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LearningAnalyticsCommunityExchange
SpecificationsandStandards-QuickReferenceGuidePublicWorkingDocument
By:AdamCooper
Published:31December2015
Keywords:learninganalytics,interoperability,standards,specifications
Thisdocumentpresentsalistofstandardsandspecifications,includingresearchwork,whichmayberelevanttopeoplebuildinglearninganalyticssystems.Abriefsummaryofthecapabilitiesofeachispresented,alongwithnotesonadoptionto-date.Theaiminwritingthisguideistoraiseawarenessofexistingtechnicalspecifications,andtosupportaprocessofduediligencethroughexplorationofpriorartwhenlearninganalyticssystemsaredesigned.
Audience:thisisarelativelytechnicaldocumentaimedatreaderswithexperienceinsoftwaredevelopmentandarchitecture,ordevelopmentofinteroperabilitystandards,etc.
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SpecificationsAndStandards-QuickReferenceGuide
Contents
1. Introduction...................................................................................................................................1
BackgroundtoLearningAnalyticsInteroperability...........................................................................1
ANoteonTerminology:interoperability,specifications,standards.............................................1
TheMotivationforaQuickReferenceGuide....................................................................................1
StatusofThisDocumentandCalltoContribute...............................................................................2
2. HowtoUsethisQuickReferenceGuide........................................................................................2
OnlineCounterpart,theMendeleyGroup........................................................................................2
StructureoftheGuide.......................................................................................................................2
EvaluatingSpecificationsandStandards...........................................................................................4
3. TheQuickReferenceGuide...........................................................................................................6
Specificationstosupportanalytics,independentofdomainofapplication.....................................6
DataExchange...............................................................................................................................6
ModelsandMethods.....................................................................................................................8
Specificationsdealingwithhumanactivitygenerally........................................................................9
Logging...........................................................................................................................................9
Communication...........................................................................................................................11
Specificationsdealingwithlearning-relatedactivity,orrootedineducationortraining...............11
ObjectivesandAssessment.........................................................................................................11
Biographical.................................................................................................................................13
ContextualandReferential..........................................................................................................14
Multi-purposeSpecifications...........................................................................................................14
OtherSpecifications.........................................................................................................................16
Privacy.........................................................................................................................................16
Protocols......................................................................................................................................16
4. GlossaryofAbbreviations............................................................................................................16
5. References...................................................................................................................................18
6. About..........................................................................................................................................26
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1. Introduction
BackgroundtoLearningAnalyticsInteroperabilityTheInstituteofElectricalandElectronicsEngineers(IEEE)definesinteroperabilitytobe:
“theabilityoftwoormoresystemsorcomponentstoexchangeinformationandtousetheinformationthathasbeenexchanged.”
AslearninganalyticsmovesfrombeingaresearchtopictoanICT-supportedserviceatscale,thepropertyofinteroperabilitybecomesmoreimportant.TheLACEBriefing“LearningAnalyticsInteroperability–TheBigPictureinBrief”[1]outlinesarangeofbothshort-termandlong-termbenefitsarisingfromincreasedinteroperability.
ANoteonTerminology:interoperability,specifications,standardsAstheIEEEdefinitionaboveindicates,“interoperability”isapropertyacollectionofsystems.Itmaybeachievedusingarangeoftechnicalandnon-technicalmeans,withwrittentechnicalspecifications–datadefinitions,HTTPRESTAPIs1,XMLbindings,etc.–onlybeingpartoftherecipeforachievingpracticalinteroperability.Thefocusofthisdocumentisonthesetechnicalspecifications.
Itiscommonfortheterm“standard”tobeappliedtosomekindsoftechnicalspecifications,butthereisnouniformityofusage;differentorganisationshavedifferentdefinitionsofwhattheyconsidertobeastandard,andthereismuchloosenessinterminologywhenthewordisusedoutsideformalstandardisationprocesses.Thisdocumentdoesnotadoptahard-and-fastdefinitionofwhatcomprisesa“standard”andtakesaninclusiveviewofthekindsoftechnicalspecificationthatareinscope,capturedinthetitleas“specificationsandstandards.”
TheMotivationforaQuickReferenceGuideTheaimoftheauthorinwritingthisguideistoraiseawarenessofexistingtechnicalspecifications,andtosupportaprocessofduediligencethroughexplorationofpriorartwhendesigninglearninganalyticssystems.Theguideisintendedtoreducetheincidenceofunintentionalinventionordeviationfromwhatalreadyexists.Theauthordoesnotassumethatanyofthesespecificationsarefitforuseastheystand.Inallcases,itwillbenecessarytoundertakeanapplication-andcontext-specificevaluation(seethesection“EvaluatingSpecificationsandStandards”,below),anditmaywellbethecasethatanexistingspecificationprovidesonlysomeinspirationforanewapproach,orthedefinitionsforsomeconcepts.Suchinspirationanddefinition-borrowingmovesusinthedirectionofevolved,andpossiblyconvergent,thinking.Ittakesusinthedirectionofinteroperability,evenifnottothedestination.
Thisguideisacatalogue2oftechnicalspecificationsthatmaybeofrelevancetoimplementationsoflearninganalytics.Foreach,itsetsouttoprovideabriefsummaryofthescopeandpurposeofthetechnicalspecification,alongwithreferencestoevidenceofimplementations,experiencesfromuse,
1Ingeneral,abbreviationsfortechnicaltermswillnotbeexpandedoninthetext.Aglossaryisprovidedas2Anothersourceofinformation,supportedbytheOpenKnowledgeFoundationandorientedtowardsopendataisawebpageentitled“Web-orientedDataFormats”and,maybefoundathttp://dataprotocols.org/data-formats/.
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critique,etc.Evidencefromuseforlearninganalyticsisprioritised,andtheapproachtakenisoneofprovidingindicativeevidenceratherthanacomprehensiveaccount.
StatusofThisDocumentA“publicdraftforcomment”ofthisdocumentwaspublished9December2014.Thedraftwasreleasedasauseful,butincomplete,workinthehopethatitwill:
1. bringexistingworkofrelevancetolearninganalyticsinteroperabilitytotheattentionofmorepeople;
2. stimulatecontributionfrompeoplewithknowledgeandexperience;and3. stimulateexperimentationandreportingontheresultsoftrials.
BasedonfeedbackfromthecommunityandworkintheInteroperabilityworkpackageoftheLACEprojectthisdocumenthasbeenupdated(byToreHoel)tothecurrentversion.
