-24(Chan,2001a,2003)andlearningpreferenceswereassessedbyusingthe20-itemChineseLSI-20,whichyieldedscoresoneightintelligences,aswellasfivelearningpreferencesthatincludedDiscussion,PeerTeaching,Lecture,Simulations,andProjects(Chan,2001b).Specifically,thisstudyexaminedstudents’perceptionoftheireightintelligencesandtheirfivelearningpreferences,assessedtherelationshipsbetweenstudents’multipleintelligencesandtheirlearningpreferences,andevaluatedtheextenttowhichlearningpreferencescouldbepredictedbyspecificintelligences.Further,thisstudyalsoexploredwhetherstudentswithspecificlearningpreferencesandstudentshavingagreaternumberoflearningpreferencescouldbecharacterizedbyspecificprofilesofintelligences.

Method

Participants

Atotalof613primaryandsecondaryChinesestudentswerenomi-natedbytheirschoolstojoindifferentgiftedprogramsprovidedat


differenttimesattheChineseUniversityofHongKongoveraperiodof8months.About98.5%ofthesenominatedstudentsparticipatedvoluntarilyinthisstudy.These604participants(321boysand283girls)wereingrades4to12,andwereaged7to18(M =11.98,Sd=2.11).Innominatingstudents,schoolswererequestedtorecom-mendstudentswhowerejudgedtobeeithergiftedintellectually(e.g.,withahighIQscore),academically(e.g.,withoutstandingper-formancesinschoolsubjects),orhaddemonstratedtalentsinotherspecificnonacademicareassuchasinmusic,finearts,andleadership.BecausetherewerenogenerallyacceptedstandardmeasuresinHongKongschoolsandschoolsgenerallydidnothaveaccesstoinforma-tiononspecificIQscoresofstudents,teachersmakingrecommenda-tionswouldmaketheirownjudgmentbasedontheirknowledgeoftheirstudents.Ingeneral,teachersalwaystendedtorecommendstu-dentswiththebestacademicrecordsintheirschools.Nonetheless,thissampleofparticipantscouldberegardedasrelativelyheteroge-neousintermsoftheirgiftednessortalentsandrepresentedstudentsfromabroadagerange.

Measures

Student Multiple intelligences Profile. TheSMIP-24isa24-itemchecklistofcharacteristicsandbehaviorsconstructedtoreflectstu-dents’self-perceptionsoftheirabilitiesintermsofGardner’s(1999a)multipleintelligences.Theoriginal21-itemSMIPwasdesignedtoassessstudents’sevenintelligences(threeitemsforeachintelligence),thatis,verbal-linguistic,musical,logical-mathematical,visual-spatial,bodily-kinesthetic,intrapersonal,andinterpersonalintelligences(Chan,2001a).IntherevisedSMIP-24,threeitemshavebeenaddedtoincorporatetheadditionofnaturalistintelligence(Chan,2003).TheSMIPhasbeenused in studieswithChinese studentsandhasdemonstratedsoundpsychometricproperties.Thescaleshaveachievedmoderateinternalconsistencyvalueswithconstructvali-dationusingitemfactoranalysis(seeChan,2001a,2003).AmoreelaboratedescriptionofthedevelopmentofSMIP,withtheitemsofSMIPintheChinesePinyinversioncouldbefoundinChan(2001a).


IncompletingSMIP-24,respondentswererequestedtoratethemselvesonthe24itemsusingafive-pointscalerangingfrom1(least descriptive)to5(most descriptive).SMIP-24canbescoredoneightscalesthatyieldeightscoresreflectingtheeightintelligences.

learning Styles inventory.TheLSI-20employedinthisstudywastheChineseshortenedversion.TheChineseversionwastranslatedfromtherevisedEnglishversion(Renzullietal.,1998)andhasbeenusedwithChinesegiftedandnongiftedstudents(Chan,2001b).AreviewofthepsychometricpropertiesoftheoriginalEnglishver-sioncanbefoundinHudak(1985).TheshortenedChinesever-sionwasdevelopedbasedonitemfactoranalysisandsubstantiveconsiderations(seeChan,2001b).LSI-20hasfivefour-itemscales:Discussion,PeerTeaching,Lecture,Simulations,andProjects.IncompletingLSI-20,respondentswererequestedtoratethemselvesontheirpreferencesforlearningactivitiesbyrespondingtotheitemsusingafive-pointscalerangingfrom1(least descriptive)to5(most descriptive).

