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USINGPRESCRIPTIVEANALYTICSTOREDUCECOURSEDROPOUT

Dr.RajeevBukralia

CIO&AssociateProvost

UniversityofWisconsin–GreenBay

 

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AGENDA

• PredictiveandPrescriptiveAnalytics• AnalyticsRoadmap

• RecommenderSystemforDropoutReduction• Challenges&Strategies• Q&A

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TypesofAnalytics

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ANALYTICSROADMAP

•  Selectproblemswellsuitedforanalytics•  Assesscapabilitiesandreadinessforanalytics

•  Business•  Needanalysis,businessprocessesandworklows,projectexpertise,performanceindicators,funding

•  Technology•  Technicalexpertise,toolsandapplication,infrastructure

•  Data•  Dataavailability,datagovernance,dataquality,privacyandsecuritypolicies

•  People•  Supportfromstakeholdersandleaders,data-drivenmindsetandorganizationalculture,collaboration,communication,andfeedback

•  Identifygapsandbuildcapabilities•  Internalandexternalenvironmentalscans•  Capabilityandreadinessgaps•  Createandimplementanactionplan

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ANEXAMPLEOFPRESCRIPTIVEANALYTICS:RECOMMENDERSYSTEM

•  Arecommendersystemrecommendssuggestionsorusefulinformationtouserstohelpachievetheirgoals.

•  Recommendersystemshavebecomepopularine-commerce.Examples:amazon.com,netlix.com

•  Recommendersystemscanusepredictivemodelsandexpertknowledgetoidentifystudentswhoareatgreaterriskofdroppingoutofthecourse.Theycansendearlyalertsandpersonalizedrecommendationstostudentsandstaff.

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RECOMMENDERSYSTEMTOPREVENTDROPOUT•  TheSeidmanRetentionFormula(Seidman,2005):Retention=early

identi.ication+(early+intensive+continuous)intervention

Predictiveanalyticsbasedintervention

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STEPSFORBUILDINGAMODEL

•  Selectaproblemwellsuitedforanalytics•  Gatherinformationandunderstandthequestions

•  Interviewstakeholders•  Existingdataandreports•  Literaturereview•  Testassumptions

•  Redeinetheproblem•  Identifyconstructsandvariables•  Preparethedatasetusingbestpractices•  Buildthepredictivemodel

•  Statisticaltechniques•  Machinelearningtechniques•  Comparethepredictiveaccuracyandperformanceofeachmodel•  Cross-testthemodelwithdifferentdatasets

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STEPSFORBUILDINGAMODEL

•  Buildtherecommendersystemusingthepredictivemodel•  Createsystemrulesandexpertrulesinconsultationwithinstructorsandstudentsupportpersonnel.

•  Createrecommendationsforeachruletobuildtherecommendationrepository.Interviewinstructorsandstudentsupportpersonneltocreaterecommendationsforeachrule.

•  Executesystemrulesandexpertrulesandcreatee-mailalerts,alongwiththerecommendation.

•  Createaweb-basedinterfacethatcanqueryappropriatedatabasetablestoshowthepredictionandalerthistorytoinstructorsandappropriatestudentsupportstaff.

 

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Recommendersystemarchitecture

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CHALLENGES

•  Datapreparation(inaccurate,noisy,andmissingdata)•  Complianceandregulationrisks•  Scalability•  Organizationalcultureandbuy-in•  Deploymentissues•  Learningcurve

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STRATEGIES

•  Deineyourneedsclearly,createKPIs•  Performgapanalysisusingtheroadmaptodevelopanactionplanandtomakethe“buildorbuy”decision

•  Usebestpracticesinprojectmanagement•  Conductanin-depthliteraturereviewtoidentifyimportantconstructsandvariablesandappropriatedataminingtechniques

•  Developstrategiestohandleinaccurateandmissingdata•  Developpoliciesfordatagovernance,security,andprivacy•  Variousdataminingtechniquesshouldbecomparedfortheirpredictiveaccuracyandtheirabilitytohandlethedataset

•  Cross-testyourpredictiveandprescriptivemodelswithdifferentdatasets

•  Engageandcommunicatewithstakeholders,incorporatefeedback

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RecommendedReading

•  Norris,D.&Baer,L.(2013).BuildingOrganizationalCapacityforAnalytics.EDUCAUSE

•  Stiles,R.(2012).UnderstandingandManagingtheRisksofAnalyticsinHigherEducation:AGuide.EDUCAUSE

References

•  Seidman,A.(2005).WhereWegofromHere:ARetentionFormulaforStudentSuccess.InA.Seidman(Ed.),CollegeStudentRetention(pp.296).Westport,CT:PraegerPublishers.

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