Alsointhisversionoftheguide,evidenceisfrequentlylacking.Thisispartiallyaconsequenceofthestageofinvestigation;however,theshortageofevidenceisalsocontributedtobyafailuretopublishtheresultsofexperimentation–bothsuccessesandfailures-orduetoabsenceofpracticalexperienceofusingthespecificationinalearninganalyticssystem.
2. HowtoUsethisQuickReferenceGuide
OnlineCounterpart,theMendeleyGroupTheGuideisanaugmentedsnapshotofthecontentsoftheMendeleyLearningAnalyticsInteroperabilityGroupError!Bookmarknotdefined..TheLACEprojecthasaddedtothecontentsintheprocessofongoingdeskresearch.TheMendeleyGroupreferencesarebrowsableby“tags”thatmatchthestructureofthisdocument,butalsousestagstodistinguishbetweentechnicalspecifications,consensus-based“standards”,evidence,andtoidentifythespecificationbyname/abbreviation3.Inthiscase,thelabel“standards”indicatesasubjectiveassessmentofwhetherornotatechnicalspecificationhasbeenarrivedatbysomekindof,maybelimited,consensusprocess.Theauthorfindsthisdistinctiontobethemostusefulprimafaciewayofprioritisingspecificationsforreview(butseethecommentsonevaluation,below).
StructureofthisGuideThisGuideincludesspecificationswithgeneralapplicabilitytoanalyticsthroughtothosethatarespecifictothedomainoflearning,education,andtraining.Withinthisspectrumarespecificationsthatcapturehumanactivitybutarenotpurelyrelevanttolearninganalytics.Thesedistinctionshaveinformedtheapproachtostructuringthecatalogueofspecifications,whichborrowsfromthebroadcategoriesoutlinedinthe“BigPicture”[1].Thestructureisasfollows,anditshouldbenotedthatthetermsinquotationmarksmatchtagsusedintheMendeleyGroup.
3Thetagsusedaredocumentedathttp://bit.ly/1wmc7Gc
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Specificationstosupportanalytics,independentofdomainofapplication
• “Dataexchange”-generalpurposeformatsandstructuresforaccessingortransferringdata,includingcomputedresults,predictions,andstatisticalmetadata.Thiscategoryisonlyusedwhennodomain-specificcategorywouldbesensible.
• “Modelsandmethods”-thespecificationdealswithanalyticalmethod(algorithmic,orworkflow)andmodelsproduced(statisticalormachine-learning).
Specificationsdealingwithhumanactivitygenerally
• “Logging”–inwhichhighlevelsemanticsarespecified,possiblywithanoptiontoincludeapplication-specificvocabularies.Thiscoverseverythingfromatomicactivitycapturethroughtocaptureofcompoundkindsofactivity.Technology-levellogssuchasweb-serversproducearenotincluded.Thiscategoryisreservedforcaseswherethespecificationisnottargetedataparticularkindofactivity4.
• “Communication”-thefocusofattentionisactivityinforums,chat,collaborativeediting,etc,andtherearespecificreferencestothesemanticsofsuchactivitiesinthespecification.
Specificationsdealingwithlearning-relatedactivity,orrootedineducationandtraining
• “Objectivesandassessment”-thereisspecificreferencetothesemanticsofassessment,includingstatementsofachievement,inthespecification.Thiscategoryalsoincludesintendedlearningoutcomes,inferredknowledge,datapertainingtotheassessmentinstrument,andalsotorecordsofself-regulatedlearning.
• “Biographical”–thespecificationexpressesdataaboutpeopleotherthantheiractions.Someofthiswouldbedescribedas"demographic"forpopulation-levelstudies.Thisincludes,forexample,socialrelations.
• “Contextualandreferential”-thisisaverylooseclassofdatathatmayassistintheinterpretationofactivitydata(e.g.identificationofthecoursebeingstudied,theaccessdeviceused)andwhichmaysometimesexistwithoutanyoneengagingintrackedlearning(e.g.learningdesigns,nationalcurriculumstandards).Contentmetadatawouldappearinthiscategory,butresourcemetadataspecificationsarenotcurrentlyincludedintheGuide.
OtherSpecifications
Thesedonotnaturallyfitintotheabovestructurebutarenonthelessofrelevancetolearninganalytics:
• “Privacy”–specificationsconcernedwithdisclosureanduseofperson-relatedinformation.GeneralinformationsecurityspecificationsarenotinscopeforthisGuide.
• “Protocol”–thespecificationincludes,orlargelydealswith,themethodsbywhichdataisexchanged,i.e.APIs.
4Specificationsareclassifiedandpresentedinthemostspecialisedclasswhenthiscanbedetermined.Forexample,althoughthereisaclearconceptualhierarchymovingfromdataexchange->logging->resourceuse->objectivesandassessment,QTIResultsonlyappearsin“objectivesandassessment”.
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EvaluatingSpecificationsandStandardsThisGuidehasintentionallyavoidedpresentinganyvalue-ladencommentsonthelistedspecificationsforseveralreasons:
1. Therewillusuallybeanumberoffactorsarisingfromtheapplicationinquestion,andthecontextofuse,whichdeterminerelevance.
2. TheGuideisintendedtosupportactivityacrossaspectrumrangingfromadoptionwithintenttoconformtoanexistingspecification,throughtogaininginspirationforatechnicaldesign.
3. Thestrengthofevaluation,andhowitisparameterised,shoulddependonrisk,andriskvariesbymanyordersofmagnitudesbetweenaprivatelearning-experimentandamulti-millionEuro/Dollarinvestment.
Inpractice,itwillusuallybenecessarytomakedecisionsabouttherelevanceofatechnicalspecificationinaricherwaythansimplyaskingwhetherornotitmeetsaparticularsetofcriteriainadefinitionof“astandard”.Forhighrisksettings,theCommonAssessmentMethodforStandardsandSpecifications(CAMSS)[2]providesausefultemplate.Thiswasdesignedforselection,andpossiblymandation,ofstandardsbyEuropeangovernments;thetypicalscaleofcostforadoption,andhostilityfrommanyinthesoftwareindustryandthepress,demandsadefensivestance.Itisused,forexample,bytheUKGovernmentOpenStandardsBoard5.Thecurrentauthorisrathercautiousaboutchecklist-basedstandardsevaluation,particularlyintheabsenceofapilotprojecttoproperlyunderstandtheissues,althoughchecklistsprovideausefulsetofpromptsofaspectstoconsider[3].
