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BarbaraMeansCenterforTechnologyinLearning
February6,2013
Next Generation Learning:What Is It? And Will It Work?
Presentation for the annual meeting of the EDUCAUSE Learning Initiative (ELI),Denver, CO.
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Overview
• TheConceptofNext-GeneraonLearning
• Next-GeneraonLearningChallengesWaveI
• EvaluaonFindings• ContrasngWaveIProjects&MOOCs
• TwoViewsontheNatureofEffecvenessEvidenceinLearningTechnologyR&D
• StrategiesforResolvingtheTensions
• FinalThoughts
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NextGeneraonLearning:APersonalView
• BeUerpreparesstudentsforaworldthatvaluesandrewardsdeeperlearning,collaboraon,skilledcommunicaon,selfmanagement,theabilitytoworkacrossdisciplines,andinnovaonpracces.
• Meetseachstudentwhereheorsheisandprovidescontent,pedagogy,andaccessopportuniestomeetindividualneeds.
• Capitalizesontheaffordancesoftechnologyforlearning• Collectsdetaileddataabouttheprocessoflearningthatcanbeusedtodiagnosestudentneeds,providefeedbacktothe
instruconaldeveloper,andcombinewithdatafromotherlearningsystemstogeneratenewinsightsaboutlearningandinstrucon.
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• Addressre-shapingthecurrentlyfragmented,sub-scalemarketforlearningsoluons
• Supportdisrupvechangeinbothsupplyanddemandforeffecve
soluons
• Fundflexibleresourcesthatinnovatorscanusetocreatechange• Breakdowntechnical&instuonalbarrierstoadopng&scalingeffecvesoluons
NextGenera*onLearningChallengesseektopinpointtechnologysolu*ons
thatcanmeasurablyimprovethequalityoflearningexperiencesand
improvestudents’collegereadinessandchancesofcomple*on.
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• Goalofdemonstrangeffecvenessatscale
• MajorgrantfromtheGatesandHewleUFoundaonsto
EDCASE
• SubgrantsfromEDCASEto29WaveIprojectsunder4
challengeareas
• Projectsranfromspring2011tofall2012orspring2013(15or
21months)
NextGenera*onLearningChallengesseektopinpointtechnologysolu*ons
thatcanmeasurablyimprovethequalityoflearningexperiencesand
improvestudents’collegereadinessandchancesofcomple*on.
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TheChallenges:DeeperLearning
• Definion–Richlyinteracvetechnologiesthatincreasestudentengagementandlearningofconceptualcontentand21st-centruryskills
• Example–UniversityofWisconsin-Milwaukee’sU-Pace
– Self-pacedIntroductoryPsychologycourse – Mastery-basedwithshortermodulesandend-of-module
quizzes
− Timelyandtailoredfeedback
− Instructorsupportandencouragement
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TheChallenges:BlendedLearning
• Example–CaliforniaStateUniversityNorthridge
– Redesignedgatewaymathcourseashybridalternaveto
convenonalcollegeAlgebra
o Individualuseofanintelligenttutorforremediangmathskills
o Interacvelecturesusingacommonsetofmaterialsacrosssecons
o Facilitatedgroupworkforanhouraweek
o Instantfeedbackononlinehomework
o Commonstudentassessmentusedacrossallcoursesecons
• Definion–Combinaonsofonlineandteacher-ledinstruconthatimprovelearning,increasecompleon,andlowercosts
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TheChallenges:OpenCoreCourseware
• Definion–High-quality,modular,openlylicensedcourse-warefordevelopmental,gateway,andhigh-enrollmentcorecourses
• Example–CerritosCollege’sProjectKaleidoscope
– Large-scaleprojectpromongtheadoponofopeneducaonresources(OER)
− Targengstudentsfromunder-representedgroups
− 12differentOERcoursesimplementedon9campuses
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TheChallenges:LearningAnalycs
• Example–MaristCollege’sOpenAcademicAnaly*cs
Ini*a*ve
– SakaiApplicaonProgrammingInterfacetosupportcapturingstudents’courseeffortfromSakaieventlogs
– Open-sourceacademicpredicveanalycsmodel,basedonPurdue’sCourseSignals
• Definion–sowareforcollecon,analysis,andreal-meuseoflearningdatabystudents,instructors,andadvisorstoimprovestudent
success
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AssumponsBehindtheIniave
• Thereisasetof“islandsofinnovaon”witheffecvetechnology-supportedsoluonsthatcouldbescaledtootherinstuons.
