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From context to text

LESLLA learners between situated learning and logic reasoning

Jeanne Kurvers

13th Nordic Conference

Helsingør, 6-4-2017

Overview

•  Literaturereview–  Literacyandmetalinguis6cawareness–  Literacyandprocessingoflinguis6cinforma6on

•  Literacyandcogni6on–  Taxonomicclassifica6on–  Syllogis6creasoning

Afewobserva6ons•  Reading

–  CouldyoureadthisleCertome?–  Hassanstoothache–  Fatmagoestoschool

• Wri6ng:–  Abookofmylife–  Emergentwriters

•  Outofschoollearning–  Somaliinterpreter

Emergent writers

aosevtok Fa6ma

Jahmila

Kwaku

Literaturereview

•  Longlas6ngresearch– Doesliteracyimpactmetalinguis6cawareness?– Doesliteracyimpactlogic/deduc6vereasoning?àToday’stopic

• Morerecentresearch– Doesliteracyimpacttheprocessingof(linguis6c)informa6on

Literacyandmetalinguis6cawareness

•  Phonologicalawareness–  Sounds:(howmanysoundsincat?)–  syllables

•  Wordawareness–  Whatisaword?–  Whatisthelastwordyouheard?–  HowmanywordsinJohntakesthetrain?

•  Printawareness–  Streetsigns,leCers,register

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Resultsmetalinguis6ctasks

•  Onnearlyalltasks:•  non-literatesdifferstronglyfromreaders

–  Excep6on:rhymerecogni6onandsegmenta6oninsyllables

Segmenta6onsentencesandwords

•  Couldyousegmentintopieces(orally)

–  IcomefromthesouthofSomalia

–  Theoldman

–  Intheshop–  Tomatoes

Examplesnon-literates•  IcomefromthesouthofSomalia

–  Icome/fromsouthSomalia

–  Youhavethesouthandthenorth,isthatit?

•  Theoldman

–  Noyoucan't

–  Doyoumeanoldmenandyoungmen?

•  Intheshop

–  No,thatisoneplace

•  Tomatoes

–  Everyoneatomato

–  Intofourparts

–  To/ma/toes

Could someone write this?

•  I live in Holland

•  Outside

•  I was raining yesterday

•  Ten

•  A baby is very old

Examplesnon-literates

•  Yes,becauseIdoliveinHolland.•  Youcouldwrite‘tree’butnot‘outside’•  Ten,yes,thatcanbewriCen•  No,becauseitwasnotrainingyesterday•  Ifitwasrainingyesterday,youcouldwritethatdown.

•  No,ofcoursenot,ababyisnotold.•  Youcouldwriteitdown,butitiss6llnonsense

Impactonlanguageprocessing

•  Repeattable,repeathable(wordandpseudo-word)n•  Verbalfluency

–  Men6onasmuchwordsasyoucanwithap–  Men6onasmuchanimals/foodasyoucan

•  Workingmemory:repeatstringsofdigitsorwords

•  Results:–  no(big)differencesbetweennon-literatesandliteratesinusing

seman6cinforma6on,–  butbigdifferencesinprocessingphonologicalinforma6on

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LanguageProcessing:wordrepe66on(Reis&Castro-Caldas,1997)

% correct Words Pseudo words

Non-literate

92% 33%

Literate 98% 99%

Literacyandcogni6veopera6ons

•  ClaimVygotskyandLuria:literacychangeswaysof(deduc6ve)reasoning

•  Studiesrevealdifferentoutcomes•  However:simplesyllogis6creasoningtasksrevealintriguingconsistentresults.

(Luria,1975;Scribner,1977;Scribner&Cole,1981;Kurvers,2002;Diasetal.,2005;Haan,2007;Counihan,2007)

Examplesresearchonreasoning(Luria,1975)

Taxonomicclassifica6onTheoddoneout-  glass,pan,glasses,boCle-  rifle,bowandarrow,gun,bird-  Saw,hammer,log,axe

Syllogisms:AllbearsonNovaZembla,farupintheNorth,arewhite.Lastyear,mycousinsawabearonNovaZemblaWhatwasthecolourofthebear?