Keyquestionstoasktypicallyrevolvearound:
1. Copyright,licenceandpatentconsiderations.Whatfreedomsdoyouhave?Thelowestriskscenarioisthatallcontributorstoaspecificationhaveexplicitlywaivedallrights,andtheIPRisownedbyanorganisationwithlong-termsustainabilityandacommitmenttofindanentitytoinheritspecificationsshouldtheywinddown,andwithaliberallicencetoimplementthespecification.Realityisoftensomedistancefromthisidealscenario,andtherearealsoissueswhereanovertly-acceptableideasuchasFRAND(fair,reasonableandnon-discriminatory)termsarelimitinginpractice(inthiscaseitisthatOpenSourceSoftwareenshrinesthefreedomtore-distributewhichisatoddswithrequirementsforroyaltypayment,nomatterhow“reasonable”).
2. TechnicalQualitymaybebestjudgedfromexperiencebutusefulproxiesinclude:theextentofparticipationinthespecificationdevelopmentandthedegreetowhichthereisevidenceofmeetingrequirementsfrommultiplestakeholders(whichisonereasonwhyconsensusprocessingeneralandOpenStandards6areimportant);theexistenceofindependentimplementations;evidenceofuseinanequivalentcontextandforasimilarapplicationtotheoneforwhichthespecificationisbeingevaluated.
3. Levelofadoption.Sincethepointofadoptionistopromoteinteroperability,valuescalesassomefunctionofadoption.Thisisnottosaythatwideadoptionisahardrequirement;anexistingspecificationwithsomeevidenceoftechnicalqualityislikelytobeagoodpointof
5https://gds.blog.gov.uk/2013/05/17/open-standards-board-sets-the-direction/6See,forexamplethe“OpenStandPrinciples”,http://open-stand.org/about-us/principles/
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referenceforaone-offsystemintegration,nomatterhowwidelyimplemented,andanewspecificationthatsolvesrealproblemsmayquicklybecomewidelyimplemented.
4. Stabilityandpersistence.Thisisoftencoupledwiththepreviousthreepoints;awidelyadoptedspecificationfoundedoncollaborationamongmultiplestakeholderswithclearIPRandtransparentlygovernedistrustable.Aspecificationfrom,andlargelyusedby,asingleentityshouldbetreatedwithcautionbecausethereisminimalbarriertochangeorabandonment.
5. Fittoyourtechnicalarchitecture,althoughevenincasesofpoorfit,itmaybepossibletoborrowandadaptpatterns,datadefinitions,etc.
6. Fittoyourorganisationalandoperationalcontext.Whatassumptionsdoesthespecificationmakeaboutworkflow,patternsofactivity,roles,etc?
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3. TheQuickReferenceGuideNote:thedescriptionsbelowaretakenfromthespecificationwhenasuitableabstractisavailable.
Specificationstosupportanalytics,independentofdomainofapplication
DataExchangeThesedataexchangespecificationscollectivelycoverawiderangeofapplicationscenariosfrombeinginputformatsforbatchdataminingthroughtofeedinglivedatatodashboardwidgets,foruseaspublishingformatsorforarchivalandaudit,etc.
ARFF–Attribute-RelationFileFormatAnARFF(Attribute-RelationFileFormat)fileisanASCIItextfilethatdescribesalistofinstancessharingasetofattributes.ARFFwasdevelopedbytheMachineLearningProjectattheDepartmentofComputerScienceofTheUniversityofWaikatoforusewiththeWekamachinelearningsoftware.ARFFfilescanbetreatedasCSVoncetheheadermetadataisremoved.
TechnicalSpecification[4]EvidentialRepositoryofmorethan600ARFFdatasets[5]SoftwareARFForiginatedwithWeka,asuiteofmachinelearningsoftwaredevelopedattheUniversityofWaikato[6]Alsosupportinseveralothertools,e.g.R[7]
CSV+-ModelforTabularDataandMetadataontheWebTabulardataisroutinelytransferredonthewebas"CSV",butthedefinitionof"CSV"inpracticeisveryloose.Thisdocumentoutlinesabasicdatamodelorinfosetfortabulardataandmetadataaboutthattabulardata.Italsocontainssomenon-normativeinformationaboutabestpracticesyntaxfortabulardata,formappingintothatdatamodel,tocontributetothestandardisationofCSVsyntaxbyIETF.Variousmethodsoflocatingmetadataarealsoprovided.
TechnicalW3CWorkingDraft[8]EvidentialTheWorkingDraftisbackedupbyacompilationofusecasesandrequirements[9]
DSPL–GoogleDataSetPublishingLanguageADSPLdatasetisabundlethatcontainsanXMLfileandasetofCSVfiles.TheCSVfilesaresimpletablescontainingthedataofthedataset.TheXMLfiledescribesthemetadataofthedataset,includinginformationalmetadatalikedescriptionsofmeasures,aswellasstructuralmetadatalikereferencesbetweentables.DatasetsdescribedinthisformatcanbeprocessedbyGoogleandvisualizedintheGooglePublicDataExplorer.
TechnicalDSPLXMLSchema[10]EvidentialGooglePublicDataExplorerusesDSPL[11]
JSON-statTheJSON-statformatisasimplelightweightJSONformatfordatadissemination.Itisbasedinacubemodelthatarisesfromtheevidencethatthemostcommonformofdatadisseminationisthetabular
TechnicalSpecification[12]Evidential
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form.Inthiscubemodel,datasetsareorganizedindimensions.Dimensionsareorganizedincategories.
AdoptersofJSON-statincludestatisticsagenciesintheUK,Norway,Sweden,Catalunya,andGalicia7.SoftwareJavaScripttoolkit[13]sRpackage[14]
GraphMLGraphMLisacomprehensiveandeasy-to-usefileformatforgraphsbasedonXML.Itconsistsofalanguagecoretodescribethestructuralpropertiesofagraphandaflexibleextensionmechanismtoaddapplication-specificdata.ThisdocumentspecifiessyntaxandprocessingrulesfortheGraphMLlanguagecore(structurallayer)andtwoGraphMLextensionsthatallowaddingbase-typeattributesandparsinginformation.
