The Effects of Product Approach on Language Preparatory School Students` Writing Score in an Academic Writing Course
Bibliographic record
Abstract
A wealth of research has evaluated the effects of varied types of approaches in Academic Writing courses.However, there has been an ongoing discussion on prioritizing one of them.In this regard, this study examined the effects of product-based approach on Academic Writing Score of language preparatory school students over 16 weeks using pre-test and post-test.Considering this aim, 50 students were split into two groups as control or experimental group through systematic sampling method.Experimental group students were exposed to product approach, whereas control group students were engaged in process approach.Each student wrote about 1 topic biweekly amounting to 8 topics during the study.Quantitative data were analyzed by IBM SPSS 23 through independent sample t test and paired sample t test.Independent sample t test and paired sample t test post test results were recorded as .000respectively, indicating that there were highly significant differences in experimental group.On the other hand, control group students` progress was not significant enough.Likewise, the questionnaire and interview analysis as a part of qualitative data show that product approach was supported by more students when compared to process-based approach.Findings of this study may have some insightful points for educators who have been covering or managing Academic Writing courses at universities around the world.
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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 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".