Lecturers’ Perspectives on Fostering Future Skills among Omani EFL Learners
Bibliographic record
Abstract
Developing future skills such as critical thinking, problem-solving, collaboration, and digital literacy is essential for Omani EFL (English as a Foreign Language) students to meet contemporary educational and workforce demands. This qualitative study explored the perspectives of 18 lecturers from the English Language Unit at the University of Technology and Applied Sciences (UTAS) in Salalah, Oman, focusing on their roles in fostering these skills among EFL students. Additionally, the study identified the challenges lecturers encounter and the strategies they employ to overcome these obstacles. Through thematic analysis of semi-structured interviews, the findings indicated that lecturers significantly contribute to skill development by motivating students, updating teaching methodologies, and integrating modern technologies into their instruction. Nonetheless, lecturers face considerable challenges, including students’ limited English proficiency, time constraints within the curriculum, and a lack of student motivation. To address these issues, the study suggested strategies such as enhancing student motivation, increasing student participation in the learning process, and utilizing innovative teaching methods with new technologies. Implementing these strategies is recommended to effectively develop future skills among Omani EFL students.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".