Examining the Integration of 21st Century Skills in EFL Instruction: A Case Study of Selected Saudi Universities
Bibliographic record
Abstract
In an era characterized by rapid technological advancements and societal transformations, the importance of 21st-century skills in education cannot be overstated. Teachers play a pivotal role not only in imparting knowledge within the confines of the classroom but also in shaping the lives of individuals within their communities. This study investigates the acquisition of 21st-century skills among English teachers in Saudi Arabian universities, recognizing the significance of these skills in preparing educators for the evolving demands of modern society. A total of 150 respondents from five universities across Saudi Arabia participated in the survey, providing insights into their proficiency in various 21st-century skills. The results reveal that participants demonstrate that they have acquired a comparatively high level in social skills, leadership, communication, and aspects of creativity. However, skills such as digital literacy, collaboration, and critical thinking exhibit only a moderate level of acquisition among participants. Despite the overall positive indication of 21st-century skill adoption in English as a Foreign Language (EFL) instruction, there remains room for improvement. The findings suggest that while Saudi Arabian English teachers have begun integrating 21st-century skills into their teaching practices, further efforts are required to fully harness the potential of these skills. Addressing this gap may necessitate additional training opportunities and the development of English language curricula that explicitly incorporate and prioritize 21st-century skill development.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".