Merging Education and Brain Science: Exploring the academic landscape and identifying the common ground

Aikaterini Bagiati*

Arthur Jordan Chabowski Moksh Jawa

Sucharita Sahu MIT – Office of Digital Learning

Dated 9.25.16

Abstract In 2016 MIT established the MIT Integrated Learning Initiative (MITili). The mission of MITili is to study learning the MIT way: through rigorous, interdisciplinary research on the fundamental mechanisms of learning and how it can be improved. The Learning x Scales (LxS) project has been developed under the umbrella of MITili. The goal of the LxS project is to support MITili’s mission by suggesting new approaches to learning, while bridging the gap between the disciplines of traditional education and brain science. This technical report presents the group’s first pilot study aiming to acquire a better understanding of the current academic landscape and identify common research ground that can be further used to initiate a possible collaboration or even a merge across the fields studying learning. In order to start exploring the current landscape of research on learning, the group started by mapping out of the most common themes that appeared to be researched, by exploring journals and conferences related to the education and the brain science communities. As a next step the identified themes were qualitatively analyzed to form wider research categories. The analyzed data were then inserted into the Gephi software to create visual representations of the common research ground between the two fields. As a final step a program was developed that allows for a manual search of research topics of interest across both fields.

1. Introduction

1.1TransformingLearningthroughResearchandAppliedPracticeLearning has been explored by different disciplines, such as education, cognitivepsychology, and neuroscience, in different ways over the previous centuries.However these disciplines commonly operate in silos and very rarely, if at all,inform each other. As technology and research regarding learning advance fasterthan ever, “there is a pressing need in higher education for deeper integration ofresearchacrossthefieldsthatimpactlearning”[1].Asaresponsetothisneed,theMIT Integrated Learning Initiative (MITili), founded in 2016, aims to study andimprove learning through rigorous and interdisciplinary research by usingfundamentalmechanisms of learning,which is theMITway. [2] According to theMITili founding declaration, “MITili will draw from fields as wide ranging ascognitive psychology, neuroscience, economics, health, design, engineering,architectureanddiscipline-basededucationresearch(DBER).Theeffortwillstudy

learningfromseveralperspectives.MITiliwillconsiderthefundamentalprocessesbehindmotivation,curiosity,knowledgeacquisition,retention,mastery,integration,creativity,transfer,andself-efficacyattheindividuallevelfrompre-kindergartentoadulthood.Atthesystemlevel,MITiliresearcherswillconsidertopicssuchasschooleffectiveness, schoolsystemdesign, social factors,educationpolicy, theeconomicsofeducation,andtheimpactofsocio-economicstatus.”[2]

1.2LearningXScalesTheLearningXScales(LxS)projectwasinitiatedinFebruary2016,andaimstosupporttheMITilimission.MorespecificallytheLxSprojectaimsto

• Explorethecross-disciplinarylandscapeoflearningatdifferentscales[Neuron,Brain,Classroom,MOOCs,GlobalEducation]

• Maptheexistinglearningrelatedresearchapproachesandidentifyunexploredareasthatwillallowforcross-disciplinaryresearchopportunities.

• Createinterdisciplinarypathways,suchasacross-disciplinaryresearchrepository,toinformnewMITeducational&researchinitiatives.

• Bridgethegapbetweentraditionaleducationandthebrainsciences.• Highlightactionableimplementationsfortherealworld.

1.3 First pilot study: Exploring the academic landscape and identifying commonresearchgroundbetweeneducationandbrainscienceInthesummerof2016,thefirstpilotstudywithintheLxSprojectwasdesignedandimplemented.Thescopeofthispilotstudywastoprovideaninitialunderstandingofthemostcommonlyresearchedtopicswithinthecommunitiesofeducationandbrainscience, toexplorepotentialcommonresearchground,andtouseboth info-graphicsandawebsitetocommunicatetheresults.

2.Methodology

2.1DataCollection Data collection for this study involved the recording and examination of “call forpapers”ofconferencesandjournalsrepresentingnumeroussubfieldsofeducation,as well as brain science. A maximum of 5 conferences or journals were firstidentified for every one of the following subfields: math education, physicseducation, engineering education, biology education, history education, musiceducation, computer scienceeducation, e-learning, andMOOCs.7 conferencesandjournalswereidentifiedunderthemoregenerictermof“education”;11conferencesand journals were identified under the field of “brain and cognition”, and 3additional conferences and journalswere identified and included under the term“neuroscience”. A different data set was collected for the fields of e-learning andMOOCs due to the very special nature of the latter that calls for a distinct

pedagogical approach. The conferences and journals were identified through aGooglesearchviarelevanttermssuchas[subjectname]educationconferencecallforpapers,or[subjectarea]educationjournalcallforpapers.Expertsineveryfieldwerealso contacted within MIT in order to contribute towards the data collection. Tofurther establish validity, in this first pilot study, only conferences organized byuniversities or entities formally related to education were included. For everyconferenceorjournalincludedinthedataset,theresearchtopicsidentifiedunderthe“callforpapers”sectionwerefurthercatalogued.