Syllogis6creasoning:AllXareY

Premise-based Non-literates Literates

Luria(1930) 22.5% 100%

Scribner(1997) 22.3% 75%

Scribner&Cole(1981)* 27% 29%/50%

Kurvers(2002) 20% 68%

Haan(2007)Counihan(2007)

14%30%

-66%

* Firstexperiment,percentagesliterates:resp.Vai-literatesandschooledliterates

StudyKurvers(2002)•  Comparisonofthreegroups:

–  preliteratechildren,non-literateadults,low-educatedliterateadults

•  Tasks:–  metalinguis6ctasksandcogni6vetasks

•  Ques6on:Impactofliteracyorsomethingelse?–  Ifchildrendifferfromadults(irrespec6veofliteracyexperience)ànoimpactofliteracy

–  Ifreadersdifferfromnon-readers(irrespec6veofage)àimpactofliteracy

Respondents

• Pre-literatechildren,lasttermKindergarten(N=23)

• Non-literateadults(N=25)•  Low-educatedliterateadults,4yearsprimaryschool(N=24)

•  All:Berber,Somali,Turks,samebackgrounds;adultsecondlanguagelearnersofDutch

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Cogni6vetasks

• RavenSPM:nonverbalIQtest,culturefree(adultsonly)(k=42)

• Taxonomicclassifica6on(k=8)• Simplesyllogisms(k=5)

• TasksconductedinL1(Berber,Turkish,Somali)unlessrespondentpreferredL2.

Cogniton: Raven SPM

ResultsRavenSPM(max=42) Classification

Examplesclassifica6on1

•  MostLiterates–  Picture,becausetheotherthreeareforreading

•  Non-Literates–  Picture,becausethatisonthewall–  Newspaper,becauseyoucanthrowitawaywhenyouhavefinishedreading.

–  LeCer,becausethatcomesthroughthepostbox–  Photo,becauseyouneedasonfortheotherthree

Exampleclassifica6on2

• Mostliterates–  Fish

•  Non-literates– Dog,becausewedonoteatdogs–  Rabbit,becausenotusefulforpeople–  Rabbit,doesnotliveintheNetherlands– Dog,becauseadogisallowedinthelivingroom–  Fish,becausetheothersdonotliveinthewater

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Analysisclassifica6on

•  Example:Saw, hammer, log, axe•  Taxonomic

–  (wood)log,becauseotherthreearetools•  Situa6onal/func6onal

–  Youalsoneedthewood,becauseotherwisethereisnothingtosaworhammer

•  Idiosyncra6c–  Thesaw,becauseyoucannotsawwiththeotherthree

Resultsclassifica6on

Child Non-literate Literate

taxonomic 38% 55% 77%

situa6onal 19% 26% 16%

idiosyncra6c 43% 19% 7%

Boxplotsclassifica6ontaxonomic Examplessyllogism

•  AllwomeninMarkeyaremarried•  Fatmaisnotmarried•  DoesFatmaliveinMarkey?

•  Allstonesonthemoonareblue•  Amanwenttothemoonandfoundastone.

•  Whatwasthecolourofthatstone?

ExamplesreasoningSyllogismtask

•  Does Fatma live in Markey? •  Most literates:

–  No, because all women are married there

•  Non-Literates: –  No, because I know Fatma. She lives here. –  How should I know, I have never been there. –  We have to ask Fatma. –  It can not be that there is a country where all

women are married. –  Should I give my opinion, or react on your words?