Procedure

All604nominatedstudentswhovolunteeredtoparticipatewiththeconsentoftheirparentsinthisresearchprojectwererequestedtocometotheuniversitycampusforassessmentontheirself-per-ceivedmultipleintelligencesandtheirlearningpreferences.Thesestudentsweretestedingroupsof80to100usingtheChineseSMIP-24(Chan,2001a,2003)andtheChineseshortenedLSI-20(Chan,2001b).

Results

Toassesstheprofilesofmultipleintelligencesandthelearningprefer-encesofthe604giftedstudents,therelevantitemresponsesofthesestudentstoSMIP-24andLSI-20werefirsttabulated.Preliminarymaximumlikelihoodexploratoryfactoranalyseswereseparatelyconductedonthe24-itemandthe20-itemcorrelationmatricesto


checkwhetherrelevantitemsdidfallappropriatelyintoeightfac-torsandfivefactorscorrespondingtoeightintelligencesandfivelearningpreferencesrespectively.Regardinglearningpreferences,theinitialestimationyieldedfivefactorswitheigenvaluesexceedingunity,accountingfor65%ofthetotalvariance.Thechi-squarevaluecomputedfortheevaluationofthelackoffitforthefive-factorsolu-tion,χ2(100)=201.03,p<.001,accountingforanestimatedvari-anceof52%,suggestedthatastatisticallyadequatesolutionmightrequireevenmorethanfivefactors.Becausethemodelwouldberejectedbythechi-squarestatisticataconventionalalphalevelifalargeenoughsamplewasused(seeBrowne&Cudeck,1993),itwasdeemedappropriatetoacceptthefive-factorsolutionasanadequaterepresentationofthefivelearningpreferencesbasedonsubstantiveconsideration,giventhattherelevantitemsoflearningactivitiesdidfallnicelyintothefivefactorsoflearningpreferences.Thus,therel-evantitemsoflearningactivitieswerescoredtoyieldscoresonfivelearningpreferences.

Similarly, intheanalysisconductedonSMIP-24,the initialestimation yielded seven factors with eigenvalues equal to orexceedingunity,accountingfor61%ofthetotalvariance.Thechi-squarevaluecomputedfortheevaluationofthelackoffitfortheseven-factorsolution,χ2(129)=266.42,p<.001,accountingforanestimatedvarianceof47%,suggestedthatastatisticallyadequatesolutionmightrequireevenmorethansevenfactors.Substantively,therelevantitemsofmultipleintelligenceslargelyloadedsalientlyontherelevantfactors,withtheitemsofintrapersonalintelligenceandthoseofinterpersonalintelligencesloadedsalientlyonthesamefactor.Inaddition,thereweresomeirregularitiesshowingthatthreeitems(onelogical-mathematical,onevisual-spatial,andonebodily-kinesthetic)didnothavesalientloadingsontheirrespectivefactors.Ontheotherhand,theeight-factorsolution,χ2(112)=211.42,p<.001,accountingforonlyaslightincreaseofanestimatedvarianceof48%overtheseven-factorsolution,yieldedonefactorwithnosalientloadingsamongtheeightfactors.OnthebasisofthepresentfactoranalysisusingorthogonalfactorsandpastfactoranalyticstudiesonSMIP-24(Chan,2001a,2003,inpress)thatthetwopersonalintelligencesweregenerallyfoundtobecloselyassociated,itwas


deemedappropriatetoscoretherelevantitemsontheeightscalesofmultipleintelligences.

