EFL Teachers’ Attitudes toward Commercial Textbooks in EFL Programs
Bibliographic record
Abstract
The perception of the English as Foreign Language (EFL) teachers in Kingdom of Saudi Arabia (KSA) is extremely crucial since their perceived views regarding the commercial English learning textbooks plays a major role in framing their attitudes towards such textbooks. These textbooks are published by different international publishers and used extensively in EFL programs around the globe. Moreover, attitudes do influence language learning. This paper aims to investigate teachers’ attitudes toward the commercial textbooks used in (EFL) programs. In this quantitative research, forty-three EFL instructors were surveyed through a Likert scale questionnaire. The results reveal, in general, the negative attitudes of the teacher towards commercial English textbooks since for them such textbooks are found insufficient in meeting the courses’ aims and objectives, students language proficiency level, their cultural sensitiveness, and their academic backgrounds. The study found teachers opinion vis-à-vis textbooks inappropriate content, a mismatch in learners’ needs and not in agreement with teaching methodologies. The paper offers a few recommendations to the EFL instructors as well as to the instructional designers to adapt and customize commercial textbooks in line with learners’ needs. It suggests teachers to use teaching material to suit the purpose, in addition to advising curriculum designers and content developers to take into account the specific needs of the students and the objectives of the course.
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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 | 0.012 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".