Developing Content-Based Criteria for EFL Textbooks: The Case of Iranian Junior & Senior High School Levels
Bibliographic record
Abstract
Images are part of the content of the English textbooks and since junior high school curriculum is currently being underdevelopment, developing criteria for the images of the content of high school textbooks needs attentive consideration. The images need to be chosen according to the needs of students and those objectives found at the higher level documents. This research is conducted based on mixed approach in which students’ need is surveyed and data gathered by sifting through the higher level documents. Also, exploring the goals and objectives of the higher level documents, the criteria are obtained and determined by which the content was developed. In addition, the Delphi method is applied to measure the validity of the developed content. The study population at this research consisted of all students in the seventh grade (the first grade of high school), the third grade Secondary School and the first grade high school in five provinces of Iran, including Tehran, Semnan, Kurdistan, Khuzestan and East Azerbaijan counting 394 boys and 396 girls who completed the questionnaire. Also, 10 accessible experts and practitioners in English curriculum participated in developing and validating the criteria. One of the findings of this research indicates that 321 students interested in real images at the first rank and 306 other students fascinated with colored ones, at the second rank respectively.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".