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Error-Annotated Learner Corpora for Profiling Research

Explore the potential of error-annotated learner corpora to identify proficiency levels based on error analysis, utilizing the Common European Framework of Reference for Languages. Statistical analyses reveal error developmental patterns and discriminatory power among different proficiency levels. Conclusive remarks highlight the necessity of capturing positive features in addition to errors for comprehensive profiling research.

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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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