Table1showsthemeansandstandarddeviationsofstudents’ratings,aswellastheinternalconsistencymeasuresofthefivescalesoflearningpreferencesandtheeightscalesofmultipleintelligences.TheeightscalesofmultipleintelligenceshadmoderateinternalconsistencyasreflectedinthevaluesofCronbach’sα(.52to.77),whereasthefivescalesoflearningpreferenceshadslightlyhighervalues(.65to.85).Therelativelymodestinternal-consistencyvaluesofthesescaleswereunderstandableasthenumberofitemsineachscalewassmall,andeachitemingeneralwasintendedtocoveradifferentaspectoftherelevantconstruct.Forexample,inassessingbodily-kinestheticintelligence,oneitemhastodowiththeagilityofbodilymovements,anotheritemhastodowiththepreferenceinengaginginactivitiesrelatedtodanceandgymnastics,andathirditemhastodowiththeeaseinmanipulatingandrepairingthings.Thus,itwasexpectedthatabroadbandapproachasusedinthesescaleswouldyieldmodestinternalconsistency. ItcanalsobeseenfromTable1thatstudentsgenerallyratedtheirpersonal(intrapersonalandinterpersonal)andverbal-linguis-ticintelligencesrelativelyhigherthantheotherfiveintelligences,andtheygaverelativelylowerratingstotheirbodily-kinestheticandnaturalistintelligences.Forlearningpreferences,theyratedthem-selveshigherinlearningthroughverbalinteractions(Discussion,Lecture,andPeerTeaching),followedbyProjects,andlowestonSimulations.Themeanscoresthussuggestedthatstudentsperceivedrelativestrengthsindifferentintelligencesandindicatedprefer-encesindifferentlearningactivities.Supportfortheperceptionofdifferencescouldbegleanedfromthetwoseparateone-waywithin-subjectsanalysesofvariance(ANOVAs),treatingtheeightscoresofmultipleintelligencesandthefivescoresoflearningpreferencesrespectivelyasdependentmeasures.Theresultsformultipleintel-ligencesindicatedthattheoveralldifferencesamongtheeightscoresweresignificant,Wilks’Λ=0.54,f(7,597)=73.95,partialη2=.46,p<.001.Follow-uppairedt-testsonthedifferencesofallpos-siblepairsofscoresindicatedthat21outofthe28pairsweresig-nificantlydifferentfromeachotheraftercontrollingforfamilywise


errorrateacrossthe28testsusingtheBonferroniprocedure,witht-valuesevaluatedat.05/28or.00179levelofsignificance.Theresultsforlearningpreferencesindicatedthattheoveralldifferencesamongthefivescoreswerealsosignificant,Wilks’Λ=0.63,f(4,600)=88.01,partialη2=.37,p<.001.Follow-uppairedt-testsonthedif-ferencesofallpossiblepairsofscoresindicatedthat10outofthe10pairsweresignificantlydifferentfromeachotheraftercontrollingforfamilywiseerrorrateacrossthefivetestsusingtheBonferroniproce-dure,witht-valuesevaluatedat.05/5or.01levelofsignificance.

Learning Preferences and Multiple Intelligences

Table2presentsthematrixofcorrelationscomputedtoexaminetherelationshipsamongthefivelearningpreferencesandtheeightintel-

Table 1 Means, Standard Deviations, and Internal Consistency

of Measures of Multiple Intelligences and Learning Preferences of Gifted Students (N = 604)

