Preparing the future workforce for 2030: the role of higher education institutions
Bibliographic record
Abstract
The invasion of futuristic technologies has made it inevitable for the future workforce to confront this reality and be ready to work in the new world of work in 2030. Higher education institutions (HEIs) are obligated to assume a pivotal role in equipping students with the requisite competencies. COVID-19 has expedited the development process compelling HEIs to make a mega shift to prepare the future workforce. Primary data was collected from HEI students across 11 countries to analyse their confidence to work in the new world of work in 2030 and to understand the role of HEIs in influencing students’ confidence. Exploratory Factor Analysis (EFA) and Partial Least squares (PLS) based structural equation modeling (SEM) procedures were employed to estimate a structural model of awareness and readiness (cognitive) and confidence (affective) factors, and the combined effect of HEI, awareness, and readiness on overall student confidence in their knowledge, skills and abilities. The statistical results indicate that there are strong significant relations between the HEI-Awareness; HEI-Awareness-Confidence; HEI-Readiness; and HEI-Readiness-Confidence and these dependencies are not just by chance. The results of this research are significant for higher education policy developers and curriculum developers to incorporate future competencies in the program. Further, educators and researchers will benefit from the results to develop teaching strategies and content to equip the future workforce for 2030.et.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".