Analysis of Spelling Errors of Beginner Learners of English in the English Foreign Language Context in Saudi Arabia
Bibliographic record
Abstract
This study reports the types of spelling errors made by the beginner learners of English in the EFL context as well as the major sources underpinning such errors in contextual writing composition tasks. Data were collected from written samples of 122 EFL students (male and female) enrolled in the intensive English language programme during the preparatory year at the University of Ha'il in Saudi Arabia. Students were given 1.5 hours to write on one of four different descriptive topics related to their life and culture. The spelling errors found in the writing samples was analysed and classified intofour categories of errors according to Cook’s Classification: omission, substitution, insertion, and transposition. An analysis of errors established that errors of omission constituted the highest proportion of errors. The majority of learners’ spelling errors were related to a wrong use of vowels and pronunciation. When uncertain about accurate spellings, beginner learners often associated a wide range of vowel and consonant combinations in an attempt to spell words accurately, sometimes even combining two distinct lexical items by overlapping vowels. The findings suggest that spelling errors are mainly the outcome of anomalies existing in the target language of the learners as well as L1 interference from their internalized Arabic language system.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".