Number Cronbach’s ofItems M Sd α Multiple intelligences Verbal-linguistic 3 12.43 2.07 .57Musical 3 12.15 2.60 .73Logical-mathematical 3 12.13 2.14 .52Visual-spatial 3 11.31 2.44 .61Bodily-kinesthetic 3 10.99 2.41 .57Intrapersonal 3 12.59 2.10 .74Interpersonal 3 12.85 1.93 .74Naturalist 3 11.11 2.84 .77

learning PreferencesDiscussion 4 17.05 2.81 .83PeerTeaching 4 15.83 2.74 .65Lecture 4 16.47 3.02 .74Simulations 4 14.35 4.19 .85Projects 4 15.39 3.73 .81

note. The multiple intelligences scales are scored in the range of 3 to 15. The learning styles scales are scored in the range of 4 to 20. α is the Cronbach’s alpha internal consistency measure.


ligences.Thecorrelationsofintelligence-preferencepairswereallsignificant(r=.24to.59,p<.001).Thehighestcorrelationswerethosebetweenthefivelearningpreferencesandthepersonal(intrap-ersonalandinterpersonal)intelligences(r =.38to.59),andbetweenthefivelearningpreferencesandtheverbal-linguisticintelligence(r =.32to.44).Thelowestoneswerethosebetweenthefivelearningpreferencesandmusicalintelligence(r =.24to.32).Thecorrela-tionsbetweenallpairsofmultipleintelligenceswerealsosignificant(p<.001).Thetwopersonalintelligencescorrelatedmosthighlywitheachother(r=.68),andthelowestcorrelationwasobtainedbetweennaturalistintelligenceandmusicalintelligence(r=.17).Thecorrelationsbetweenallpairsoflearningpreferenceswerealsosignificant(p<.001).ThehighestcorrelationwasbetweenLectureandDiscussion(r=.60),andthelowestcorrelationwasbetweenLectureandSimulations(r=.27).

To examine more closely how specific learning preferenceswererelatedtotheeightintelligences,aseriesofmultiplelinearregressionanalyseswereconducted.Specifically,separatesetsofmultiple regressionanalyseswereperformedtopredict the fivespecificlearningpreferences.Foreachofthecriterionmeasures,threesetsofanalyseswereconducted.Inthefirstsetofregressionanalyses,genderandagewereusedaspredictors(Set1predictors)to examine whether demographic variables could account fora substantial amount of variance in the criterion measures oflearningpreferenceswithoutinvokingthepredictorsofmultipleintelligences.Thesecondsetofanalysesusedtwoorderedsetsofpredictors,withSet1predictorsenteredfirst,followedbySet2predictorsoftheeightintelligences.ThechangesinrsquareandfwereassessedtoevaluatewhethertheSet2predictorsofmultipleintelligencespredictedthecriterionmeasuresoverandabovetheSet1predictorsofdemographicvariables.Thethirdsetofanalysesusedall10predictorswiththestepwiseproceduretoretainsignificantpredictors.Table3summarizestheresultsoftheregressionanalyses.

FromTable3,itcanbeseenthatSet1predictorsofgenderandagedidsignificantlypredictallfivelearningpreferences,thoughtheamountofvarianceaccountedforwasrelativelymodest(.02to.06).Genderemergedasthesignificantpredictorforallfivelearning


preferenceswhereasagewasasignificantpredictorinpredictingPeerTeachingonly,suggestingthatgirlspreferredthefivelearningpreferencesmorethanboysdid,andolderstudentsmightappreciatemorethecontributionsofpeersintheirlearningthandidyoungerstudents.TheadditionofSet2predictorsofmultipleintelligencestoSet1predictorsyieldedbetterpredictionthanusingSet1predictorsaloneandaccountedforasignificantlygreaterproportionofvarianceinallfivelearningpreferences.GendercontinuedtoemergeasasignificantpredictorforallfivelearningpreferencesandageasoneforPeerTeaching.Agealsoemerged,inthecontextofthemultipleintelligencespredictors,asasignificantpredictorinthepredictionofProjectsandLecturesuggestingthatthesetwolearningpreferenceswerepreferredmorebyyoungerstudents.

