Effective Use of Metacognitive Strategies of Students in ESL Writing Based on Gender
Bibliographic record
Abstract
There is a clear association between gender differences in using metacognitive strategies in English as a second language (ESL) writing and poor writing achievement, and one of the factors that contributes to this correlation is the sense of how male or female students use their brain hemisphere when presented with the writing task. Students who are studying ESL view the ESL writing task as difficult because they find it significantly more difficult to use metacognitive writing strategies in their writing tasks due to their poor proficiency in ESL and failure to use appropriate metacognitive writing strategies, which makes it difficult for them to write a coherent and cohesive essay. This study's primary purpose is to investigate whether students’ gender significantly affects their metacognitive writing strategies before, during, and after writing. This study employed a descriptive research design using quantitative data. 480 Form Four students from 12 secondary schools in Melaka, Negeri Sembilan, and Selangor made up the study's sample. The sample was chosen via purposive sampling. In order to gather data, the questionnaire served as the preferred method. The data from the questionnaire were analyzed using SPSS for Windows version 25. In this study, a one-way ANOVA was employed to detect whether there was a discernible gender gap in the use of metacognitive writing strategies before, during, and after the writing process by ESL male and female students. This study showed some interesting findings. First, the findings showed that in the use of overall metacognitive writing strategies, female students were much higher than their counterparts in ESL writing in terms of taking a position on the topic. Second, and this difference is crucial, female students also employed more metacognitive writing strategies before, during, and after writing. Therefore, it is suggested that metacognitive writing strategies be employed as an effective pedagogical tool to improve students’ writing performance and thus, education quality, in future ESL classes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".