Instructors’ Perceptions of English for Academic Purposes Textbooks at University Level
Bibliographic record
Abstract
The present paper aims to investigate EFL instructors’ perceptions of Cambridge English Unlimited (CEU) textbooks taught at Taif University English Language Center (TUELC) in the Academic year 2017-2018. To achieve this purpose, the researcher attempted to answer three questions. The first investigates instructors’ perceptions of the textbooks. The second question aims to find out the features that add to the strengths of the textbooks. The third question is an attempt to reveal the shortcomings of the textbooks from the instructors' perspectives and their suggestions to overcome these drawbacks. A questionnaire of 4- Likert scale was used to gather data from ninety two instructors to answer the first two questions, and content analysis was used to answer the third question. The collected data were analyzed in the form of descriptive statistics, using means, standard deviation and percentages. The results showed that instructors have a very positive attitude towards the textbooks in terms of the criteria and features investigated in the first two sections of the study tool. These answer the first two questions. However, they had certain concerns and suggestions in aspects other than those included in the study tool. These have been summarized according to their frequency of occurrence in the instructors' responses. Based on the results, the researcher drew a number of conclusions and recommendations.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".