Apartfromthecontributionofgenderandageinthepredictionofthefivelearningpreferences,itcanbeseenfromTable3thatthefivelearningpreferenceswereeachpredictedbyslightlydifferentsetsofpredictorsofmultipleintelligences.Thestepwiseanalysisalsoprovidedasimplifiedpicturebytrimmingandretainingsignificantpredictors.Specifically,Discussionwaspreferredbystudentswhoratedthemselveshighlyonconventional(logical-mathematicaland

Table 2 The Correlation Matrix of Multiple Intelligences

and Learning Preferences (N = 604)

LearningPreferences Peer

Intelligences Discussion Teaching Lecture Simulations Projects

Verbal-linguistic .44 .34 .39 .41 .32Musical .29 .24 .25 .32 .25Logical-mathematical .38 .28 .33 .28 .32Visual-spatial .30 .30 .26 .35 .34Bodily-kinesthetic .36 .33 .24 .42 .36Intrapersonal .59 .42 .54 .38 .40Interpersonal .52 .41 .45 .38 .40Naturalist .31 .29 .31 .28 .33

note. All correlations are significant, p < .001 (2-tailed).

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verbal-linguistic)andpersonal(intrapersonalandinterpersonal)intelligences,andthustendedtobereflective,sociable,articulate,andrational.PeerTeachingwaspreferredbystudentswhotendedtobe reflective (intrapersonal),organized(naturalist), sociable(interpersonal),physicallyactive(bodily-kinesthetic),andweremorelikelytobeolderinage.Lecturewaspreferredbystudentswhotendedtobereflective(intrapersonal),organized(naturalist),andrational(logical-mathematical),andwhoweremorelikelytobefemale.Simulationswerepreferredbystudentswhotendedtobephysicallyactive(bodily-kinesthetic),articulate(verbal-linguistic),andsociable(interpersonal).Projectswerepreferredbystudentswhotendedtobereflective(intrapersonal),organized(naturalist),rational (logical-mathematical), and physically active (bodily-kinesthetic),andwhoweremorelikelytobefemale.

The Multiple Intelligences Profiles of Students With Specific Learning Preferences

Fromaslightlydifferentperspective,itwasalsoofinteresttoexplorewhethertheprofilesofmultipleintelligencesweredifferentforstu-dentswhohadaspecificlearningpreferenceasopposedtostudentswhodidnothavethatspecificlearningpreference.Forthepurposeofthisstudy,studentswhoscoredabove16onaspecificlearningpref-erencewereregardedasendorsingthatspecificlearningpreference.Thiscriterionwasinlinewiththecriterionadoptedusinganaver-agescoreoffourintheoriginalstudy(seeRenzulli&Smith,1978).Accordingly,studentswhoindicatedspecificlearningpreferencesofDiscussion,Lecture,Projects,PeerTeaching,andSimulationswere61.6%,54.5%,44.5%,42.5%,and35.6%,respectively.Usinglearn-ingpreference(scored16orbelowvs.scoredabove16)asagroup-ingvariableandtheeightintelligencesasdependentmeasures,fiveseparateMANOVAswereconducted.Theresultssuggestedthatstudentswhohadaspecificlearningpreferencehadsignificantlydifferentmultipleintelligencesprofilesfromstudentswhodidnotindicatesuchpreference,asindicatedbythesignificantpreferencemaineffects:Discussion(Wilks’Λ=.71,f [8,595]=29.82,partialη2=.29,p<.001),Lecture(Wilks’Λ=.74,f [8,595]=26.62,par-


tialη2=.26,p<.001),Projects(Wilks’Λ=.84,f [8,595]=14.52,partialη2=.16,p<.001),PeerTeaching(Wilks’Λ=.87,f [8,595]=11.07,partialη2=.13,p<.001),andSimulations(Wilks’Λ=.80,f [8,595]=18.75,partialη2=.20,p<.001).SubsequentunivariateANOVAsoneachoftheeightintelligenceswereconductedasfol-low-upteststothesignificantMANOVAmaineffectonpreferenceseparatelyforeachofthefivelearningpreferences.TheevaluationofsignificantdifferenceofeachANOVAwasbasedontheBonferroniprocedureofadjustingformultipletestsatthevalueof.05/8or.00625.Theresultsindicatedthat,forallfivelearningpreferences,studentswhoindicatedpreferencehadsignificantlyelevatedprofilesonalleightintelligences(higherscoresontheeightintelligences)thanhadstudentswhodidnotindicatesuchpreference.