TechnicalSpecificationversion1.1[15]EvidentialImplementedinSNAPP8[16]SoftwareGephi9implementationofGraphML[15]
Thereareseveralotherformatsforsocialnetworkdata,GraphMLhasbeenselectedbecauseofitspopularityandsupportintoolsalreadyusedforlearninganalytics.
ODataTheOASISODataTC[TechnicalCommittee]workstosimplifythequeryingandsharingofdataacrossdisparateapplicationsandmultiplestakeholdersforre-useintheenterprise,Cloud,andmobiledevices.AREST-basedprotocol,ODatabuildsonHTTP,AtomPub,andJSONusingURIstoaddressandaccessdatafeedresources.Itenablesinformationtobeaccessedfromavarietyofsourcesincluding(butnotlimitedto)relationaldatabases,filesystems,contentmanagementsystems,andtraditionalWebsites.ODataconsistsofasuiteofspecifications.ODatawasformerlyaMicrosoftOpenSpecificationPromisespecification.
TechnicalWorkinggrouphomepage[17]EvidentialUseinQuestionmarkOpenAssessmentPlatform[18]SoftwareCatalogueofODataproducersandconsumers[19]
PiwikReportingAPIThePiwikReportingAPIprovidesaccesstotypicalwebanalyticsreportdata.NB:thisisasinglesupplierspecificationbutPiwikisOpenSourceSoftware.
TechnicalReportingAPIdeveloperdocumentation[20]EvidentialPilotprojectusingPiwikforalearninganalyticsdashboard[21]
RDFDataCubeVocabularyTherearemanysituationswhereitwouldbeuseful Technical
7Linkstotheseexamplesmaybefoundathttp://json-stat.org/8SNAPPistheSocialNetworksAdaptingPedagogicalPracticetool,whichperformsreal-timesocialnetworkanalysisandvisualizationofdiscussionforumactivity.http://www.snappvis.org/9Gephiisacross-platforminteractivevisualizationandexplorationplatformforallkindsofnetworksandcommonlyusedforsocialnetworkvisualisation.http://gephi.github.io/
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tobeabletopublishmulti-dimensionaldata,suchasstatistics,onthewebinsuchawaythatitcanbelinkedtorelateddatasetsandconcepts.TheDataCubevocabularyprovidesameanstodothisusingtheW3CRDF(ResourceDescriptionFramework)standard.ThemodelunderpinningtheDataCubevocabularyiscompatiblewiththecubemodelthatunderliesSDMX(StatisticalDataandMetadataeXchange),anISOstandardforexchangingandsharingstatisticaldataandmetadataamongorganizations.TheDataCubevocabularyisacorefoundationwhichsupportsextensionvocabulariestoenablepublicationofotheraspectsofstatisticaldataflowsorothermulti-dimensionaldatasets.
W3CRecommendation[22]EvidentialStudyonusingRDFDataCubeforLinkedOpenData,includingreferencestolearninganalytics[23]Listofimplementationsandconformancereports[24]
SDMX–StatisticalDataandMetadataeXchangeTheStatisticalDataandMetadataExchange(SDMX)initiativesetsstandardsthatcanfacilitatetheexchangeofstatisticaldataandmetadatausingmoderninformationtechnology,withanemphasisonaggregateddata.SDMXissponsoredbytheBankforInternationalSettlements,theEuropeanCentralBank,EUROSTAT(StatisticalOfficeoftheEuropeanUnion),theInternationalMonetaryFund,theOrganizationforEconomicCo-operationandDevelopment,theUnitedNations,andtheWorldBank.ThespecificationsincludeXMLandUN/EDIFACTsyntaxes,aregistryspecificationandwebserviceguidelines.
TechnicalSDMXversion2.1[25]Evidential/SoftwareSDMXimplementations[26]
Seealso:PAR(returnofpredictionstopartnerinstitutions).
Inadditiontothesewaysofexchangingthedata,therearealsoseveralspecificationsintendedtobeusedtodescribedatasetsordatasetcatalogues,suchasDCAT[27]andaproposaltoWebSchemas[28].Thesehavepotentialrolesforarchivalandpublishing.
ModelsandMethodsPMML–PredictiveModelMarkupLanguageThePredictiveModelMarkupLanguage(PMML)isanXML-basedlanguagewhichprovidesawayforapplicationstodefinestatisticalanddataminingmodelsandtosharemodelsbetweenPMMLcompliantapplications.
TechnicalPredictiveModelMarkupLanguagev4.2[29]EvidentialAwhitepaperonstandardsinpredictiveanalytics[30]OpenAcademicAnalyticsInitiative(OAAI)projectfindings[31]PaperonOAAIinJournalofLearningAnalytics[32]Aneducationaldataminingexample[33]SoftwarePMMLissupportedbyarangeofsoftwarefromdesktop,e.g.Knime,tocloud-based,e.g.ZementisPMMLpackageforR[34]PMMLpre-processingandGooglepredictionAPI[35]
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VTL–ValidationandTransformationLanguage(SDMX)ThepurposeoftheVTListoallowaformalandstandarddefinitionofalgorithmstovalidatestatisticaldataandcalculatederiveddata.Thevalidationisassumedtobeaparticularcaseoftransformation,thereforetheterm“Transformation”ismeanttobemoregeneralandtoincludealsothevalidation.
TechnicalVersion1.0publishedin2015[36]
Specificationsdealingwithhumanactivitygenerally
LoggingActivityStreamsThisspecificationdetailstheserializationofastreamofsocialactivitiesusingtheJSONorAtomformats.Activitiesareimportantinthattheyallowindividualstoprocessthelatestnewsofpeopleandthingstheycareabout.
