The Perceptions of Faculty Members and EFL Learners to Proficient Instructors: A Philosophical Approach
Bibliographic record
Abstract
Adopting a philosophical approach to the concepts of knowledge and instruction, this study aimed to explore the perceptions of faculty members and EFL learners towards the efficiency of EFL instructors at the University level of Sudan University of Science and Technology, College of Languages. Two tools were used to collect data. A direct interview was administered to both learners and faculty members of the English Department at the College of Languages in which faculties and learners were asked about their perceptions of the good EFL instructor. A 30-point questionnaire was distributed to both learners and instructors. The instructors and learners were requested to assess the points from the items of opinion including the valuable instructor merits. The findings revealed that the concepts of instruction varied immensely over the past two decades. Also, the results showed that the EFL learners and the teachers are different regarding the arrangement and exhibition of learning resources. The instructors prepared the tutorial plans in a way that grabs learners' attention and that satisfies the learners' needs to achieve gauges in a useful EFL tutor. To sum up, it might be said that skilled Sudanese EFL instructors are in high demand since they can motivate and push their students to reach their best potential to maximize their language learning prospects.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".