EFL Arab Learners’ Misselection of English Prepositions: Analysis of Interlingual Errors in Voice Messages at Chat Groups
Bibliographic record
Abstract
This study examines interlingual prepositional errors that EFL Arab learners wrongly substitute (misuse) during their verbal interactions (on social media). It aims to investigate the most frequent prepositions that EFL Arabic learners misuse in their conversations. The focus was on selected prepositions: 'in', ‘from’, ‘to’, 'for', and ‘by’. The data was gathered by analysing audio recordings of participants' responses within a WhatsApp chat group. It showed that EFL Arab learners make incorrect substitutions with any collection of prepositions (typically two or three) that correspond to one equivalent preposition in their mother tongue. The participants frequently substitute prepositions ‘in, on, and at' as equivalents for Arabic preposition ‘fi’, ‘for’ and ‘to’ as equivalents for Arabic preposition ‘ila’, and ‘from and of' as equivalents for Arabic preposition 'min’. Therefore, the participants incorrectly employ 'in' instead of 'on and at', 'for' instead of 'to' and vice versa, and 'from' instead of 'of and since'. The collections of English prepositions that have only one Arabic counterpart are a cause of confusion, as evidenced by the incorrect substitutions that EFL Arab learners made. Based on findings, the area of the prepositions of multi-uses and semantically complex requires contextualized teaching strategies that contrastively present prepositions in bundles or phrases.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".