TechnicalVersion1.0specifications(JSONandAtom)[37]JSONActivityStreams2.0[38]EvidentialActivityStreamsprovidessomeofthefoundationforxAPI(seebelow).LearningRegistryParadataSpecification[39]Seealso“Additionalresources”,below.SoftwareActivityStreams2.0JavaReferenceImplementation[40]
CaliperAnalyticsIMSCaliperAnalyticsprovidesLearningMetricProfilesasstandardiseddescriptionsofactionsandrelatedcontexts.LearningSensorAPIscommunicatewithLearningEventsStoretoaggregatemetrics.ThespecificationleveragesexistingIMSspecifications,likeLearningToolInteroperability(LTI)specification,LearningInformationServicespecification,andQuestion&TestInteroperabilityspecification
TechnicalPublisheddocumentsandcode:IMSCaliperAnalyticsBackground;IMSCaliperAnalyticsBestPracticeGuide;IMSCaliperAnalyticsImplementationGuide;IMSCaliperAnalyticsConformanceandCertificationGuidev1.0,andCaliperAnalyticsv1PublicReposforSensorAPIs[97]EvidentialIMSGlobaloffersConformanceCertificationformembers.SoftwareCaliperEventStoreisavailableasaGitHubdownload
CAM–ContextualisedAttentionMetadataContextualizedAttentionMetadata(CAM)enablesthecaptureofuserinteractionswithlearningenvironments,allowingmodellingofauser'shandlingofdigitalcontentacrosssystem
TechnicalCAMSchema[41]Evidential
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boundaries.AsCAMwasdevelopedtodescribeasmanytypesofattentionmetadataaspossible,CAMrecordsofausercannotmerelydescribetheuser'sfociofattentionbutratherhisentirecomputerusagebehaviour.CAMcanbeanalysedtoprovideanoverviewaboutwhere(i.e.withwhichapplication)andwhenanactiontakesplaceandwhathappensintheenvironment.CAManalysesenablethediscoveryofpopularity,usageburstsandtrendsoftools.Itcanalsouncoverpatternslikeapplicationsbecomingunpopular,risingstarsandnewapplicationstakingoverolderapplications.Informationaboutwhenanactiontakesplacecanbeusefulincontrolledenvironmentssuchasformallearningenvironmentswhereactivitiesareusuallyscheduled.
Atrust-basedrecommenderforteachers[42]UseinaPersonalLearningEnvironment[43]Bookchapter[44]Seealso“Additionalresources”,below.
TMF–PittsburghScienceofLearningDataShopTutorMessageFormatThisguideisintendedforasoftwaredeveloperwhowantstoevaluate,implement,orupdatelogginginaneducationaltutoring[ITS]application,orconvertexistinglogscreatedbyatutoringapplication.Inaddition,adeveloperworkingonalearningenvironmentcoulddescribetheapplication'seventsintheformatdescribedinthisdocument.
TechnicalSpecificationv4[45]EvidentialDataShop,anonlinerepositoryandanalyticaltoolsusingTMF[46]BookchapteronDataShop[47]
xAPI-ExperienceAPITheExperienceAPI[alsoknownasTinCanAPI]specificationdescribesadatamodelandwebservicethatallowsstatementsofexperiencetobedeliveredtoandstoredsecurelyinaLearningRecordStore(LRS).Thesestatementsofexperiencearetypicallylearningexperiences,buttheAPIcanaddressstatementsofanykindofexperience.TheExperienceAPIisdependentonActivityProviderstocreateandtracktheselearningexperiences;thisspecificationprovidesadatamodelandassociatedcomponentsonhowtoaccomplishthesetasks.
TechnicalVersion1.0.2specification[48]TinCanRegistryofvocabularies[49]EvidentialxAPIhasbeenwidelyimplemented,includingsomepilotsfocussingonlearninganalytics[21][50]
XES–eXtensibleEventStreamEventlogs,astheyoccurinpracticeandresearch,cantakeaplethoraofdifferentformsandinstantiations.Everysystemarchitecturethatincludessomesortofloggingmechanismhassofardevelopedtheirown,insular,solutionforthistask.XESisanXML-basedstandardforeventlogs.Itspurposeistoprovideagenerally-acknowledgedformatfortheinterchangeofeventlogdatabetweentoolsandapplicationdomains.Itsprimarypurposeisforprocessmining,i.e.theanalysisof
TechnicalXESv2.0[51]EvidentialIEEECISTaskForceonProcessMiningincludeactivitytostandardiseXES[52].DraftofOctober2015ispublised10SoftwareProMv6.4(ProcessMiningFramework)[53]
10http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:20151002_xes
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operationalprocessesbasedontheireventlogs.However,XEShasbeendesignedtoalsobesuitableforgeneraldatamining,textmining,andstatisticalanalysis.
ProMhasbeenusedforlearninganalyticsbutwithafore-runnerdataexchangeformat[54]
Additionalresourcescomparingseveraloftheabove,discussingofrequirementsandissues,describingprototypes,etc:
• “Aggregatingsocialandusagedatasetsforlearninganalytics:dataorientedchallenges”[55]• “AnOverviewofUsageDataFormatsforRecommendationsinTEL”[56]• “ASurveyonLinkedDataandtheSocialWebasfacilitatorsforTELrecommendersystems”
[57](alsoreferstoSIOC,seebelow)
CommunicationAtomSyndicationFormatAtomisanXML-baseddocumentformatthatdescribeslistsofrelatedinformationknownas"feeds".Feedsarecomposedofanumberofitems,knownas"entries",eachwithanextensiblesetofattachedmetadata.Forexample,eachentryhasatitle.TheprimaryusecasethatAtomaddressesisthesyndicationofWebcontentsuchasweblogsandnewsheadlinestoWebsitesaswellasdirectlytouseragents.
TechnicalIETFRFC4287[58]EvidentialAtomiswidelyused(seee.g.ActivityStreams,OData,Leap2A)
SIOCTheSIOC(Semantically-InterlinkedOnlineCommunities)CoreOntologyprovidesthemainconceptsandpropertiesrequiredtodescribeinformationfromonlinecommunities(e.g.,messageboards,wikis,weblogs,etc.)ontheSemanticWeb.
TechnicalSIOCCoreOntologySpecification[59]EvidentialListofSIOCimplementations(2009)[60]Bookchapteronlinkeddataforrecommendersystems[57]
ArgumentationInterchangeFormat[61],which“representsaconsensus‘abstractmodel’establishedbyresearchersacrossfieldsofargumentation,artificialintelligenceandmulti-agentsystems”mayberelevanttospecialcases.
SeealsoActivityStreamsandxAPI
Seealsothesection“Multi-purposeSpecifications”,below.
Specificationsdealingwithlearning-relatedactivity,orrootedineducationortraining
ObjectivesandAssessmentEuroLMAI–EuropeanLearnerMobilityAchievementInformationTheEuropeanLearnerMobilityAchievement Technical
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InformationModel(EuroLMAI)aspirestoaddresstheidentifiedneedforaharmonizedsolutiontotherecordingandexchangeoflearnermobilityinformationwithintheEuropeanEducationArea.TheresultsofthisworkwillcontributetotheefforttowardsinteroperableEuropean-wideITsystemsthatmanageandexchangelearnerachievementinformation.TheEuroLMAIworkhasitsrootsintheEuropeantransparencytoolsandespeciallyintheEuropassframeworkforthetransparentdescriptionofqualificationsandcompetences.Europassprovidesthecommonbasisforthewell-structuredrecordingofalllife-longlearningopportunitiestaken,includingEuropeanHigherEducationstructuresandlearners'privateandinstitution-ownedinformation.
