The L2 Impact on the Acquisition of Dutch: The L2 Distance Effect - - PowerPoint PPT Presentation

The L2 Impact on the Acquisition of Dutch: The L2 Distance Effect

Job Schepens1, 2 Frans van der Slik1 Roeland van Hout1 ¹ Centre for Language Studies, Radboud University Nijmegen, the Netherlands ² International Max Planck Research School for Language Sciences, Nijmegen, the Netherlands j.schepens@let.ru.nl

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Linguistic differences

56% of citizizens in the EU member states are able to hold a converstaion in at least one language apart from their mother tongue (Euro Barometer 243, 2006) As compared to 47% 5 years earlier (Euro Barometer 55.1, 2001)

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Introduction

• TOPIC

The effect of language background on the acquisition of Dutch

• SITUATION

It is unclear how similarity affects acquisition of an additional language Quantifying similarity is not straightforward (e.g. McMahon & McMahon, 2005; WALS online, 2011)

• RESEARCH QUESTION

How does the mother tongue (L1) and additional language background (L2) influence the acquisition of Dutch?

• HYPOTHESES

Two separate L1 and L2 distance effects vs. one interactive effect of language background

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Empirical Measure of Proficiency in Dutch

• State exam “Dutch as a Second Language”
• Produced by CITO, a Dutch organization that produces tests and exams
• For non-native speakers who intend to start a higher level education / occupation
• 1995 – 2010
• 50,000 test scores available
• Enough data to test learning differences across 74 mother tongues
• Available data
• Individual differences: gender, age of arrival, length of residence, years of education
• Contextual differences: educational quality, language background

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Empirical Measure of Proficiency in Dutch

• The speaking exam
• 14 tasks in 30 minutes
• e.g. provide information, give instructions, and so on
• in Dutch television, a lot of ads are made for all kinds of products, even in the

middle of a program. What is your opinion about ads on TV?

• Evaluation on content and correctness
• Passing level
• 500 points ( ≈ upper-intermediate / B2 level)

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0.000 0.002 0.004 0.006 0.008 0.010 300 400 500 600 700

Speaking Density

Evolutionary Measure of Linguistic Differences

• Cognates contain “genetic” information
• tomaat (NL), tomato (EN), Tomat (DE)
• Cognate based account of evolutionary relatedness

(Gray & Atkinson, 2003) using expert judgements of genetic relatedness in 200 lexical item lists

• Phylogenetic tree
• Branch lengths are proportional to ML estimates of

evolutionary change per cognate

• Linguistic distance
• The sum of branch lengths joining one language to the
• ther (via the most recent common ancestor) represents

the amount of evolutionary change between two languages

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HITTITE Greek Armenian Albanian Kashmiri Singhalese Nepali List Bengali Hindi Panjabi Gujarati Marathi Persian List Afghan Lithuanian Latvian Slovenian Bulgarian Serbocroatian Slovak Ukrainian Byelorussian Russian Polish Breton Irish Romanian French Spanish Portuguese Catalan Italian German Dutch List Afrikaans Flemish English Swedish Icelandic Faroese Danish

Investigation 1: Influence of the Mother Tongue

• Fixed Effects
• Gender, age of arrival, length of residence,

years of daily education, additional language, educational quality, linguistic distance

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Mother Tongue Country

• f Birth

Mean Speaking Linguistic Distance Schooling Quality Group Size Kurdish Syria 487 .426 423 63 Kurdish Turkey 490 .426 454 185 French Congo 491 .398 350 65 French France 531 .398 497 936 French Switzerland 550 .398 517 37 German Germany 558 .037 510 4434 German Switzerland 571 .037 517 190

• Sample
• 35 Indo-European mother tongues
• 89 different countries
• 33,000+ learners
• Dependent variable
• speaking proficiency

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Methods

Learner 2 Farsi Iran Learner 1 Learner 3 Kurdish Afghanistan

• Linear Mixed Effects Regression
• to model dependencies in variation

by estimating group level adjustments to the intercept

• assumes adjustments are:
• normally distributed,
• centred around 0, and
• rthogonal to the individual

level noise

• With partially crossed random

effects

• We included languages with at

least 20 learners per country only

L1 C

Linear Mixed Effects Regression Results

• Distance explains a high percentage of between mother tongue variance
• Correlation of observed scores with predicted scores is higher

(r = .87, p < .0001) than correlation of observed scores with linguistic distance only (r = -.77, p < .0001)

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R2

Learner

R2

Country of Birth

R2

Mother Tongue

Model Fit Null Model Variance Components 977.51 (75.2%) 184.33 (14.2%) 137.28 (10.6%) 159,535.0 (LogLik) Model with 6 fixed effects Explained variance 4.2% 59.0% 75.1% 1516.6 (Chisq), 6 (Chi Df), p < 2.2e-16 ***

Linear Mixed Effects Regression Results

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Mixed Linear Regression measures of fit for all learners