ExamplesreasoningSyllogismtask

•  What was the colour of that stone? •  Most literates:

–  Blue, because all stones are blue there

•  Non-Literates: –  Black, because it is very hot there –  How should I know, I have never been there –  There are no stones on the moon –  Brown, just look outside. –  I think blue, because the sky is blue. –  Black or white, that depends

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Typesofarguments•  Premisebased

–  Becauseallstonesonthemoonareblue–  Becauseotherwiseheshouldhavehadthreeheads–  Becauseyoutoldmeallstonesareblue–  Ifshelivedthere,shewasmarried

•  Experiencebased–  BecauseIhavebeeninAmsterdam–  IknowFatma,sheismarried–  Becauseofthecolorofthewater–  Lookoutside,allstonesarebrown–  WehavetoaskFatma–  Peopletoldmeitisanicecity

TypesofargumentsCtn.•  Discussionpremise(alsoexperiencebased)

–  Itcannotbethatthereisacountrywhereallwomenaremarried

–  Therearenostonesonthemoon– Ahumanpersoncannothavethreeheads

•  Don’tknow/noargument– HowwouldIknow?–  Youdidn’ttellme.

Frequencies arguments by group

Child Non-literate Literate

Premise based 33% 19% 67%

Experience based

39% 75% 27%

No argument 28% 6% 6%

PearsonCorrela6ons

Classifi-ca>on

Raven Printawareness

Meta-linguis>c

L1reading

syllogisms .26 .39* .57** .77** .59**

**p<.000,*p<.05

Differencesbetween5items?

1.  AllCi6esinHollandarenice2.  AllwomeninMarkeyaremarried3.  Allstonesonthemoonareblue4.  AllpeopleonMarshavethreeheads5.  Achmedwentforawalk(allstonesinthe

riverwereyellow)–  Storyembeddedsyllogism

MeansbygroupItem1-5

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Conclusionssyllogism1-5•  Differencesbetweenadultnon-literatesandliteratesstrongestforthemoon(syllogism3)andtheriversyllogism/story(syllogism5)

•  Childrenseemtoprofitfromstory-embedding(syllogism5),non-literateadultsdonot

•  Reasoningnon-literatesinstoryembeddedsyllogismsimilar–  White,becauseGodmadethestoneswhite–  Idon’tknow,Ihaveneverbeenthere–  Blue,becausethewatermadethemblue–  Itmustbeabeau6fulcolour.Wedon’tknowthecolour

Acloserlookatreasoning

•  Isittheques6on?•  Isittheverbalaspect?

–  Boxtask(Haan2007)•  Isitthemeaningoftheconcept“all”?

–  Brothers(Haan2007)• Somein-betweenanswers

Scribner&Cole(1981)

•  E:AllKpellemenarericefarmers.MrSmithisnotaricefarmer.IsheaKpellemen?

•  S:Idon’tknowthemaninperson.Ihavenotlaideyesonthemanhimself.

•  E:Justthinkaboutthestatement.•  S:IfIknowhiminperson,Icananswerthatques6on,butsinceIdonotknowhiminpersonIcannotanswerthatques6on.

Kurvers,2002

•  E:Listen(repeatssyllogisme).DoesFatmaliveinMarkey?

•  Arkem:FatmalivesinMarkey,orinTurkey(laughs).Fatmaisnotmarried,hè?Allwomenaremarried,sheisnot.Butwhyisshenotmarried?

•  E:DoessheliveinMarkey,youthink?•  Arkem:Idon’tknow.Shemightlivethere,orhere.

Verbalaspect:Boxtask(Haan,2007)

•  Threeredboxesinatray.•  Eachboxcontainsaping-pongball(show).•  Closeallthreeboxes,hidethethreeboxes,showoneoftheredboxesagain.

•  “Whatisinthisbox?”

•  Correct:69%•  Respondentscandeduceinforma6onfromthe‘premises’iftheinforma6onispresentedvisually.

Conceptall?(Haan,2007)•  Simplifiedsyllogism

–  Ihavethreebrothers.AllthreeofmybrothersliveinRoCerdam.Janisoneofmybrothers.InwhichcitydoesJanlive?

•  Correct:25%• Whatisthedifferencewiththebox-task?