CENWorkshopAgreement[62](CWA,openaccess)EuropeanStandard[63](basedonCWA,feedue)EvidentialOutlineofusewithUKHigherEducationAchievementReport[64]
IMSLIS(LearnerInformationServices)OutcomesManagementThisdocumentcontainstheIMSGlobalOutcomesManagementServicev1.0InformationModel.ThisserviceisusedtoexchangeinformationaboutOutcomes.TheOutcomesdatamodelisbaseduponresultsthatcanbegroupedtogethertoreflectanassessmentactivityforaCourse.TheOutcomesdatamodelisbaseduponLineItems,ResultsandResultValues.Thebusinesstransactionsincludethesimplecreate,read,update,deleteandsimplesearchingoftheOutcomesdatamodel.Thisdocumentcontainsthedefinitionoftheabstractapplication-programminginterfacefortheOutcomesManagementService.
TechnicalVersion1datamodelandAPI(fullLISmodel)[65]Publicdraftv1.0ofadata-compatibleserviceforusewithIMSLTI[66]EvidentialImplementationsincludeBlackboard11andOraclePeopleSoft12.
IMSQTI(QuestionandTestInteroperability)ResultsReportingThisdocumentisareferenceguidetothedatamodelforreportingtheresultsofanassessment,andprovidesdetailedinformationaboutthemodelandspecifiestheassociatedrequirementsondeliveryengines.
TechnicalVersion2.1specification[67]Evidential-
InLOC–IntegratingLearningOpportunitiesandCompetencesInLOCprovidesamodelfortheinformationdefiningbothintendedLearningoutcomesandWorkcompetences(LOCs).Thatinformationisimportanttopersonal,professionalandvocationaldevelopment,humanresourcesandemployeeperformancemanagement,trainingandeducation,
TechnicalCENWorkshopAgreement(part1)[68]EvidentialPython/Djangosourcecode[69]
11https://help.blackboard.com/en-us/Learn/9.1_2014_04/Administrator/070_Server_Management_and_Integrations/System_Integration/Student_Information_System_%28SIS%29/030_SIS_Integration_Types/030_LIS12http://www.oracle.com/us/industries/education-and-research/056908.pdf
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whetherintheworkplaceorinschool,vocationalorhighereducation.InLOChelpswiththemanagementandexchangeoflearningoutcomeandcompetenceinformation,bydefiningcommoncharacteristicsoflearningoutcomesandcompetencesandmodellingtheminformatsthatcanbeshared.
OpenBadges13–MozillaOpenBadgeSpecification(Assertions)OpenBadgesisaninitiativebytheMozillaFoundationtohelprecognizeandsupportlifelonglearningthroughabadgeecosystem.Assertionsarerepresentationsofanawardedbadge,usedtoshareinformationaboutbadgesthatyou'veearnedwiththeBackpack.Withthe1.0releaseoftheOBI(OpenBadgeInfrastructure),therearetwotypesofassertions:hostedandsigned.
TechnicalAssertionsspecification[70]EvidentialSourcecodeforOpenBadgeInfrastructureonGitHub[71]
ThereisalsoaworkshoppaperonHumanPerformanceMarkupLanguage[72],butnopublictechnicalspecificationisavailable.
Seealsothesection“Multi-purposeSpecifications”,below.
BiographicalFOAF–FriendofaFriendFOAFisaprojectdevotedtolinkingpeopleandinformationusingtheWeb.Regardlessofwhetherinformationisinpeople'sheads,inphysicalordigitaldocuments,orintheformoffactualdata,itcanbelinked.FOAFintegratesthreekindsofnetwork:socialnetworksofhumancollaboration,friendshipandassociation;representationalnetworksthatdescribeasimplifiedviewofacartoonuniverseinfactualterms,andinformationnetworksthatuseWeb-basedlinkingtoshareindependentlypublisheddescriptionsofthisinter-connectedworld.FOAFdoesnotcompetewithsocially-orientedWebsites;ratheritprovidesanapproachinwhichdifferentsitescantelldifferentpartsofthelargerstory,andbywhichuserscanretainsomecontrolovertheirinformationinanon-proprietaryformat.
TechnicalFOAFVocabularySpecification0.99[73]EvidentialAtrust-basedrecommenderforteachers[42]
IMSLIS(LearnerInformationServices)PersonThisdocumentcontainstheIMSGlobalPersonManagementServicev2.0.1InformationModel.Thisserviceisusedtoexchangeinformationaboutindividualsincludingname,address,etc.Thebusinesstransactionsincludethesimplecreate,read,updateanddeleteofthePersondatamodelforasingleinstance.Thecorrespondingdatamodel
TechnicalSpecificationv2.0.1[74]EvidentialSeeentryforIMSLIS(LearnerInformationServices)OutcomesManagement
13http://openbadges.org/
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fromtheIMSGlobalLearnerInformationPackage(LIP)v1.0specificationhasbeencombinedwiththeoriginaldatamodelinversion1oftheIMSGlobalPersonManagementServicespecification.
Seealsothesection“Multi-purposeSpecifications”,below.
ContextualandReferentialASN–AchievementStandardsNetworkVocabularyTheAchievementStandardsNetworkCoreRDFnamespaceprovidesURIsforvocabularytermsusefulinRDFamarkupandindevelopmentofapplicationprofilesinthemannerdefinedbyDublinCore.CustomApplicationProfilesforcompetencyframeworksthatmeetparticularnationalorprojectneedscanbedesignedusing:(1)propertiesandclassesfromtheASNCorenamespace,(2)locallydefinedsub-classesandsub-propertiesoftheASNterms;and(3)otherRDFschemasandontologiesusefulinthecontextoftheapplicationprofile.
