A Review of Spelling Errors in Arabic and Non-Arabic Contexts
Bibliographic record
Abstract
The purpose of this review paper is to identify the core spelling errors in Arabic and Non Arabic Contexts. The most common difficulty that Arab learners may face during their English writing is correct spelling, for many different reasons such as the interference between English and Arabic language and the irregularity of the English language system. Several studies have been undertaken to evaluate writing mistakes and spelling errors in English, and most of them have classified spelling errors into three different categories: morphemic errors, where the errors occur in the morphemes parts (prefixes and suffixes); Intra-Morphemic errors, where errors occur in the word roots themselves such as deleting the final (silent) e vowel in the word write, and splits types, where the learners leave a space inside the word for example, write my self as two words instead of myself, one word. Apart from the three categories mentioned above, other studies claim that there are eight different types of error related to the abilities of the students and the nature of the error, and these include inversion, omission, substitution, segmentation, insertion, pronunciation, miscellaneous, and unclassified errors. In this review paper, we have found interlingual and intralingual –related errors where interlingual errors are mainly caused by the interference of the primary or mother language, while, intralingual errors are due to the system and instruction of the target language. Finally, suggestions are given based on previous research about how to review the spelling errors in Arabic and Non Arabic contexts to identify the error and also overcome the problem through alternatives that can be implemented to create a positive impact and can be furthermore used for all types of positive learning.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".