Perception Deception: Exploring the Gap between Self-Perception and Phonemic Perception among Arabic-speaking EFL Learners
Bibliographic record
Abstract
This study explores the relationship between perceived and actual phonemic perception abilities among Arabic-speaking English as a Foreign Language (EFL) learners. It investigates whether learners’ perceptions align with their actual performance and whether they tend to underestimate or overestimate their phonemic abilities. Fifty-eight participants, native speakers of Qassimi Arabic, rated the perceived difficulty of English vowels and completed a vowel perception task. Results reveal a significant discrepancy between perceived and actual abilities, with most participants underestimating their phonemic perception skills. A weak positive correlation between perception and performance suggests that learners’ self-assessments may not reliably reflect their actual abilities. Findings provide further empirical evidence of the Dunning-Kruger effect (Dunning, 2011) and extend such evidence to include Arab EFL learners’ perceived phonemic abilities. Theoretical, epistemological, and pedagogical implications of the study are discussed, including a call for less reliance on learners’ self-perceptions in L2 research and instruction in favor of objective performance measures.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".