Improving Language Preparatory School Students` Writing Skills through Process Approach
Bibliographic record
Abstract
An increasing number of studies has been conducted about the approaches to improve learners` writing in an Academic Writing course.However, no approach stood out as the most efficient one.In this respect, process based approach and product based approach were compared in this study.The study was conducted on language preparatory school students at a prestigious private university in Erbil over 17 weeks.Simple random sampling method was employed to choose the sample from the population.Experimental and control group students were equal as 20.Experimental group students took instructions based on the steps of process approach, whereas control group students followed the steps of product approach.Each student was required to write about 8 topics regardless of being in control or experimental group.The students took two exams as pretest and posttest to make comparisons through SPSS 23.Independent samples t test p value was measured as .004which was significant.Also, paired samples t test p value was .000which was also highly significant in experimental group.These results reveal that the students who followed a process based approach instruction outperformed the students who got a product approach based instruction.Similarly, the questionnaire and interview analysis as a part of qualitative data uncovered that the students` satisfaction rate was higher once they followed a process based approach writing instruction.Findings of this study suggest that process approach can be integrated into Academic Writing curriculums without having any hesitation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".