According to the present classification based on learningpreferences, studentsmight indicatepreferenceonnoneofthelearningpreferencesoronetofivelearningpreferences.Indeed,the percentage of students indicating preference on zero, one,two,three,four,andfivelearningpreferenceswere18.0%,14.6%,19.5%,18.2%,17.7%,and11.9%,respectively.Tofurtherclarifythedifferencesbetweenstudentswhohadnopreferencesorpreferencesonasmallnumber(onetotwo)oflearningactivitiesandstudentswhohadpreferencesonthreeormorelearningactivities,aone-way MANOVA was conducted on the eight intelligences asdependentmeasures.Theresultsindicatedthatthesetwogroupsofstudentsdifferedsignificantlyintheirself-perceivedmultipleintelligences,Wilks’Λ=.69,f (8,595)=32.98,partialη2=.31,p<.001.SubsequentseparateunivariateANOVAsontheeightintelligenceswereconductedasafollow-uptesttothesignificantMANOVAresults.UsingtheBonferroniproceduretoadjustformultipletests,eachANOVAwasevaluatedatthelevelof.05/8or.00625.Theresultsindicatedthatthesetwogroupsofstudentsdifferedsignificantlyfromeachotheronalleightintelligences(p<.001).Thegreatestdifferenceswithsubstantialeffectsizeindiceswereinintrapersonalintelligence(partialη2=.22),interpersonalintelligence(partialη2= .18),andverbal-linguistic intelligence(partialη2= .16).Thus, studentswhohadagreaternumberoflearningpreferencestendedtohaveelevatedprofilesofintelligences


especiallyinthetwopersonalintelligencesandverbal-linguisticintelligence.TheprofilesofintelligencesofthesetwogroupsofstudentsaresummarizedinTable4.

Discussion

Thisstudyservedtoexpandpastfindingsonperceivedmultipleintel-ligencesandthoseonlearningpreferencesofChinesegiftedstudentsinHongKongandsoughttomakeconnectionbetweenthetworesearchtraditions.Inrecentyears,Gardner’sMItheoryhasgainedincreasingacceptanceamongHongKongeducatorswhoregardthedevelopmentofmultipleintelligencesasinlinewiththeChinesetraditionaleducationalidealsofnurturingchildreninfivedomainsofethics,intellect,physique,socialskills,andesthetics(de, zhi, ti, qun,andmei),andasawayofeducatingthewholepersontoyieldabalanceddevelopmentinchildren(seeChan,2000).WhilethefiveChineseeducationaldomainscouldnotpreciselymapontotheeightintelligences,MItheorylendsrenewedsupporttothenotionthatitisimportanttoadaptthecurrenteducationsystemwithitscur-ricularoveremphasisonverbal-linguisticandlogical-mathematicalintelligencestoasystemthataimstomeetvariousindividualdiffer-encesinthedevelopmentofmultipleintelligencesforbettereduca-tionalgains(Kornhaber,Krechevsky&Gardner,1990;Walters&Gardner,1986).