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Examplebrothers’task•  Exp:WheredoesJanlive?•  Lahcen:[longpause]YoudidnottellmewhereJanlives.YoutoldmethatyourbrothersliveinRoCerdam,butnotwhereJanlives.

•  Exp:AllthreeofmybrothersliveinRoCerdam,allthree.Janisoneofmybrothers.WheredoesJanlive?

•  Lahcen:ThosethreebrothersofyoursliveinRoCerdam,hemaybeoneofthem.

•  Exp:Janisoneofmybrothers.•  Lahcen:ThentheyallliveinRoCerdam

In-betweens

•  Highscoringnon-literates–  Khadizja(1experiencebased,4deduc6ve)– Habiba(1experiencebased,4deduc6ve)–  Lionel(2experiencebased,3deduc6ve)

•  Onnotaskallin-betweensdodifferfromtheaverageofthewholegroup,exceptformetalinguis6cawarenessandprintawareness

Waysofreasoningin-betweens

•  Blue, you told me all stones are blue there •  If she lived in Markey, she would have been

married •  I think yes, although I have never been there. •  No, she is not married. That is not allowed. •  Yes blue, all stones are blue there, isn’t it. •  Shall I give my opinion, or react on your

words?

Compare•  Lahcen:YoudidnottellmewhereJanlives

–  Implicitques6on:“DoyourememberwhatItoldyouaboutJan?”

•  Khadizja:Blue,becauseyoutoldme– Answertoadifferentques6on:“WheredoesJanlivewhenAandBaretrue?”

•  Compareexperienceswithreadingcomprehensioninliteracyclasses

Conclusions•  Literacyopensnewwaysofhandlingverbalinforma6on•  Defaulthandling:rela6ngverbalstatements(separate

facts;exemplars)successivelyandonebyonetotheimmediate,outsidecontext,thedirectworld;situatedcogni6on,combiningandintegra6ngac6ngandspeaking:contextualverbalreasoning

•  Literate(metalinguis6c)handling:rela6ngverbalstatementsfirstofalltoeachother,withinthetext:textualverbalreasoning.

•  Theliterate(metalinguis6c)pointofview:integra6ngverbal(textual)informa6onbeforecontextualchecking

•  Nextstep,‘symbolic’cogni6on,withwithin(inside,text-bound)trueandfalsevalues:symbolicreasoning,formallogic

References Counihan,M.(2007),Lookingforlogicinallthewrongplaces.aninvesGgaGonoflanguage,literacyandlogicinreasoning.Amsterdam:ILLCCastro-Caldas,A.,&Reis,A.(2003).Theknowledgeoforthographyisarevolu6oninthebrain.ReadingandWriGng:AnInterdisciplinaryJournal,16,81–97.Diasetal.,(2005).ReasoningFromUnfamiliarPremisesAStudyWithUnschooledAdults.Psychologicalscience,16,7,550-554.Haan,G.(2007);HowilliteratesinterpretsyllogisGcproblems.MScthesisinLogic.Amsterdam:UVAKurvers,J.,(2002).MetongeleCerdeogen.Kennisvantaalenschri|vananalfabeten.Amserdam:AksantAcademicPublishers.J.Kurvers,I.vandeCraats&R.vanHout(2015)Footprintsforthefuture:Cogni6on,literacyandsecondlanguagelearningbyadults.In:IvandeCraats,J.Kurvers&RvanHout(Eds.)Adultliteracy,secondlanguageandcogniGon.EditorsNijmegen:CenterforLanguageStudies(CLS)Luria,A.(1975),CogniGvedevelopment:itsculturalandsocialfoundaGons.Cambridge:CambridgeUniversityPress.Ong,W.(1982).OralityandLiteracy:TheTechnologizingoftheWord.NewYork:Methuen.Reis,A.,&Castro-Caldas,A.(1997).Illiteracy:Acauseforbiasedcogni6vedevelopment.JournaloftheInternaGonalNeuropsychologicalSociety,3,444–450.Scribner,S.(1977).”Modesofthinkingandwaysofspeaking”in:JohnsonLaird&Wason(eds),Thinking.ReadingsinCogniGveScience.CambridgeUniversityPress,CambridgeMassachuseCsScribner,S.,&Cole,M.(1981).Thepsychologyofliteracy.Cambridge,MA:HarvardUniversityPress.Vygotsky,L.(1978).Mindinsociety:Thedevelopmentofhigherpsychologicalprocesses.Cambridge,MA:HarvardUniversityPress