2.2DataAnalysisMixedmethodswereusedforanalyzingthedata.Asafirststep,thedatawassplitonto2groups,namelydatafromthefieldsofeducation,anddatafrombrainscience.Due to the particular nature of the subfield, a separate group emerged out of theeducation data that includes data related to e-learning with MOOCs. A generalinductiveopencodingqualitativemethod [3]wasused to identify larger thematicresearchcategories.Tworesearchers initiallyexaminedapproximately50%of thedata.Researchtopicswerecolor-codedtoformlargerresearchthemes(samecolorfortopicsthatcouldpossiblybemergedintoonegreatercategory),andsoaninitialset of thematic categories were identified. Table 1 presents a sample of 3conferences on physics education, and includes identified research topics, alongwithhighlightingthecolor-codingschemethatwasfirstapplied.Table 1. Color-coded data sample representing research topics identified by 3conferencesinthefieldofphysicseducationConference/Journals

2016 InternationalConference onPhysics and PhysicsEducation

ICPE 2016: 18thInternational ConferenceonPhysicsEducation

2nd WCPE (WorldConference on PhysicsEducation)

Researchtopic

TeachingStrategies PhysicsTeachingandLearningatElementary,Secondaryand

UniversityLevels

ICTandMulti-MediainPhysicsEducation

PhysicsExperiments PhysicsTeachingandLearninginInformalSettings

MotivationalStrategiesandMetacognition

PhysicsCourseReform In-serviceandPre-serviceTeacherEducation

PhysicsCurriculum

EducationalTechnology PhysicsCurriculumandContentOrganization

Socio-culturalIssues

PhysicsEducationResearch

PedagogicalMethodsandStrategies

PrimarySchoolPhysics

PhysicalProblemsandResearch

HistoryofPhysicsinPhysicsEducation

SecondarySchoolPhysics

PhysicsandInformationTechnology

ICTandMulti-MediainPhysicsEducation

UniversityPhysics

MotivationalStrategiesandMetacognition

VariousTopicsonPhysicsEducation

Socio-CulturalIssues

TeacherProfessionalDevelopment

InternationalPerspectives

Thetworesearchersthenmetwithanexpertwhohadseparatelyanalyzedasampleofthedata.Thethematiccategorieswerefurtherdiscussedandredefineduntilthewhole group came to a consensus with regards to the definitions. At the end, aspresented inTable2,16 thematic categoriesweredefinedand thewholedatasetwasagainanalyzedaccordingtothenewdefinitions.Researchtopicsthatdidnotfitin anyof the thematic categorieswerenot included in thispilot studybutwillbeincorporatedatafuturepoint.Table2:The16thematiccategoriesthatemergedfromthedataanalysisResearchThematicCategory

Definition

Technology Researchregardinghowtechnologycanbeusedtoenhancethelearningexperience.

SocioculturalIssues Researchregardingsocioculturalissues(of,orrelatingto,toacombinationofsocialculturalelementssuchassocioeconomicstatus,race,religion,age,etc.).

Gender Researchregardinghowgenderaffectseducation.Innovations Researchregardinginnovationsthatdevelopwithin/forthedeliveryorcreation

ofcontentandcurriculumintheclassroom.Ethics Researchregardingethicalconsiderationsineducation(bothregardingthe

practiceandthecontenttobetaught).

StudentPsychology Researchregardingstudentpsychologyfactors,suchasmotivation,thatinfluencestudentswhiletheyarelearningandultimatelyaffectretentionandunderstanding.

AssessmentofLearning

ResearchregardinghowteachersoraMOOCsplatformtestwhethersomeonehaslearnedtheirmaterial.

AssessmentofTeaching

Researchregardinghowateachercanbeassessedforthewayhe/shemaintainsaclassroomandteaches.

Assessment&AccreditationofPrograms

Researchregardingprogramassessmentoraccreditation.

TeacherDevelopment Researchtopicsrelatedtohowteachersgetfurthereducatedwithregardstodevelopmentofnewcontent,ornewdeliveryandassessmentmethods.

CollaborativeLearning Researchregardingcollaborativelearning.Curriculumdesign Researchregardingcurriculumdesign.BusinessOpportunities

Researchtopicsrelatedtothebusinessmodels,partnerships,andopportunitiesforfundingthatemergewithineducation,especiallythroughe-learning.

ConnectionswithIndustry/JobMarket

Researchregardinghoweducationconnectstotherealworld,andonhoweducationtranslatestothejobmarketorfurtheremployment.

Policy Researchregardingprinciplesandgovernmentpolicy-makingintheeducationalsphere,aswellasthecollectionoflawsandrulesthatgoverntheoperationofeducationsystems.

HowLearningWorks Researchregardinghowthebrainprocessestheinformationreceivedtoformlearning.

