Comparing the Effects of Four Instructional Treatments on EFL Students’ Achievement in Writing Classified Ads
Bibliographic record
Abstract
The current study set out to compare the effect of traditional and non-traditional instructional treatments; i.e. explicit, implicit, task-based and no-instruction approaches on students’ abilities to learn how to write classified ads. 72 junior students who have all taken a course in Reading Journalistic Texts at the Payame-Noor University streamed by performing a TOEFL proficiency test. The selected participants were randomly divided into the following four groups; an explicit group which received direct instruction; an implicit group which were instructed indirectly, and the self-study group with no-instruction treatment in comparison to the task-based group which were asked to prepare a classified ad. A pre-test and a post-test were administered before and after the treatment. The moves in classified ads pre- and post- tests were analyzed through descriptive and inferential statistics. The quantitative analysis of the post-tests revealed that the explicit and task-based groups outperformed the implicit and self-study instruction groups. The findings of this research offer English teachers the chance to reconsider their practices and performances through the advantages and disadvantages of the traditional and new techniques which were employed in the current research and combine them to help learners improve their reading and writing skills.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".