Developing Students’ 21st-century Skills: Are EFL Teachers in Cyprus Up for the Task?
Bibliographic record
Abstract
This study investigates the perceived readiness and competence of teachers of English as a Foreign Language in Cyprus to develop students' 21st-century skills within the framework of the Theory of Planned Behavior. These skills, e.g., critical thinking, creativity, communication, collaboration, and digital literacy, are essential for navigating the modern world. This study has shown that despite the fact that all teachers support the claim that language teachers are particularly well-suited to help students develop these skills, only 65% of the surveyed teachers claim to fully understand what these skills are. The study further reveals that teachers consider the 4Cs (critical thinking, creativity, communication, and collaboration) along with problem-solving, decision-making and autonomy of learning as the most important skills. They also feel confident about developing their students’ communication and collaboration skills but feel less confident about developing other skills. The set of skills they feel the least confident about is digital literacies. This is arguably concerning given the importance of these skills in today’s world and the fact that students today, the so-called digital natives, are not as technologically-savvy as previously thought. This study has also revealed that there is a positive correlation between the importance ascribed to each skill and teachers' perceived ability to teach it. This indicates that targeted training emphasizing the relevance and application of these skills can enhance teachers' confidence and competence. By equipping teachers with the necessary skills and knowledge, students can be better prepared for the challenges of the 21st century.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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 | 0.001 |
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".