21st Century Skills for Higher Education Students in EU Countries: Perception of Academicians and HR Managers
Bibliographic record
Abstract
The main objective of the study is to analyze the EU labor market needs and expectations in 21st Century skills in five countries from the point of view of academicians and HR managers. The meta-analysis research method was used to analyze the current reports of Turkey, the Czech Republic, Italy, Bulgaria, and Spain. The research results and findings of each country report have been comparatively analyzed. The research sample consists of five national reports. All views obtained from 28 human resources managers and 14 academicians were examined. According to research results, HR managers have more practical and pragmatist expectations from graduates such as business intelligence, knowledge of foreign languages, and continuous learning. Academicians emphasize graduates’ data mining ability, which refers to critical thinking. While academicians give high priority to communication and problem-solving, HR managers prioritize collaboration/team working skills. Agility skills defined as the ability to adapt to the changing conditions, are put in the second place by HR managers. According to academicians and HR managers, the most important 21st Century skills, in five countries, are communication, collaboration, and self-direction. There exists a need for innovative teaching materials to teach aforementioned skills to higher education 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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".