Investigating EFL Students’ Perceptions about the Use of Beyond Textbook Materials for University Courses
Bibliographic record
Abstract
Proficiency in English, in today’s globalized atmosphere, needs to be prioritized. This can be achieved by incorporating diverse, dynamic, and productive strategies intended to improve language proficiency. The use of different beyond textbook materials for teaching skills courses such as reading, writing, listening and speaking can be one such strategy. This study advocates that student teacher’s reliance should not always be based on prescribed textbooks, which may not always satisfy all students’ needs in different circumstances. Instead, resources other than textbooks can be a supportive supplement. This quantitative study investigates EFL students’ perceptions about the use of beyond textbook materials while studying skills courses in Prince Sattam bin Abdulaziz University (PSAU). The sample consisted of 68 student-participants: 30 males and 38 females studying in level three of 4-year BA program in the Department of English. Based on purposive sampling, only those student-participants were chosen who had already studied skills courses as freshmen. To collect data, a 20-item questionnaire was created, online, using Google Forms. The responses were collected using 5-point Likert scale containing agreement, frequency and quality response options. The findings reveal that students positively reacted to the use of beyond textbook materials for skills courses and acknowledged the contributions of such non-textbook resources towards learning EFL. This study is limited to a selected context. Therefore, it is recommended that future studies may focus on student-participants from different locations to, supposedly, achieve results that are more comprehensive. It is hoped, findings of this study will provide valuable insights for students, teachers, and future researchers alike in the context of EFL.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".