Second Language Learners’ Performance and Strategies When Writing Direct and Translated Essays
Bibliographic record
Abstract
The purpose of this study was to investigate ESL students’ performance and strategies when writing direct and translated essays. The study also aimed at exploring students’ strategies when writing in L2 (English) and L1 (Arabic). The study used a mixture of quantitative and qualitative procedures for data collection and analysis. Adapted strategy questionnaires, writing essay prompts and follow-up questions were utilized for data gathering. Thirty six university students participated in writing three different essays (direct L2 essay, L1essay, and translated essay). Furthermore, the participants responded to strategy questionnaires and answered follow-up questions. The results revealed statistically significant differences between direct and translated writing in favor of the first one. No significant differences between direct and translated writing in the use of strategies were found. The study’s findings may have pedagogical implications for the fields of writing instruction, writing assessment and teacher training. Based on the results, the study ended with some recommendations to assist and direct future research.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".