The Use of Journal Writing and Reading Comprehension Texts During Pre-Writing in Developing EFL Students’ Academic Writing
Bibliographic record
Abstract
This study aimed at investigating the effects of journal writing and reading comprehension practice during pre-writing on the development of the writing of college students enrolled in English as a foreign language (EFL) programme. A factorial design was manipulated, where subjects (n = 42) were randomly assigned to control and experimental groups. Existing scores on the students’ first semester writing achievement test were used to determine the writing proficiency levels of the subjects. Data were collected through administering two writing tests, an one-hour test and a 15-minutes free writing test. The results of these tests were analyzed using t-test to assess the relationship between writing fluency, complexity and accuracy. Descriptive statistics (means and standard deviations) and two multivariate analyses of variances (MANOVA) were further run in order to address the questions raised in the study. Findings of the study showed that there was no significant difference between journal writing and reading comprehension practice in improving the writing fluency, complexity and accuracy of the students. The MANOVA test run to test the interaction between the treatment (journal writing and reading comprehension texts) and the writing proficiency levels (low vs. high) on the writing fluency, complexity and accuracy showed no significant results. Recommendations for future research were provided at the end of the study.
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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 | 0.015 |
| 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 | 0.001 |
| 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".