Investigating EFL Students’ and Instructors’ Perceptions of Dictionary Usage in Writing Assessment
Bibliographic record
Abstract
In this mixed-methods study we investigated the attitudes of 32 students and 34 teachers from a Saudi Arabian university toward dictionary use in writing assessments in EFL settings. We aimed to discern their views on the role of dictionaries in writing assessments and overall language proficiency. We asked both students and instructors to answer a 4-point Likert-style questionnaire to investigate their perceptions about using dictionaries in writing assessments. We interviewed participants later for further investigation. After analyzing the data both quantitatively and qualitatively, we found that students generally perceived dictionary use as being beneficial by enhancing vocabulary acquisition, improving writing performance and accuracy, and fostering positive attitudes toward writing without significantly affecting comprehension or focus on content. In contrast, teachers were skeptical, doubting dictionaries’ contribution to vocabulary development, writing quality, and accuracy. They also raised concerns about dictionaries’ not promoting positive writing attitudes or independent learning and potentially slowing down the writing process due to ineffective usage. Our study highlights a notable discrepancy between students’ positive perceptions and teachers’ reservations about dictionary use in language assessments. It suggests a need for further research to understand dictionaries’ impact on language learning and assessment outcomes, acknowledging the limitations of the study and the need for broader exploration in diverse educational contexts to resolve these differing views.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".