Higher Education and Its Contribution to Economies of African Countries: Move Towards Competence-Based and Skills Demand-Driven Standards in Collaboration with Industry
Bibliographic record
Abstract
This study explores the ecosystemic impacts of higher education (HE) on the economies of African countries, emphasizing the need for competence-based, and skills-demand-driven standards in collaboration with industry. HE is vital for equipping individuals with essential knowledge and skills for socio-economic transformation. However, in Africa, this role has weakened, with industry assuming a leading position. Curricula in HE institutions are slow to adapt to the skills needed by industries, leading to a range of challenges such as outdated curriculum delivery, desertion of technical and vocational training, inadequate research resources, insufficient collaboration frameworks between HE and industries, minimal support for entrepreneurship, and poor infrastructure. Aligning HE curricula with industry skills requirements is crucial for enhancing African economic development and competitiveness. Unfortunately, there is a notable lack of partnerships and practical mechanisms for curriculum integration among African HE institutions, which results in graduates possessing skills that do not meet industry demands. This paper reviews the extensive literature on HE's role in African economies, advocating for in-depth collaboration between HE and industry in order to tackle skills mismatches. Accordingly, establishing a healthy partnership between HE institutions and industries could facilitate work-integrated learning, encourage industry-led curriculum development, and prepare graduates with applicable skills and relevant knowledge for the job market. Thus, developing a proactive framework that can facilitate and enforce collaboration between higher education and industries could be critical in addressing the challenges faced by African economic development.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".