• Technicalandinstuonalbarriersgetinthewayof
scalingtechnologysoluonsthatimprovelearningandchancesofcompleon.
• NGLCprojectscancreateanimprovedmarketplacewithdevelopersandadoptersworkingtogether.
• Thesepartnershipswilldevelopandsustaineffecvesoluonswiththeresultthatstudentengagement,performance,persistenceandcompleonallrise.
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By the Numbers
29 funded proposals
200 institutions
78 community
colleges
117,000 students currently
served
72,945 students by grant end
[Wave I Planned]
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By the Numbers
29 funded proposals
121 institutions
53 community
colleges
students currently served
63,567 students by grant end
[Wave I Actual to Date]
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NGLC Wave I Participants
• 78% of faculty who used Wave I innovations said they woulduse them again.
• 48% of studentsexperiencing the NGLCinnovations were at 2-year colleges.
• 40% of student participantswere low income (Pell granteligible).
• Average course completionrate was 77% (93% in 4-year colleges and 64% incommunity colleges).
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WhatWeLearned
• MostoftheWaveIinnovaonsdidnotreallyhaveevidenceofeffecvenessbeforethegrantsbegan.
• Manytechnologycomponentswerenotcompletelydeveloped
beforefall2011.
• Themostfrequentlyreportedimplementaondifficulesweretechnologyandstudentresistance.
• Manyfacultywillvolunteertotryoutnewlearning
technologies.
• Mostfacultydidnotviewtheseparcularinnovaonsas
parcularlychallengingtoimplement.
• Butthosewhofelttheinnovaonrequiredthemtochange
thewaytheytaughtweremoreposiveabouttheinnovaon
theyhadused.
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Technology Problems Were the Biggest
Barrier to Implementation
But most instructors did not regard the barriers as major.
Implementa)onBarrier Percent
Technologyproblems(e.g.,access,reliability,ease
ofuse)
57
Studentresistance 41
Lackofalignmentwithmyteachingapproach 11
Didn’tworkwellwithcoursescheduleoralloUed
me
9
Lackofalignmentwithcoursegoals 4
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Instructors Were More “Favorable” Who . . .
• Had volunteered to use the innovation
• Attended training on the innovation
• Said the innovation provided student data and theyused it
• Said online assessment data had affected how theytaught
• Described the innovation’s effect on the way theytaught as “major”
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Instructors Were Less “Favorable” Who
Reported . . .• Technology challenges
• Lack of alignment with their teaching approach
• Lack of alignment with their course goals
• That the NGLC innovation didn’t fit into their allottedclass time
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BroaderImplicaons
• Thecombinaonofcampusimpedimentstoafaststartandlackofsowarereadinessmeantthat15monthswasnotenoughme.
• Manyfacultywillvolunteertotryoutnewlearningtechnologies,andthesevolunteersrespondmoreposivelytoinnovaonsthatpushthemtochangetheirinstruconal
pracces.• Fewonlineandblendedlearninginiavesaresetuptocollectrigorousevidenceoftheirinnovaon’simpactonstudents.
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BarrierstoCollecngRigorousEvidence
• CampuspoliciesorIRBsmayprohibitassigningstudentstocourseswithsignificantonlinecomponentsatrandom.
• CampusIROsmaynotbewillingtoreleasethestudent-leveldatathatwouldbeneededtocontrolforpriordifferencesin
analysescomparingcurrenttopriorcohorts.• It’sdifficulttogetdifferentinstructorstoadministerthesameassessment.
• Valid,reliableassessmentswerenotreadilyavailableformanyoftheprojects’learningobjecves.
MostoftheWaveIinterven*onswerenotmatureenoughtoconsiderstudentoutcomesduringthegranttermasthefinalwordontheireffec*veness.Butstudentoutcomedataareimportantas
benchmarksforimprovementefforts.
