The Impact of Gender and Academic Degrees on the Performance of Transversal Competencies in Higher Education Students
Bibliographic record
Abstract
There is some consensus among academics and employers that transversal competencies are one of the key aspects in training people to adapt to the demands of today's world. Universities make a great effort in the design of training programs, in preparing their teachers in training methodologies and in the evaluation systems to guarantee that their graduates acquire an adequate level of these skills. However, there are few studies that address the impact of gender and academic degrees on the performance of transversal competencies.This study aims to assess whether gender and degree have any impact on the level of transversal competencies obtained at the end of their higher education studies. To this end, we have evaluated 1,614 final year students from 11 higher education centers using a standardized questionnaire on the competencies of Communication, Leadership, Teamwork, Adaptation to change, Initiative, Problem solving, Decision-making, Planning and Organization. We have carried out a Multivariate Variance Analysis to analyze the effect of gender, degree and the interaction between both factors on the students' competence profile. The results show that men perform better in Leadership, Initiative and Decision-making, whereas women score better in Planning and Teamwork skills. Students of Social Sciences degrees have a poorer performance in the competencies than students of Health Sciences and Technical Education. In Planning, women perform better, regardless of the degree, compared to men.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".