Bibliographic record
Abstract
While integrating visual features can be among the most important characteristics of English language textbooks, reviewing the current locally-produced English for Specific Purposes (ESP) ones reveals that they lack such a feature. Enjoying a rich theoretical background including Paivio’s dual coding theory as well as Sert’s educational semiotics, this research was done to investigate the probable effectiveness of using pictorial context in ESP reading comprehension ability of Iranian university students whose syllabus mostly focuses on this skill. To do so, this study was conducted on two groups of Iranian students majoring physics. Before the treatment, pretest was performed in both groups. The students in the experimental group were taught through passages furnished with different kinds of pictures while the ones in the control group were taught through the same passages without the pictorial context. At the end of the treatment which took twenty two sessions of two hours during twelve weeks, the posttest was administered to both groups. At the end, drawing on t-test at the significance level of 0.05, the students’ performance was compared. The results revealed that there was a significant difference between the mean score of the two groups. Thus, it was concluded that using pictorial context improves the ESP reading comprehension of students.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".