学生减负外文文献翻译.docx
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学生减负外文文献翻译.docx
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学生减负外文文献翻译
学生减负外文文献翻译2019
英文
Apeerassessmentmethodtoprovidefeedback,consistentgradingandreducestudents'burdeninmassiveteachingsettings
OscarLuaces,JorgeDíez,AntonioBahamonde
Abstract
Togradeopen-responseanswersinamassivecourseisanimportanttaskthatcannotbehandledwithouttheassistanceofanintelligentsystemabletoextendtheabilitiesofexperts.Apeerassessmentmethodmaybeusedforthis.Thestudentswhowrotetheanswersalsoplaytheroleofgradersforareducedsetofanswersprovidedbyotherstudents.Thegradesthusobtainedshouldbeaggregatedtoprovideareasonableoverallgradeforeachanswer.However,thesesystemspresenttwocleardisadvantagesforstudents:
theyincreasetheiralreadyheavyworkload,andthegradesthatstudentsfinallyreceivelackfeedbackexplainingthereasonsfortheirscores.Thecontributionofthispapercomprisesaproposaltoovercometheseshortcomings.Thestudentsactingasgradersareaskedtoevaluateanumberofdifferentaspects.Oneofthemistheoverallgrade,butthereareotherannotationsthatcanbeincludedtoexplaintheoverallgrade.Moreover,werepresenttheresponsesgivenbythestudents(textdocuments)astheinputsinalearningtask,inwhichtheoutputsaretheaspectstobeassessed(labelswithanordinallevel).Ourproposalistolearnalltheselabelsatonceemployingamultitaskapproachthatusesmatrixfactorization.Themethodpresentedinthispapershowsthatpeerassessmentcanprovidefeedbackandcanadditionallybeextendedtogradetheresponsesofstudentsnotinvolvedinthepeerassessmentloop,thussignificantlyreducingtheburdenonstudents.Wepresentthedetailsofthemethod,aswellasanumberofexperimentscarriedoutusingthreedatasetsobtainedfromcoursesbelongingtodifferentfieldsatouruniversity.
Keywords:
Peerassessment,Students'burdenreduction,Feedback,Factorization,Preferencelearning
Theassessmentofopen-responseassignmentsisfrequentlyaproblem.ThisisthecaseinmassivecourseslikeMOOCsorevenwhentherearealotofassignmentsduringacourse.Oneoftheoptionstoovercomethisproblemistoavoidopen-responseinfavorofmultiple-choicequestions.However,thissignificantlyreducesthecommunicationbetweenstudentsandinstructorsthatmayinvolvehandlingdifferentformsofdata,includingcomputerprograms,video,audio,andwrittentexts.Thealternativeisforthestudentsthatwrotetheanswerstoalsoplayaroleintheassessment.Peerassessmenthasbeenexploredasanefficientproceduretodealwiththisproblem;seeforinstance(Díez,Luaces,Alonso-Betanzos,Troncoso,&Bahamonde,2013; Formanek,Wenger,Buxner,Impey,&Sonam,2017; Kulkarnietal.,2015; Labutov&Studer,2016; Luaces,Díez,Alonso,Troncoso,&Bahamonde,2015a; Luaces,Díez,Alonso-Betanzos,Troncoso,&Bahamonde,2017, 2015b; Piechetal.,2013; Raman&Joachims,2014, 2015; Sadler&Good,2006; Shah,Bradley,Parekh,Wainwright,&Ramchandran,2013).Ithasbeenacknowledgedasanactivitythatenhancesstudentlearningin Sun,Harris,Walther,andBaiocchi(2015).
However,peerassessmenthasanumberofflawsthatshouldbeaddressedinordertobedeployedmoreextensively.Firstly,peerassessmentmayconsiderablyincreasetheburdenonstudents.Second,thequalityofthefeedbackreceivedbystudentsshouldbeimproved(Gielen,Peeters,Dochy,Onghena,&Struyven,2010; Hovardas,Tsivitanidou,&Zacharia,2014; Liu&Carless,2006; Tseng&Tsai,2007).Inadditiontoagrade,studentsshouldobtainsomeannotationspointingtotheweakandstrongaspectsoftheiranswers. LuandLaw(2012) presentaninterestinganalysisontheeffectsofprovidingfeedbackbothforthestudentsbeingassessed,andforthestudentsactingasgraders.Theauthorsconcludethatfeedbackisclearlybeneficialandtheyalsopointoutsomeaspectsthatmustbeconsideredinordertodesigngoodpeerassessmentprocesses.
Finally,thefactthateachassignmentisevaluatedbydifferentpeerassessorsgivesrisetotheproblemofreachingaconsensustosummarizethosedifferentopinionsintoasinglegradeandafeedback.Thisisnottrivial,aswewillexplaininSection 2.
Inthispaperweexploreamethodtoaddressthementionedissuesinpeerassessmentwhenopen-responsesarewrittendocuments.Toimprovethefeedbackfromanautomatedperspective,weproposetouseasetoflabelsorannotationsthatmaybeattachedtoanswerswithalevel.Theselabelsshouldcovertheexplanationsthatastudentcouldobtainfromapersonalizedassessmentgivenbyaprofessionalinstructor.Wetestedthisproposalinthreecoursesatouruniversitybelongingtodifferentfields:
LawandEconomy.Instructorscouldeasilyexpressthepossibilitiesofannotationsintermsoflabelswithlevels.Ontheotherhand,thestudentswereabletounderstandtheassessmenttaskwithannotationseffortlessly.
