Future Challenges and Opportunities in the Development of Soft Skills in Higher Education: Scenarios and Responses
Bibliographic record
Abstract
Given the current technological changes in the requirements of professional activity, the development of soft skills in students is an important task of modern higher education. Accordingly, the purpose of the study is to analyse the main difficulties in the development of social skills in higher education and to identify opportunities and scenarios for improving the integration of soft skills in higher education. This cross-sectional study collected data from 2 groups: teachers (40) and students (60). The inclusion of respondents was based on a stratified sample, which allowed us to take into account different groups of participants. Students of full-time higher education institutions and teachers with at least 1 year of experience were selected for the analysis. The students and teachers also had different specialities. All respondents had different experiences of involvement in modern educational initiatives and activities that contributed to the development of social skills. The main tool was a questionnaire consisting of closed questions and a Likert scale. The results showed that all participants in the educational process attach considerable importance to soft skills (4.3 points among students and 4 points among teachers). The main obstacles to further integration of soft skills into the higher education system are lack of motivation, imperfection of modern curricula, limited resources and resistance to change in the environment of teachers. The conclusions identify the main promising opportunities for improving the state of soft skills development. In particular, in the future, significant attention should be paid to participation in volunteer projects, individual consultations with specialists, and the case study method. For teachers, training and especially internships offer a significant opportunity to develop their social skills.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| 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".