Insight into Gender Effect on EFL Writing Strategies in the Narrative and Expository Genres: A Case Study of Multilingual College Students in Morocco
Bibliographic record
Abstract
Research on gender and writing strategies in English as a foreign language (EFL) is scarce. This study investigates whether Moroccan male and female undergraduates use similar or different writing strategies when composing essays in the narrative and expository genres. Using think-aloud as a main research tool, a questionnaire, and retrospective interviews, the researcher collected data pertaining to male and female students’ strategy use and cognitive processes while writing in EFL. The analysis of 64 think-aloud protocols revealed Moroccan undergraduates’ use of a variety of writing strategies in terms of type and frequency. Both main types and subtypes of writing strategies emerged. Two-way Analysis of Variance (ANOVA) revealed that each gender group used some writing strategies more frequently than the other group; however, this difference in frequency of use was not statistically significant. In addition, the interaction of gender, writing strategy use, and discourse type yielded a significant difference in the use of the strategy of codeswitching only (i.e., language switch). On the other hand, the qualitative analysis of the protocols and interviews revealed a large variation between males and females in the use of the twelve strategies under investigation, together with overall writing behaviors. These strategies shall be presented together with recommendations for teaching composition in the EFL classroom.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".