DespitetherecognitionthattheMIapproachcouldbecomeapromisingapproachinHongKongschoolpractice,thequestionremains as to how educators could make the approach moreappealingtoteacherswithoutrequiringthemtodeviatetoomuchfromtheirusualclassroomteachingandlearningactivities.Veryoften,teachersarerequestedtoassessandaccommodatestudents’learning preferences in order that students’ learning outcomescan be optimized. The assumption is that students will learnmore easily and enjoyably when their learning preferences areaccommodatedininstructionalstrategiesthatarecongruentwiththesepreferences(seeRenzulli&Smith,1978;Renzullietal.,1998).Inthisregard,theassessmentofstudents’learningpreferencesor


correspondingteachingstrategies,aswellasthemappingoftheselearningpreferencesontomultipleintelligences,couldberevealingtoteachersandstudents.Thus,bothassessmentandmappingwillhelppointoutthevarietyoflearningpreferenceswithinaclassroom,alertingteacherstomakeuseofavarietyofinstructionalstrategiestoreachstudentswithdifferentprofilesofintelligencesandtousethemoreadaptiveteachingstrategiesthathaveprovedtobebeneficialinengagingdifferent intelligencesof students fortheiroptimallearning.Futurestudiescouldalsoaimtoexpandtherepertoireoflearningactivitiesandmappingthisexpandedrepertoireontothemultipleintelligencesofstudents.

ThefindingsinthisstudyindicatedthatChinesegiftedstu-dentsinthissampleperceivedtheirstrengthsininterpersonal,intra-personal,andverbal-linguisticintelligencesandtheirweaknessesinbodily-kinestheticandnaturalistintelligences.Theyalsoindicatedgreaterpreferencesinlearningactivitiesrelatedtoverbalinteractions(Discussion,Lecture,PeerTeaching),andtheirleastpreferredlearn-

Table 4 Profiles of Multiple Intelligences of Students With Less

or Greater Number of Learning Preferences LessNumber GreaterNumber ofLearning ofLearning Preferences Preferences (n=315) (n=289) EffectSizeIntelligences M Sd M Sd f (1,602) partialη2

Verbal-linguistic 11.63 2.07 13.30 1.69 118.05* .16Musical 11.43 2.71 12.92 2.25 53.27* .08Logical-mathematical 11.52 2.24 12.80 1.81 58.32* .09Visual-spatial 10.65 2.46 12.02 2.21 51.92* .08Bodily-kinesthetic 10.24 2.31 11.80 2.25 70.37* .11Intrapersonal 11.66 2.14 13.61 1.50 164.49* .22Interpersonal 12.07 2.00 13.70 1.44 130.07* .18Naturalist 10.21 2.86 12.09 2.48 74.55* .11

note. Students with less number of learning preferences were students who reported two or less learning preferences; students with greater number of learning preferences were students who reported three or more learning preferences. *p < .001.


ingactivitieswererelatedtoSimulations.ItwasplausiblethattheopportunityforSimulationsasatypeoflearningmightbelimitedinHongKongclassrooms.Nonetheless,thisconjectureneedstobetestedinfutureinvestigations.Further,thepresentfindingsalsoindi-catedthatspecificlearningpreferencescouldbeassociatedwithspe-cificintelligences.Students’well-developedintelligencescouldthusbemeaningfullyengagedthroughtheassessmentofstudents’learningpreferencesandaccommodatingthesepreferenceswithcorrespond-inglearningactivities.Forexample,studentswhopreferdiscussionarelikelytobethosewhohavewell-developedconventional(verbal-linguisticandlogical-mathematical)andpersonalintelligences.Ontheotherhand,studentswhoprefersimulationsarelikelytobephysi-callyactive(bodily-kinesthetic),articulate(verbal-linguistic),andsociable(interpersonal).Conversely,teacherswhoinvolvestudentsindiscussionmayhelpengagestudents’conventionalandpersonalintelligences,reinforcingtheseintelligencesiftheyarewelldevelopedandstrengtheningtheseintelligencesiftheyarelessdeveloped.Inasimilarvein,teachersusingsimulationsaslearningactivitiesmighthelpengageanddevelopstudents’differentintelligences,especiallybodily-kinesthetic,verbal-linguistic,andinterpersonalintelligences.Moreimportantly,thepresentfindingsalsosuggestedthatstudentswithagreaternumberoflearningpreferencescouldbecharacter-izedbyspecificprofilesofintelligencesidentifiedbyhighpointsinpersonalandverbal-linguisticintelligences.Insummary,theassess-mentofstudents’profilesofmultipleintelligencescouldbehelpfulindelineatingtheirstrengths,aswellasweaknesses,andteacherswhoaresensitivetostudents’profilesofmultipleintelligencescouldhelpstudentsstrengthentheirwell-developedandlessdevelopedintelli-gencesthroughlearningactivitiescongruentwiththeseintelligences.Futurestudiesmightfocusonhowcongruentorincongruentlearn-ingactivitieswithanindividualstudent’sprofileofmultipleintelli-gencescouldaffectthestudent’slearningandtalentdevelopment.

