Evaluation of Modular EFL Educational Program (Audio-Visual Materials Translation & Translation of Deeds & Documents)
Bibliographic record
Abstract
Modular EFL Educational Program has managed to offer specialized language education in two specific fields: Audio-visual Materials Translation and Translation of Deeds and Documents. However, no explicit empirical studies can be traced on both internal and external validity measures as well as the extent of compatibility of both courses with the standards and criteria of scientific educational program. In a bid to address these issues, this study was conducted to evaluate the program from five fundamental criteria including: Admission Requirements, Program Content, Program Resources, Program Instruction/Evaluation Methods, and Graduation/Employment Requirements. Methodologically, the study is based on the requirements of both qualitative and quantitative research paradigms. To this end, a sample of teachers enjoying at least five years of offering both courses attempted a 22-item Likert-scaled questionnaire accommodating subcategories of the five macro criteria followed by open-ended written protocol commenting spaces for qualitative data. The findings revealed controversies over the all the macro-criteria and compatibility of the program with these well-established standards; suggesting exercise of comprehensive revisits and modifications in all aspects of the program as a whole.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".