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A Data driven Investigation of Corrective Feedback on Subject Omission Errors in First Language Acquisition

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Figure

Table 1: Overview of our dataset containing lon-gitudinal data from 25 different children.
Figure 1: Sample child-adult utterance pair with information layers automatically added during prepro-cessing, plus a Corrective Feedback layer manually annotated with the decision tree in Figure 2.
Figure 3: Amount of CF exchanges against child’sage. Pearson’sr r =–0.90 (Lara), r =–0.88 (Thomas), =–0.97 (Trevor), and r = +0.32 (Emily).
Figure 4: Development of child MLU, proportion of SOE and CF frequency (in standardised zscores)for Adam in the Brown corpus, when measured by month (left) and after smoothing (right).
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