As thepointof thispilotstudywas to identifypotential commonresearchgroundbetween different fields studying learning, an info-graphic was selected by thegroupasamedium to communicate the results.Afterexaminingvariousavailablesoftware packages, the group decided to use Gephi, an opensource software forvisualizing and analyzing large networks graphs. All datawasmanually importedintothegephisoftware.Fiveinteractivegraphswereextractedfromgephi.Thefirstthree independently present the research in the field of brain science, education,andMOOCsinrelationtothe16thematiccategories.Thelasttwoarecomparativeinteractivegraphs.Thefirstcomparesthefieldsofbrainscienceandeducation.Thesecond compares brain science, education, and MOOCs. Once the graphs werecompleted, theyweresharedonawebsitecreated for theproject.Additionalcodewasthenwrittenandaddedtothewebsitetomakethegraphsfullysearchable,sotheusercaneithersearch foraparticularconferenceor theme,and identifywhatother conferences fromdifferent fields include the same research themes aswell.Oncecompleted,alldatawasexportedtoanexcelfileforfurtheranalysis.Figures1,2,and3illustratetheinteractivegraphscreatedforthefieldofeducationand how the user can interact with this graph. In these Figures, blue representsconferencesandjournalsonSTEMeducation,pinkrepresentsMusicandLanguageeducation, green represents Online Education in class, while red represents allremainingconferencesandjournals.The16graycirclesrepresentthe16researchthematiccategoriesidentified.

Figure1:Interactivegraphpresentingalldatagatheredforthefieldofeducation.

Figure2:Interactivegraphhighlightingconferencesandjournalsthatincludetheresearchthemeoftechnology

Figure3:Thecompletesearchablelistaspresentedonthedevelopedwebsite.Listontherightpresentsallconferencesandjournalsthatincluderesearchontechnology

Figures4,5,and6presentthecomparativeinteractivegraphsbetweenthefieldsofbrain science, education, andMOOCs. In the following Figures, yellow representsconferences and journals from the field of brain science, green representsconferences and journals from the field of education, and orange representsconferencesandjournalsonMOOCs.The16graycirclesrepresentthe16researchthematiccategoriesidentified.

Figure4:Comparativeinteractivemaprepresentingallconferencesandjournalsgatheredforthefieldsofbrainscience,education,andMOOCs

Figure5:Interactivecomparativegraphhighlightingallconferencesandjournalswithinthe3fieldsthatincludetheresearchthemeoftechnology

Figure6:Thecompletesearchablelistaspresentedonthedevelopedwebsite.Listontherightpresentsallconferencesandjournalsthatincluderesearchontechnologyamongthefieldsofbrainscience,education,andMOOCs

3.FindingsInthisfirstpilotstudythatincludedamaximumof6conferencesandjournalspergroupinourdatacollection,atotalof217differentresearchtopicswereidentifiedin the field of brain science, a total of 652 topics were identified in the field ofeducation,and53werespecificallyidentifiedasresearchtopicsinMOOCs.Thelargediscrepancywasexpected,consideringthe longhistoryof the fieldofeducation inacademia compared to the relatively shorter existence of the other two fields,especiallythefieldofMOOCs.Whencomparingtheresearchthatappearstobemoreprominentwithinthe3fieldsaspresented inFigure7, someresearch themesappear tobeof common interest.Considering that the scope of this project is to identify and highlight possibleresearch themes that can serve as a starting ground for collaboration across thedifferentfieldsthatstudylearning,thetopicsofTechnology,AssessmentofLearning,Policy, Sociocultural Issues, StudentPsychology,TeacherTrainingandCollaborativeLearningappeartobeofinteresttoallfields.ThethemesofInnovationandBusinessOpportunitiesappeartobeattractiveinthefieldsofeducationandMOOCs.

Figure7:Percentagesofconferencesandjournalswithinthefieldsofbrainscience,education,andMOOCS,includingresearchonthe16researchthemesidentified.

4.FutureStepsAsthisisapilotstudywithalimitedsetofdatacollectedforeachfield,astudyincludingamorecomprehensivedatacollectionwillfollow.Thenextstepoftheresearchgroupistowritethecodethatwillautomatizetheprocessofdatacollectionandstorethedatainadatabase.Thiscanthenbesentasaninputtothefinaldynamicwebsite,whereitwillremainconstantlyupdated.

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HigherEducationReforms.AccessedonSeptember26,2016athttps://oepi.mit.edu/sites/default/files/MIT%20Online%20Education%20Policy%20Initiative%20April%202016_0.pdf

2. http://mitili.mit.edu/about.html3. Thomas,D.R.(2003).Ageneralinductiveapproachforqualitativedataanalysis.

SchoolofPopulationHealth,UniversityofAuckland,NewZealand.AccessedonSeptember26,2016athttp://www.frankumstein.com/PDF/Psychology/Inductive%20Content%20Analysis.pdf