This study certainly had many limitations. One obviouslimitation,amongmany,wastherepresentativenessofthepresentsample,asallstudentswerenominatedbyteacherswho,atleastinthisstudy,tendedtonominateacademicallyachievingstudents.Thus,itisnotknowntowhatextentthispossiblebiasinsampleselection


mightbereflectedinstudents’profilesofmultipleintelligences,theirlearningpreferences,andtherelationshipsbetweenintelligencesand learningpreferences.Whilehighachieverscouldhavegiftsandtalentsindifferentareasinadditiontoacademicachievement,cautionmustbeexercisedingeneralizingthepresentfindingstothelargerpopulationofChinesegiftedstudents.Thus,theneedforreplicationwithmorerepresentativesamplesofChinesegiftedstudentsshouldbeemphasizedinfuturestudies.

Anotherimportantlimitationofthisstudywastherelianceonself-reportmeasuresforassessingstudents’multipleintelligencesandlearningpreferences—thepresentmeasuresinevitablyassessonlyasmallpartofthetotalspectrumofstudents’abilitiesandlearningpreferences.Specifically,itcanbearguedthatperceivedmultipleintelligencesandlearningpreferencescouldbeverydifferentfrom“actual”multipleintelligencesorlearningstyles,anditisnotknowntowhatextentthetwowouldcorrespond.Accordingly,oneshouldguardagainstthereificationoftheseself-perceptionsandavoidmakingunwarrantedinferencesbeyondtheseself-perceptions.Ontheotherhand,itcanalsobearguedthatusingself-reportsdoeshaveadvantages.Students’viewsandreportsontheirownabilitiesandlearningpreferencesshouldhavemoremeaningforstudents,and students should have expert knowledge about themselves,their unique strengths, weaknesses, needs, and what learningactivitieswouldbestsuitthem.Despitethesepossibleadvantages,theuseofself-reportsinthepresentstudytoassessbothmultipleintelligencesandlearningpreferencesofstudentsalsoraisedtheissueofinflatingtheassociationbetweenmultipleintelligencesandlearningpreferencesbecauseofcommonmethodvariance.Indeed,itwaspossiblethatstudentswhotendedtoratethemselveshighlyonmultiple intelligenceswouldalsotendtogivehigherratingsonpreferencesforspecificlearningactivities,yieldingthefindingsthatstudentswithagreaternumberoflearningpreferenceswouldhaveuniformlyelevatedprofilesofmultipleintelligences.Withthisview,andconsideringthecomplexityandmultidimensionalityofhumanabilitiesandstudents’possiblylimitedclassroomexposuretodifferent learningactivities, theuseofalternativeassessmentprocedures,especiallythoseinvolvingobservationandperformance-


basedassessment,foridentifyingandevaluatingstudents’abilitiesandstrengths inmultiple intelligencesand learningpreferencesshouldbeemphasizedandexploredinfuturestudies(seeChen&Gardner,1997;Sternberg&Grigorenko,2002).

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Author Note

ThisstudywassupportedinpartbyadirectgrantforresearchfromtheChineseUniversityofHongKong.Correspondenceconcern-ingthisarticleshouldbeaddressedtoDavidW.Chan,Departmentof Educational Psychology, Faculty of Education, the ChineseUniversityofHongKong,Shatin,NT,HongKong.E-mail:[email protected].

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