Learning Complex Features: A Morphological Account of L2 Learnability

in Language Dynamics and Change
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Certain first languages (L1) seem to impede the acquisition of a specific L2 more than other L1s do. This study investigates to what extent different L1s have an impact on the proficiency levels attained in L2 Dutch (Dutch L2 learnability). Our hypothesis is that the varying effects across the L1s are explainable by morphological similarity patterns between the L1s and L2 Dutch. Correlational analyses on typologically defined morphological differences between 49 L1s and L2 Dutch show that L2 learnability co-varies systematically with similarities in morphological features. We investigate a set of 28 morphological features, looking both at individual features and the total set of features. We then divide the differences in features into a class of increasing and a class of decreasing morphological complexity. It turns out that observed Dutch L2 proficiency correlates more strongly with features based on increasing morphological complexity (r = -.67, p < .0001) than with features based on decreasing morphological complexity (r = -.45, p < .005). Degree of similarity matters (r = -.77, p < .0001), but increasing complexity seems to be the decisive property in establishing L2 learnability. Our findings may offer a better understanding of L2 learnability and of the different proficiency levels of L2 speakers. L2 learnability and L2 proficiency co-vary in terms of the morphological make-up of the mother tongue and the second language to be learned.

Learning Complex Features: A Morphological Account of L2 Learnability

in Language Dynamics and Change

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    Figure 1. Verbal person marking (100): neutral (violet and red) versus non-neutral alignment (yellow and orange). Verbal subject marking for person and number (feature 29): none (violet and orange) versus other than none (yellow and red) (Dryer and Haspelmath, 2011)

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    Figure 2. The distribution of adjusted proficiencies exhibits positive skew

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    Figure 3. The distribution of adjusted proficiency among 33 non-Indo-European languages

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    Figure 4. The distribution of adjusted proficiency among 39 Indo-European languages

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    Figure 5. The relationship between speaking proficiency scores (y-axis) and a weighted sum of features that are less complex in the L1 than in Dutch (x-axis)

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    Figure 6. Between-family variation as estimated by a random intercepts and slopes model (dotted lines) and a random-intercept model (solid lines). The label “Other” contains L1s from families with 1 or 2 languages available in our sample

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