Entrepreneurship Skills Needed by Nigerian Tertiary Institution Students and Graduates for Global Relevance
Bibliographic record
Abstract
The study examined entrepreneurship skills needed by students/graduates for global relevance. The survey research design was chosen for the study. The population of the study comprises Business Education lecturers and students from three polytechnics, four Colleges of Education and Delta State University, Abraka, all in Delta State. A sample of 110 was randomly selected comprising 53 lecturers and 57 students. Data collection was via the questionnaire which was validated by three Business Education lectures from Delta State Polytechnic Ozoro, College of Education, Agbor and Delta State University, Abraka, all in Delta State, Nigeria. In analyzing data, mean and standard deviation were used for research questions, while independent samples t-test was used to test hypotheses at 0.05 level of significance. The findings revealed that entrepreneurship skills needed by students/graduates of tertiary institutions include trade show planning, promotion and execution. Others include skills for processing inventories, gross and net profit as well as keeping debt ledgers. The findings also specified ICT skills needed by students/graduates to include skills for accessing contra vision electronic software, deleting and merging mails, keying in data, copying, pasting and inserting in appropriate locations. Also revealed in the findings are significant difference between entrepreneurship and ICT skills essential for students and those essential for graduates. On the basis of these findings, it was recommended that some of these skills should be integrated into the tertiary education curriculum so that undergraduate students could be exposed to them as early as possible.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".