The Impact of Metacognitive Strategies on Jordanian EFL Learners’ Writing Performance
Bibliographic record
Abstract
One of the most challenging aspects of foreign language learning is writing. Writing is the most demanding and complicated aspect of language system. Writing requires the collective effort of orthographic, graphomotor and other linguistic skills with the inclusion of semantics, syntax, spelling, and writing conventions without being restricted to the aforementioned skills. The Improvement of cognitive psychology, metacognition has drawn the focus of an increasing number of researchers’ and paved way for recent dimensions on EFL writing, particularly in the aspect of writing achievement. Due to the fact that the method possesses a highly-placed executive aptness which comprises of formulation, supervision, and assessment, this study attempts to investigate the influence of using metacognitive strategies on Jordanian EFL learners' writing performance. Forty four students were randomly selected from secondary school level to partake in experimental control of the study. The researcher made use of the intervention program based on CALLA model of teaching in classroom. The experimental group (EG) received metacognitive strategies-based writing instruction whereas the control group (CG) received only the routine writing instruction (Product Approach). After five weeks of instruction, both groups were post-tested and at the end of program which lasted for twelve weeks, the students carried out another post-test. Data were submitted to the independent Mann-Whitney U test followed by Wilcoxon Signed-Rank test analysis. The results showed that there was a positive effect in the experimental group's writing performance. The findings of this study have implications for pedagogy as well as for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".