TechnicalClassandtermdefinitions[75]EvidentialUSeducationalstandardsinASNform[76]AustraliannationalcurriculuminASNform[77]
CEF–CurriculumExchangeFormatThisEuropeanStandardisapplicabletothedigitalexchangeofinformationabouttermsorconceptsrelatingtocurriculuminformation.Thisincludesvaluestobeusedinmetadatatodescribelearningresourcesandlearnerprofiles.ThemainusesofCEFinstancesandrelatedservicesareexpectedtobetheprovisionof:controlledvocabularies;navigationstructures;additionalcurriculuminformation;mappings.
TechnicalEuropeanStandard[78]Evidential-
Seealso:ActivityStreams,xAPI,IMSLISPerson,andInLOC.
Seealsothesection“Multi-purposeSpecifications”,below.
Multi-purposeSpecificationsThissub-sectioncontainsspecificationsthatsupportmorethanoneofthecategories:objectivesandassessment,biographical,contextualandreferential,andcommunication.National/statedatasetdefinitions,asusedbylocalandnationalgovernmenteducationdepartments(e.g.CEDS[79]andCBDS[80]),arealsotypicallymulti-purposeandexpressdataattheleveloftheindividual.ThesearenotincludedinthisGuidebecausetheytypicallymodeldataaccordingtolocaleducationsystemidiosyncrasies,andbecausecomparisonwouldbeaconsiderableexercisewithminimalbenefits.Havingsaidthis,forasingle-jurisdictionimplementation,thesenationalstandardsarevaluablesourcesofguidanceaboutinformationthatislikelytobeavailable,andofrelativelyhighquality,intheITsystemsofeducationalestablishments.
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Leap2A–ePortfolioPortabilityandInteroperabilityLeap2ArepresentsportfolioinformationasanAtomfeed,withoptionalattachments.Itpermitsexchangeofthreedifferentkindsofinformationthatareoftenincludedinportfolios:digitalartefactsmadeorjointlymadebytheportfolioholderinformationabouttheportfolioholder,theirabilities,achievements,experiences,activities,goals,plansandsuchlike;thingswrittenthatarenotspecificallyaboutoneofthethingsabove—thesemayincludeblogposts,comments,reflections,etc.
TechnicalLeap2Aspecification[81]EvidentialImport/exportsupportedinMaharaePortfolio[82]
MOOCdbMOOCdbisashareddatamodelstandardforthedataemanatingfromMassiveOpenOnlineCourses.Thedatamodelisplatformagnosticandisbasedonsomebasiccoreactionsthatstudentstakeonanonlinelearningplatform.Studentsusuallyinteractwiththeplatforminfourdifferentmodes:Observing,Submitting,Collaboratingandgivingfeedback.
TechnicalTheMOOCdbschema[83]EvidentialWorkshopproceedingsdescribingthedevelopmentofMOOCdb[84]
OAAI(OpenAcademicAnalyticsInitiative)DatasetFormatThisisthedatasetdocumentationfromtheOpenAcademicAnalytics(OAAI)Project.OAAIdeveloped,deployedandreleasedanopen-sourceecosystemforacademicanalytics,designedtoincreasestudentcontentmastery,semester-to-semesterpersistenceanddegreecompletioninpostsecondaryeducation.
TechnicalProjecttechnicaldocument[85]EvidentialPaperonOAAIinJournalofLearningAnalytics[32]
PAR–PredictiveAnalyticsReportingFrameworkDataCookbookThePARFrameworkisanon-profitmulti-institutionaldataminingcollaborativecomprisedoftwo-year,four-year,public,proprietary,traditionalandprogressiveinstitutionscontributingtheiranonymizedstudentdataandexpertisetoidentifycommonfactorscontributingtostudentlossandfindeffectivepracticesthatmeasurablyimprovestudentmomentumandprogressioninU.S.highereducation.
TechnicalCookbook[86]EvidentialThedatacookbookisanoperationaltoolforPAR.
Thereareanumberofotherresourcesthatdonotmeritanentryoftheirown,eitherbecausethepublishedworklackssufficientdetail,orbecausetheydescribeasingleimplementationorresearchactivity:
• Chatti,Dyckhoffetal[87][88]describethedevelopmentanduseofacross-platformdatamodeltosupportlearninganalytics,butgivenotechnicaldetails.
• Merceronetal[89]describeadatastructuretobeusedacrossLMSplatformstoexpressbasicactivitydata.
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• TheOpenEdXDatastage[90]describesalosslesstranslationfromtheOpenEdxplatformtrackinglogstoarelationaldatabasestructure.
OtherSpecifications
PrivacyPMRM-PrivacyManagementReferenceModelandMethodologyThePrivacyManagementReferenceModelandMethodology(PMRM)addressestherealityoftoday’snetworked,interoperablecapabilities,applicationsanddevicesandthecomplexityofmanagingpersonalinformation(PI)acrosslegal,regulatoryandpolicyenvironmentsininterconnecteddomains.
TechnicalPrivacyManagementReferenceModelandMethodology(PMRM)V1.0[91]Evidential-
AlthoughPMRMisnotaninteroperabilityspecification,itisincludedintheQuickReferenceGuidebecauseofthesignificanceofthetopicofprivacywhenbuildinginteroperablesystems.OASISisalsothevenueforsomerelatedwork,the“PrivacybyDesignDocumentationforSoftwareEngineers(PbD-SE)”TechnicalCommittee,whichhasrecentlypublisheddraftprivacygovernanceanddocumentationstandardsforsoftwareengineers[92].
UMA–User-ManagedAccessUser-ManagedAccess(UMA)isaprofileofOAuth2.0.UMAdefineshowresourceownerscancontrolprotected-resourceaccessbyclientsoperatedbyarbitraryrequestingparties,wheretheresourcesresideonanynumberofresourceservers,andwhereacentralizedauthorizationservergovernsaccessbasedonresourceownerpolicy.
TechnicalUser-ManagedAccess(UMA)ProfileofOAuth2.0[93]EvidentialUserManagedAccess(UMA)CaseStudies[94]
ProtocolsIMSLearningToolsInteroperabilityTheIMSLearningToolsInteroperability™(LTI)specificationenablestheimplementationofsystemsinwhichremotetoolsandcontentareintegratedintoaLearningManagementSystem(LMS).Version1.xisthenextiterationoftheBasicLTIspecificationthatwasreleasedinMay2010,whileversion2.xaddsmoresophisticationtothespecifiedinteractions.
TechnicalVersion1.1.1(Final)[95]Version2.0(Final)[96]EvidentialNumerousv1implementationscertifiedbyIMS14Pilotuseforlearninganalytics[21]
SeealsothesespecificationswhichincludeAPIdescription:OData,xAPI,IMSLISPerson,andIMSLISOutcomes.
