Perspectives of Nigerian Graduates on Curriculum Reengineering, Acquisition of Emerging Technologies, and Job Creation: A Descriptive Study
Bibliographic record
Abstract
Curriculum reengineering, web-based technology acquisition, and job creation among Nigerian tertiary institution graduates were all examined in this research. The researchers used a descriptive survey design following the quantitative research approach. The study included all Nigerian graduates eligible for national service or its exemption who earned their diplomas or degrees between 2016 and 2021. The data was gathered via an online survey titled "Curriculum Re-engineering, Acquisition of Emerging Technologies, and Job Creation Questionnaire (CRAETJCQ). To assemble the data for this study, we used a snowball method. There were 4,874 replies countrywide after four months of data collection; however, only 4,628 responses satisfied the data analysis conditions after screening out irrelevant responses. Results indicated low curriculum reengineering in Nigerian postsecondary institutions. Nigerian graduates had a poor adoption of new web-based technologies but are increasingly using them for word processing, graphics, data science and data analysis. Nevertheless, only a few graduates employed emerging techs for other purposes (such as printing, YouTube video creation, course design and development, software development, digital marketing, online advertising, and consumer outreach). Although 58.30 per cent of graduates reported having not created any job, 41.70 per cent have done so between 2016 and 2021. Of the 1,930 graduates who owned at least one small or medium enterprise, 58.96% had no employees, whereas 41.04% had hired at least one employee between 2016 and 2021. The graduates' job creation index was estimated to be approximately 50% using a new formula. Based on these results, conclusions and recommendations were made.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".