A Comparative Taxonomy of Errors Made by Iranian Undergraduate Learners of English
Bibliographic record
Abstract
This study tried to identify and investigate errors made by Persian learners of English according to comparative taxonomy which categorizes errors based on the source of errors such as interlingual, developmental, ambiguous and other errors. To conduct this study, 40 Persian learners of English were selected according to their Grade Point Average from Shiraz Azad University. Elicitation test was used for data collection. Writings of the students were analyzed and the errors were extracted and categorized based on comparative taxonomy. The results showed that the majority of the errors can be attributed to developmental, other, ambiguous and interlingual errors respectively. It proved that majority of errors were those which are common among native speakers of English and foreign leaners of English. Interlingual errors constitute the lowest number of errors. This finding rejected positive transfer from Persian learner’s mother tongue, Farsi. Key words: Language learning; Writing; Contrastive analysis; Error analysis; EFL; ESL Resume Cette etude a tente d’identifier et d’enqueter sur les erreurs commises par les apprenants de l’anglais persans selon la taxonomie comparative qui categorise les erreurs sur la base de la source des erreurs telles que des erreurs interlingues, developpement, ambigue et d’autres. Pour realiser cette etude, 40 apprenants de l’anglais persans ont ete selectionnes en fonction de leur moyenne ponderee cumulative de Shiraz Universite Azad. essai Elicitation a ete utilise pour la collecte des donnees. Ecrits des etudiants ont ete analysees et les erreurs ont ete extraites et classees en fonction de la taxonomie comparative. les resultats ont montre que la majorite des erreurs peuvent etre attribuees a developpement, d’autres, les erreurs ambigus et interlinguistique, respectivement. Il s’est avere que la majorite des erreurs sont ceux qui sont frequents chez les locuteurs natifs de inclines parmi anglais et etrangers de l’anglais. erreurs interlingues constituent le plus petit nombre d’erreurs. Cette constatation a rejete un transfert positif de la mere persane apprenant la langue, le persan. Mots-cles: Apprentissage des langues; L’ecriture, L’analyse contrastive; Analyse des erreurs; EFL; ESL
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".