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Showcasing the potential of error-annotated learner corpora for profiling research

Showcasing the potential of error-annotated learner corpora for profiling research. Jennifer Thewissen Centre for English Corpus Linguistics (CECL). Profiling research. Definition

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Showcasing the potential of error-annotated learner corpora for profiling research

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  1. Showcasing the potential of error-annotated learner corpora for profiling research Jennifer Thewissen Centre for English Corpus Linguistics (CECL)

  2. Profiling research • Definition • Finding ‘criterial features’ that discriminate between different levels of proficiency (e.g. Hawkins & Buttery, 2010) • CEF levels • C2 • C1 • B2 • B1 • A2 • A1

  3. Feature we focussed on • Construct of accuracy, viz. errors • Focus on four proficiency levels, viz. B1, B2, C1, C2 • Aim = See whether errors constituted a «criterial feature» to distinguish these levels

  4. Data & methodology

  5. International Corpus of Learner English (Granger et al., 2009)

  6. Threefold analysis • Error annotation, i.e. errortagging phase • CEF rating phase • Errorcounting phase

  7. Error annotation

  8. Error tagging examples The fastspread of televisioncantransformitinto a double-edged(FS)wheapon$weapon$. I willtry to giveseveral(XNUC)proofs$proof$ of the truth of the sentence. • 46errorsubcategories • Result: a detailederror profile per text

  9. The CEF rating procedure • Individual rating of the 223 learner scripts according to the linguisticdescriptors in the Common European Framework of Reference for Languages (CEF) (Council of Europe, 2001) • B1, B2, C1 or C2 (with + and – increments) • 2 professionalraters (+ 1 rater in cases of widedisagreement) (r = 0.70)

  10. Tracking development CEF score Error profile Development: Progress? Stabilisation? Regression?

  11. Error counting: potential occasion analysis (GNN)

  12. Statistical analyses: ANOVA & Ryan (GNN) GNN = [B1/B2]>[B2/C1]>[C1/C2]

  13. Results for profilingresearch

  14. 4 main error developmental patterns

  15. Two dominating error patterns

  16. Where do progress and stabilisation mainly occur? Discriminating power of errors

  17. Preliminary observations for profilingresearch

  18. Some concluding remarks • Errors (negative features) • Stronger discriminatory power between certain levels (viz. B1 vs. B2) than others (viz. B2 vs. C1 vs. C2) • Need to capture other features than errors (e.g. positive features) • Conclusion for profiling research: errors are useful but they are not enough in and of themselves

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