Enhancing Graduate Employability: Inculcating Soft Skills into the Tertiary Institutions’ Curriculum
Bibliographic record
Abstract
The perennial issue of graduate employability remains topical in today’s turbulent labour market environment. Most graduates bear the brunt of unemployment especially in developing countries. Most qualifications offered by higher education institutions specifically focus on technical skills. With the unremitting demands of soft skills in the corporate world, there is a widespread concern and transcending need for the redesign of the curriculum to inculcate the soft skills. In dynamic environment, the tertiary institutions are required to produce highly competent graduates to bode well and meet the relentless demands of South African economy. An avalanche of diversification, globalisation, internationalisation of workplaces has a strong bearing on skill sets employees are expected to possess in South Africa. Against this backdrop, the study sought to investigate soft skills that can be inculcated into the South African’s Tertiary Institutions curriculum to improve graduate employability. Using systematic review method, a total of 85 peer reviewed articles were considered as final studies for review to achieve the primary objective of this paper. From the content analysis, the findings revealed several soft skills. The paper also gave directions on how soft skills can be embedded into the university curricula to prepare graduates for the world of work.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".