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1、UsingFeatureSelectionandUnsupervisedClusteringtoIdentifyAffectiveExpressionsinEducationalGamesSaleemaAmershi1,CristinaConati1,andHeatherMaclaren11DepartmentofComputerScience,UniversityofBritishColumbia,2366MainMall,Vancouver,BC,V6T1Z4,Canada{samershi,conati,maclaren}@cs.ubc.caAbstract.Educationalga
2、mescaninduceawiderangeofemotions,andsorecognizingspecificemotionsmaybevaluableforanintelligentsystemthataimstoadapttovaryingstudentneedssoastoimprovelearning.Thelong-termgoalofthisworkistounderstandhowuseraffectimpactsoveralllearninginaneducationalgame.Themaincontributionofthispaperisaninvestigatio
3、nintotheuseofanunsupervisedmachinelearningtechniquetohelprecognizemeaningfulpatternsinbiometricaffectivedata.Resultsshowthatthismethodcanidentifyinterestingandsensiblestudentreactionstodifferentgameevents.Keywords:biometrics,affectivemodeling,unsupervisedlearning1IntroductionEducationalgamescanindu
4、ceawiderangeofemotions[4],andsorecognizingspecificemotionsmaybevaluableforanintelligentsystemthataimstoadapttovaryingstudentneedssoastoimprovelearning.Theworkpresentedhereisaninvestigationintobiometricexpressionsofaffectivereactionsexhibitedbystudentsinteractingwithaneducationalgame.Measuringthestu
5、dents’biometricexpressionsallowsustorecordaffectivereactionswithoutinterruptingthestudent’sengagementduringgame-playing.Thelong-termgoalofthisworkistounderstandhowstudentaffectimpactsoveralllearninginaneducationalgame.Otherworkonidentifyingbiometricexpressionsofaffecthaslookedatusingsupervisedmachi
6、nelearningtechniquestoproduceclassifiersthatmaprecordeddatatoaffectivelabels(e.g.,[12])andtoperformfeatureselection(e.g.,[16]).Affectivelabelingofinteractioneventshasinvolvedmethodssuchasvideo-annotationsofspecificemotions(e.g.,[13]).However,wefoundinpreviouswork[3]thatmoststudentstendedtointernali
7、zeallbutthestrongestfeelings,leadingtoalowagreementrateamongstannotators.Collectingself-reportsofemotionduringengaginginteractions[2]isalsoproblematicsincethismethodcanonlyaskaverysmallnumberofhighlydirecte