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Example:U-Pace
• Compared230studentsin-PacePsychologyto334studentsinconvenonalPsychologycourse
• Posiveeffectson – PercentageofstudentsearninganAorB(ES=+.96) – Coursecompleon(ES=+.35)
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Example:CaliforniaStateUniversityNorthridge
• Compared4,479studentswhotooktheNGLCHybridcourseto1,824studentsfrompastcohortswhotookCSNcollegealgebra
• Posiveeffectson – Coursegrade(ES=+0.49) – Selectedcommonfinalexamproblems(ES=+0.68)
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Example:ProjectKaleidoscope
• Posiveeffectsoncoursecompleonandpersistencecomparedtopriorcoursetaughtbysameprofessorsfor
– BeginningAlgebra(ES=+.51) – IntermediateAlgebra(ES=+.60)
– IntroducontoPsychology(ES=+.48)• Negaveeffectsfor – PhysicalGeography(ES=-.24)
• Nosignificantdifferencefor – DevelopmentalWring(ES=-.14)
– DevelopmentalReading(ES=+.08)
– EnglishComposion(ES=-.34)
– BusinessFundamentals(ES=+.11)
– BusinessManagement(ES=+.01)
– FundamentalsofBiology(ES=+.38)
When students do better or the same with OER, it’s advantageous because
students are spared the costs of textbooks.
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ChallengesforInterprengAvailable
Evidence• Categoriesareill-defined,intervenonsarediverse,andbothtechnologyandpraccearechangingrapidly.
• It’sdifficulttogetapple-to-applescomparisons.Oen
- Instructorisconfoundedwithcoursemedium.
- Mulpleaspectsofinstruconvarybetweencoursesorsecons
withandwithouttheNGLCinnovaon.
- Studentcharacteriscsmayvarybetweencourses,secons,and
cohorts.- Differenalretenoninonlineandclassroomseconscanbias
outcomes.
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BoUomLineonNGLCWave1
• Impactsonstudentoutcomesweremixed.
• TheNGLCassumponthattherewerealreadyproven-effecvenext-generaonlearninginnovaonsthatjustneededmodestsupportfor
scalingprovedtobeincorrect.• TheWaveIinnovaonsdidnotrequirefundamentalchangesonthepartofhighereducaoninstuons,andthemajorityofthemdidnotrequirefacultytomakemajorchangesinhowtheyteach.
• Themajorityofprojectsneededmorethan15monthstomeettheirgoals.
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BoUomLineonNGLCWave1
• Impactsonstudentoutcomesweremixed.
• TheNGLCassumponthattherewerealreadyproven-effecvenext-generaonlearninginnovaonsthatjustneededmodestsupportfor
scalingprovedtobeincorrect.• TheWaveIinnovaonsdidnotrequirefundamentalchangestohighereducaoninstuonsandthemajorityofofthemdidnotrequirefacultytomakemajorchangesinhowtheyteach.
• Themajorityofprojectsneededmorethan15monthstomeettheirgoals.
At the same time we saw . . .
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The Meteoric Rise of MOOCs
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In the News
• HowCalifornia’sOnlineEducaonPilotwillEndCollegeasWeKnowItTechCrunch
• OnlineCoursesNeedHumansto
EducateCNN
• APromisingTimeforOnlineLearning,HigherEducaonKansasCityStar
• Is2013theYearoftheMOOC?
Informa*onWeek
• AreTheyLearningorCheang?Forbes
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• Scores of courses from 33 universities• Over 2.3 million people have signed up for Coursera’s
free courses
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Typical MOOC Features
• Availableforfreeorverylowcost
• Notforcredit(yet)• Designedtobeusablewithverylargenumbersofstudents
• Classpacingandassignmentsandtests
• Automatedscoringofstudentresponsesand/orpeergrading• Mechanismsforpeer-to-peerinteracons
• Bite-sizedlecturesegments
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WhydidMOOCstakeoffwhileNGLC
innovaonsscaledincrementally?• Aerbriefincubaoninauniversity,theywerelaunchedbyexternalorganizaons
• Theseorganizaonswereincenvizedtoscaletheir
innovaonstomanyinstuons,usuallyIHEsarenot.
