Determining the Effectiveness of the Process Genre Approach in Increasing and Decreasing Saudi EFL University Students’ Complexity, Accuracy, and Fluency in Reaction Essays
Bibliographic record
Abstract
This study investigates the level of effectiveness that the process genre approach has on increasing and decreasing Saudi advanced EFL undergraduates’ Complexity, Accuracy, and Fluency (CAF) in reaction writing. Sixteen level six participants were recruited from the College of Languages and Translation at Al-Imam Muhammad Ibn Saud Islamic University. All participants undertook a pre-test and a post-test on reaction writing. After collecting 32 essays, they were analyzed based on 55 indices of CAF, and then a Wilcoxon Signed-Rank Test was applied to compare each CAF construct/sub-construct measure’s mean in the pre-test with its mean in the post-test, and between the total mean of all measures for each CAF construct/sub-construct in the pre-test with their total means in the post-test. The findings showed that there were no significant total or partial impacts of the process genre approach on participants’ reaction essay syntactic complexity, lexical density, lexical sophistication, and fluency. However, the results indicated that there were only partial effects of the approach (across some measures) on participants’ reaction essay lexical variation and accuracy. Finally, the study yields several pedagogical implications and recommendations for EFL writing instructors, educators, and researchers.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".