Random Effect Structure Df logLik Chisq Chi Df Pr(> Chisq) Null model L1, C 4

• 247,546.18

L1, C 10

• 246,336.19

2,419.978 6 < 2.2e-16 *** L1L2, C 10

• 246,097.03

478.33 < 2.2e-16 *** L1,L2, C 11

• 246,003.81

186.43 1 < 2.2e-16 *** L1,L1L2, C 11

• 245,993.14

21.35 < 2.2e-16 *** L1,L2,L1L2, C 12

• 245,945.03

96.21 1 < 2.2e-16 ***

P values and HPD confidence intervals

Fixed Effects Estimate MCMCmean HPD95lower HPD95upper pMCMC Pr(> |t|) (Intercept) 505.0218 504.8874 498.4352 511.3588

• 1. Gender (1 = Female)

7.3931 7.4079 6.7446 8.0468

• 2. Age of Arrival
• 0.7248
• 0.7249
• 0.7661
• 0.6841
• 3. Length of Residence

0.6183 0.6181 0.5509 0.686

• 4. Years of Daily Education
• 0.7686
• 0.7817
• 1.8289

0.2446 0.1368 0.143

• 5. Secondary School Enrollment Rate

0.1785 0.1798 0.1121 0.2493

• 6. interaction 4*5

0.0365 0.0367 0.0242 0.0487

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Random Effects Name Std.Dev. MCMCmedian MCMCmean HPD95lower HPD95upper L1L2 (Intercept) 3.29 2.95 2.96 2.27 3.7 C (Intercept) 8.3 7.86 7.9 6.55 9.26 L1 (Intercept) 11.13 10.49 10.55 8.76 12.46 L2 (Intercept) 3.82 3.88 3.93 2.64 5.27 Residual 31.34 31.36 31.35 31.16 31.55

Pair Wise Comparisons

• Using the aggregated adjustments by L2

to incorporate interactional effects Kurdish English

• 1.77

Kurdish Arabic

• 7.92, p<.0001

Kurdish Monolingual

• 9.28, p<.0001

Kurdish Farsi

• 13.47, p<.0001

Kurdish Turkish

• 19.9, p<0.0001

Serbian German 10.21 Serbian English 2.89, p<.0001 Serbian French

• 1.14, p<.0001

Serbian Russian

• 4.62, p<.0001

Serbian Monolingual

• 7.89, p<.0001

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Hungarian German 19.64 Hungarian Romanian 18.79, p<.0001 Hungarian English 16.93, p<.0001 Hungarian Monolingual 4.73, p<.0001 Polish German 9.44 Polish English 5.06, p<.0001 Polish French 2.53, p<.0001 Polish Russian

• .85, p<.0001

Polish Monolingual -1.88, p<.0001 Polish Italian

• 3.59, p=0.008

German French 36.44 German English 34.12, p<.0001 German Italian 31.20, p<.0001 German Spanish 31.06, p=0.90 German Russian 27.67, p<.0001 German Monolingual 26.66, p=0.11

L1L2 L2

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Standard normal quantiles

• 20

20

• 2
• 1

1 2

(Intercept) Standard normal quantiles

• 10
• 5

5 10

• 2
• 1

1 2

(Intercept) Standard normal quantiles

• 20

20

• 2
• 1

1 2

(Intercept) Standard normal quantiles

• 10
• 5

5 10

• 2

2

(Intercept)

L1

Adjustments are Normally Distributed

C Quantile to quantile plots of random intercepts with HPD intervals L2 noise > L1 noise

• Gap at upper intermediate part of scale
• German and Swedish seem extremely useful

TOP 10 By-Mother Tongue Adjustments

Language L1 only L2 added Difference German 25.93 27.07 1.14 Swedish 24.97 24.24

• 0.73

Slovenian 21.67 19.97

• 1.70

Afrikaans 19.27 19.09

• 0.18

Danish 18.96 17.56

• 1.40

Norwegian 18.90 17.20

• 1.70

Estonian 16.53 14.69

• 1.84

Papiamentu 15.08 15.14 0.06 English 12.90 16.29 3.39 Byelorussian 12.84 11.55

• 1.29

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Pearson’s r = .994, n = 74

TOP 10 By-Best Additional Language Adjustments

Language L1 and L2

• nly

Interactions added Difference German 10.11 10.15 0.04 Swedish 4.49 4.39

• 0.10

English 3.33 2.32

• 1.01

Czech 2.52 2.40

• 0.12

Hindi 2.50 2.30

• 0.20

Norwegian 2.46 2.30

• 0.16

Hebrew 2.23 2.32 0.09 Slovak 1.94 2.00 0.06 Urdu 1.87 1.69

• 0.18

Pashto 1.74 1.03

• 0.71

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Pearson’s r = .984, n = 45

Conclusions

• We claim that linguistic distance is an essential concept in second

language acquisition

• Moreover, there may be a similar distance effect for one’s best additional

language

• The evidence indicates that an L2 distance effect operates side by side and interacts
• nly slightly with an L1 distance effect. This might have consequences for our

understanding of multilingual language processing.

• Mixed effects modeling enables researchers to analyze

interactions between different random effects

• Quantitative models of linguistic differences are useful for

researchers who want to model differences in language skills

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