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Thankyou!

j.j.kurvers@uvt.nl

Doinbetweensdiffer

•  Background(age,schoolingetc)?•  Non-verbalintelligence?• Memory?•  Theconceptall?• Waysofreasoning?• Whatelse?

Correlations

syllogisms literacy classification metalinguistic raven syllogisms Pearson Correlation 1 ,572** ,263* ,772** ,385*

N 62 62 62 62 34 literacy Pearson Correlation ,572** 1 ,431** ,663** ,583**

N 62 62 62 62 34 classification Pearson Correlation ,263* ,431** 1 ,202 ,202

N 62 62 62 62 34 metalinguistic Pearson Correlation ,772** ,663** ,202 1 ,595**

N 62 62 62 62 34 raven Pearson Correlation ,385* ,583** ,202 ,595** 1

N 34 34 34 34 34

Relevant tasks / tests Literacy and raven, adults only

Syllogisms highest corelation with metalinguistic abilities

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig. B Std. Error Beta 1 (Constant) -1,750 1,006 -1,739 ,093

raven -,029 ,031 -,136 -,913 ,369 literacy ,004 ,015 ,052 ,277 ,784 classification -,012 ,237 -,007 -,052 ,959 metalinguistic ,078 ,017 ,826 4,614 ,000

2 (Constant) -1,789 ,668 -2,679 ,012 raven -,028 ,031 -,135 -,929 ,360 literacy ,004 ,014 ,049 ,280 ,781 metalinguistic ,078 ,017 ,825 4,701 ,000

3 (Constant) -1,730 ,625 -2,769 ,009 raven -,026 ,029 -,124 -,900 ,375 metalinguistic ,081 ,013 ,855 6,207 ,000

4 (Constant) -1,903 ,593 -3,210 ,003 metalinguistic ,074 ,010 ,781 7,077 ,000

Predicting syllogisms

Continuum, correlation

Background NL in-betweens Age L2 school textL1

74 25 5 mth 0 yes

76 47 2 mth 0 no

131 37 10 mth 2 Some decoding

Average group 38.8 2.3 0.4 no

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Background in-betweens classification

Raven Sentence repetition

objectivation

Picture story: coherence

Picture story questions

74 4 14 8 2 3 3

76 4 20 8 2 4 2

131 3 31 2 0 3 3

Average group

4.3 14.9 6.2 0.5 2.2 1.4

Examplebrothers’task

•  Exp:CanyourememberwhatItoldyouaboutmybrothers?

•  Zina:Youtoldmeyouhavethreebrothersandonesister,andtheplaceyoumen6onedIdonnotknow/Icannotremember.[..]

•  Exp:AllthreeofmybrothersliveinRoCerdam.Janisoneofmybrothers.InwhichcitydoesJanlive?

•  Zina:Idon’tknow.

Boxplotssyllogisms,correctpremise-basedanswers Predic6ngsyllogis6creasoning

Model

Standardized Coefficients

t Sig. Beta 1 (Constant) -1,739 ,093

Raven -,136 -,913 ,369 Print awareness ,052 ,277 ,784 Classification -,007 -,052 ,959 Metalinguistic ,826 4,614 ,000

2 (Constant) -2,679 ,012 Raven -,135 -,929 ,360 Print awareness ,049 ,280 ,781 Metalinguistic ,825 4,701 ,000

3 (Constant) -2,769 ,009 Raven -,124 -,900 ,375 Metalinguistic ,855 6,207 ,000

4 (Constant) -3,210 ,003 Metalinguistic ,781 7,077 ,000