Theoutputofpeerassessmentsisadatasetthatmustsomehowbefilteredtoaggregateorreconcilethegradesreceivedbyoneanswerfromseveralstudentsactingasgraderswithoutexperienceinthistask.ThisisusuallytackledusingMachineLearningmethods.Intheexperimentsreportedattheendofthepaper,weprovethatmodelslearnedtoaggregategradescanbeusedtoeasetheacademicworkloadofstudents.
Theideaistoextendtheassessmentmodeltoanswersnotinvolvedinpeerassessmentinanyway.Forthispurpose,weuseacontent-basedapproachsimilartothoseemployedinRecommenderSystems.Inthiscontext,contentsaredocuments(thestudents'answers)thatcanberepresentedusinga bagofwordsrepresentation.Severalapproacheshavebeenproposedtoovercomethelimitationsofthisrepresentationparadigm;seeforinstance(Deerwesteretal.,1990).Inthispaper,weproposeamatrixfactorizationmethodtolearnhowtogradethatincludesamethodtoarrangetheanswersofstudentsinametricspaceaccordingtotheirgrades.
Noticethatwehavetolearntogradeeachoftheaspectsoftheanswerthatneedtobeconsidered:
theoverallgrade,andthelevelofeachofthelabelsorannotationsforfeedback.Wepresentamultitask(Caruana,1997)methodtosimultaneouslylearnalltheaspectstobeassessed,andweshowintheexperimentsthat,infact,thereisaninductivetransferthatimprovesthewholeMachineLearningprocess.
Thecontributionsofthisarticlecanbesummarizedasfollows.Theevaluationofopenresponseassignmentsisataskthatmustbecarriedoutbyanexpert.However,whenthereexistsalargenumberofassignments,whichwilltaketheinstructor(s)aprohibitivetimetoassess,peerassessmentiscommonlyused.Theinherentsubjectivityintheassessment,aswellasthefactthateachassignmentwillreceivedifferentmarksfromdifferentpeerassessors,posestheneedtoaggregatethosescores.Computingtheaverageisrisky,sincewehaveonlyafewassessmentsperassignment,soweneedtouseansmarterapproach.Thus,thehelpofanintelligentsystemcapableofperformingthistaskisneeded.
Generallyspeaking,anintelligentsystemisapieceofsoftwareabletoperformataskwhichrequiressomekindofintelligentbehaviour.Inthiscontext,ourproposedmethodisabletogeneralizethecriteriaofthepeerassessors(graders),goingbeyondaveragingtheirscores,andgettingridoftheirsubjectivityintheassessment.
However,peerassessmententailsanaddedburdentothealreadylargevolumeofworkrequiredtothestudents.Thefirstcontributionofthispaperisamethodthatdoesnotrequireallstudentstoparticipateinthepeerassessment.Thesecondisthatourmethodprovidesfeedbacktostudentsintheformofasummarizedexplanationofthefinalgrade,somethingwhichisnotdoneautomaticallybyotherpeerassessmentmethods.
Thepaperisorganizedasfollows.Firstweexplainthewholeprocessasitisseenbystudentsandinstructorsandthenintroducetheinsightbehindtheapproachpresentedhere.Thefollowingsectionisdevotedtopresentingtheformalsetting.Wethenreporttheexperimentsconductedtoevaluatetheapproachpresentedinthepaper.Weendwiththeconclusionsofthisresearch.
Inthispaper,weaddresstwoimportantissuesinordertoincreasethequalityofthepeerassessmentofwrittenopen-responses:
theneedtoprovideusefulfeedbacktostudents,andtorelievetheirworkload.Theproposalrequiresgraderstoassessanumberofannotationsorlabelsabouttheanswerthattheyareassessing.Theoverallgradeisanotherlabelinthiscontext.Wehavepresentedamethodthatusesamultitaskapproachtosearchforgradingpatternsinalllabelsatthesametime.
Multitaskleveragestheaccuracyofabaselinethatsuccessivelyfocusesoneachlabelseparately.Thus,theassessmentsprovidedbystudentscanbeaggregatedinalistofgradedlabelsthatinformstheirpeersoftheiroverallgrade,aswellasprovidinganumberofreasonsexplainingweakandstrongpointsintheiranswers.
Ontheotherhand,modelslearnedusingthemultitaskapproachcanbeextendedtoanswersnotatallinvolvedinpeerassessment.Theconsequenceisthatapartofthestudentscanberelievedoftheassessmenttask,therebyreducingtheburdenonstudentsinprocessesofthiskind.
Thepaperthuspresentsanintelligentsystemthatallowsimplementingtheassessmentofopen-responseassignmentsinmassivecourses.Ourproposalpresentstwofundamentalcontributionswithrespecttotheexistingliterature.Ontheonehand,itconsiderablyreducesthestudentworkload.Infact,whenstudentsarerequiredtoassesstheworkofothers,theymustspendalotoftimeonthistask.Inourproposal,byexplicitlyusingthetextsoftheanswers,theintelligentsystemisableto
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