ESP Textbook Evaluation: The Case of Kuwaiti Students of Business Administration
Bibliographic record
Abstract
Textbooks are considered an essential component of classroom materials. Teachers usually examine a wide range of textbooks offered in the book market to determine an appropriate one that fits the pedagogical demands. Textbooks’ evaluation is, therefore, important to determine and improve their suitability for students’ needs. Specifically, in teaching English for Specific Purposes (ESP), textbooks play a significant role in enhancing students’ learning of specialized English skills and using such skills to address occupational needs. This study was undertaken to evaluate the quality of the Business Result Second Edition (2017) textbook and its suitability for the needs of students majoring in Business Administration (BA) at the College of Business Studies (CBS) in Kuwait. An evaluation checklist was utilized to examine five factors (subject matter, linguistic issues, exercises and activities, course objectives, and layout of materials and visuals). Participants included 10 English language teachers who taught the ESP course at CBS. Results indicated that despite having some disadvantages, the textbook was relatively suitable for the course. The findings of this study can be adopted by the curricula designers at CBS to improve or modify the textbook in question.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".