4. GlossaryofAbbreviationsAnoteaboutaudience:thisisarelativelytechnicaldocumentaimedatreaderswithexperienceinsoftwaredevelopmentandarchitecture,ordevelopmentofinteroperabilitystandards,etc.Thisglossaryisintendedtoassistreaderswhoarerelativelylesstechnicalinreadingthedocument,ratherthanprovidingfulldefinitions.
14http://www.imsglobal.org/cc/statuschart.cfm
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Abbreviationsforspecificationsandstandardsthataredescribedinthebodyofthetextarenotincluded.
API ApplicationProgrammingInterface,themeansbywhichsoftwarecomponentsexchangedataordirectprocessing.
CEN theEuropeanCommitteeforStandardisation.
CSV CommaSeparatedValues(alsogeneralisedtoCharacterSeparatedValues),asimpletextualrepresentationoftabulardata.
CWA CENWorkshopAgreement,apre-standardisationconsensusdocumentproducedbyaCENWorkshop,anexpert-basedforum.
HTTP HyperTextTransportProtocol,defineshowmessagesbetweendevicesonthewebareformattedandexchanged“onthewire”.
IEEE theInstituteofElectricalandElectronicsEngineers,whichisresponsibleforsomestandards.
IETF theInternetEngineeringTaskForce,along-establishedinternetstandardisationbodywithanopenprocessbywhichpeoplesubmit“RequestsforComment(RFC)”totheIETFcommunityandselectRFCsarepublishedasde-factostandards.
IMS shortforIMSGlobalLearningConsortium,amembershiporganisationthatissuesconsortiumstandards.
ISO theInternationalOrganisationStandarization,thestandardsbodyrecognisedbytheUN.InformationtechnologystandardsaredevelopedbyISO/IECJTC1,aJointTechnicalCommittee(JTC)withtheInternationalElectrotechnicalCommission(IEC).
ITS IntelligentTutoringSystem.
JSON JavaScriptObjectNotation,atextualhuman-readable,andlight-weightexchangeformatfavouredbywebdevelopersbecausethedataisimmediatelyaccessibletoJavaScriptcodeinawebpage.
OAuth anopenstandardforauthorisationovertheweb,widelyusedtoprovidesecuredelegatedaccesswithoutdisclosingpasswordstothird-partyservices(e.g.toallowanapptoaccessyourtwitteraccount).
RDF ResourceDescriptionFramework,afamilyofW3Cstandardsforexpressinginformationonthewebinamachine-readableway.RDFiscorecomponentofthe“semanticweb”.
RDFa ResourceDescriptionFrameworkinAttributes,aW3CstandardforexpressingRDFconceptsinvariouskindsofweb-nativedocuments(typicallywebpages).
REST REpresentationalStateTransfer,anarchitecturalstyleforAPIsthatexploitsthearchitectureoftheweb,notablytheuseofHTTPandURLs.
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UN/EDIFACT UnitedNations/ElectronicDataInterchangeForAdministration,CommerceandTransport,anapproachtodataexchangethatpre-datesXMLandJSON.
URI UniformResourceIdentifier,themeansbywhichresourcesareidentifiedontheweb.Inpracticalapplications,theURIsusedareactuallyURLs.
URL UniformResourceLocator,aparticularkindofURIthatgivesthe“address”ofaresourceontheweb.AURLisalsoknownasa“webaddress”.
W3C theWorldWideWebConsortium,theprincipalorganisationdevelopingstandardsfortheweb.
XML eXtensibleMarkupLanguage,atextualandhuman-readablerepresentationofstructuredinformation.An“XMLbinding”definesthewayconceptualdataelementsarerepresentedinXML,andmaybeexpressedinamachine-readableformknownasanXMLSchemausingXMLSchemaLanguage.
5. References
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[9] J.Tandy,D.Ceolin,andE.Stephan,“CSVontheWeb:UseCasesandRequirements,”2014.[Online].Available:http://www.w3.org/TR/2014/WD-csvw-ucr-20140701/.[Accessed:17-Nov-2014]
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6. About...
AcknowledgementsTheauthorwouldliketothankToreHoelforcontributingreferencestotheMendeleyGroupandforreviewingthefirstdraft.RebeccaFergusonmadesomehelpfulsuggestionstoimprovereader-friendliness,andBrianKellyprovidedmewiththebenefitofathoroughreviewonpointsofstyleandclarity.
ThisdocumentwasproducedwithfundingfromtheEuropeanCommissionSeventhFrameworkProgrammeaspartoftheLACEProject,grantnumber619424.
AbouttheAuthorAdamworksforCetis,theCentreforEducationalTechnologyandInteroperabilityStandards,attheUniversityofBolton,UK.HeratherenjoysdatawranglingandhackingaboutwithR.HeisamemberoftheUKGovernmentOpenStandardsBoard,andamemberoftheInformationStandardsBoardforEducation,SkillsandChildren’sServices,andisastrongadvocateofopenstandardsandopensystemarchitecture.Adamisleadingtheworkpackageoninteroperabilityanddatasharing.
Aboutthisdocument(c)2015,AdamCooper,Cetis,UniversityofBolton.
LicensedforuseunderthetermsoftheCreativeCommonsAttributionv4.0licence.Attributionshouldbe“byAdamCooper,fortheLACEProject(http://www.laceproject.eu)”.
Formoreinformation,seetheLACEPublicationPolicy:http://www.laceproject.eu/publication-policy/.Note,inparticular,thatsomeimagesusedinLACEpublicationsmaynotbefreelyre-used.
Thisisapublicdraftdocumentforcomment;thelatestversionisavailablefrom:http://www.laceproject.eu/dpc/standards-specifications-quick-reference-guide/.Thefinalversionwillbelinked-tofromthere.
AboutLACETheLACEprojectbringstogetherexistingkeyEuropeanplayersinthefieldoflearninganalytics&educationaldataminingwhoarecommittedtobuildcommunitiesofpracticeandshareemergingbestpracticeinordertomakeprogresstowardsfourobjectives.
Objective1–PromoteknowledgecreationandexchangeObjective2–IncreasetheevidencebaseObjective3–ContributetothedefinitionoffuturedirectionsObjective4–Buildconsensusoninteroperabilityanddatasharing
http://www.laceproject.eu @laceproject