• Theyreceivedmajorventurecapitalfunding(millions,notthousands)
• Theyembodiedabig,excingidea
• Theyhadtop-notchprogrammingtalent
• TheirleadershadstrongPRskills
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NotReallyaFairComparison
• MOOCsarefreeandopentoanyone
• Registraonisnotthesameascompleon
0
10
20
30
40
50
60
70
80
90
AchievingtheDream NGLCWaveI MOOCs
Series1
Achieving the Dream NGLC Wave I MOOCs
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TwoViewsonEvidenceinaDigitalWorld
• ProducedforOfficeofEducaonalTechnologyof.S.DepartmentofEducaon
• Describesapproachestoevidencegatheringthatcapitalizeondigitallearningdata
• Presentsaguidanceconcerningthestrengthofevidenceneededfordifferentkindsoflearningtechnologydecisions
http://www.ed.gov/edblogs/technology/evidence-framework/
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TwoViewsonEvidenceinaDigitalWorld:
PrevailingModelinEduca*onResearch
EarlyStage
EfficacyStudy
EffectivenessStudy
ProvenInnovation
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TwoViewsonEvidenceinaDigitalWorld:
TechIndustryR&DModel
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TensionBetweenIHEConstraints&Rapid
Scaling• Tradionofindividualfacultydevelopmentoftheirowncoursesreducesinterestinexternallydevelopedcourseware.
• IHEsmoveatanacademic(statelyifnotglacial)pace.
• Courseapprovalandposngpoliciesdelayimplementaonofnewkindsofcourses.
• Instuonsrequireinformingstudentsinadvanceifacoursewillbeconductedlargelyonlineandsomemes
prohibitofferingacourseonlyonline.
• Anycourse-lengthintervenoncanonlyfitin2-3iteraonsayearonagivencampus—prevenngtherapiditeraonswithrefinementthatspeedupimprovement.
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TwoStrategiesforResolvingtheTension
• Gooutsidethecoreformaleducaonsystem(cf.Christensen’sDisrup*ngClass)
– StudyHelp – Games(self-iniated)
– MOOCs
• Collaboraonsamongdesigners,researchers,andfacultytoconduct“micro-trials”
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TheImprovementCycle
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WhySpeedMaUers
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• PathwaytoCollegeMathiniavebringsresearchersandcollegestogethertotackle“apersistentproblemofpracce.”
• Sharedgoalofdoublingthenumberofstudentswhoearn
collegemathcreditwithinayearofconnuousenrollment.• Combinaonsofnewpolicies,coursestructures,supportsand
coursecontenttriedoutatvariousparcipanginstuons.
• Offeringnewcoursesonadigitallearningplaormmakesitpossibletocollectdetailedlearningdataonstudents’
engagementwithindividualproblems.• Groupisexaminingthesedatabothtopinpointaspectsofthe
coursethatcanbeimprovedandtounderstandlocalvariaons.
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FinalThoughts
• Wemaynotbequitesurewhatitis,but“next-generaonlearning”iscoming
• Now isthemetostateandtestdesignprinciplesfornext-
generaonlearning.
• Ascollegeinstruconbecomesincreasinglydigital,there’sawealthofdatathatcanbeanalyzednotonlytoimprovetheparcularproductbutalsototestdesignprinciples.
• Thatrequirescollaboraonsamongcolleges,researchers,
dataminers,andtechnologydevelopers.• Ifwedon’tcollectcredibledataonnext-generaonlearningimpacts,the“winners”willbethosewiththebestPR.
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Headquarters:SiliconValley
SRIInterna,onal
333RavenswoodAvenue
MenloPark,CA94025-3493
650.859.2000
Washington,D.C.
SRIInterna,onal
1100WilsonBlvd.,Suite2800
Arlington,VA22209-3915
703.524.2053
Princeton,NewJersey
SRIInterna,onalSarnoff
201WashingtonRoad
Princeton,NJ08540
609.734.2553
Addi*onalU.S.andinterna*onalloca*ons
www.sri.com
ThankYou
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Pleasecompleteanevaluaonforthistalk.
WhatcouldIdotoimproveit?
