Bibliographic record
Abstract
This study investigates the performance of a spell checker designed for native writers on misspellings made by second language (L2) learners.It addresses two research questions: 1) What is the correction rate of a generic spell checker for L2 misspellings?2) What factors influence the correction rate of a generic spell checker for L2 misspellings?To explore these questions, the study considers a corpus of 1,027 unique misspellings from 48 Anglophone learners of German and classifies these along three error taxonomies: linguistic competence (competence versus performance misspellings), linguistic subsystem (lexical, morphological or phonological misspellings), and target modification (single-edit misspellings (edit distance = one) versus multiple-edit misspellings (edit distance > 1)).The study then evaluates the performance of the Microsoft Word® spell checker on these misspellings.Results indicate that only 62% of the L2 misspellings are corrected and that the spell checker, independent of other factors, generally cannot correct multiple-edit misspellings although it is quite successful in correcting single-edit errors.In contrast to most misspellings by native writers, many L2 misspellings are multiple-edit errors and are thus not corrected by a spell checker designed for native writers.The study concludes with computational and pedagogical suggestions to enhance spell checking in CALL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".