PEDAGOGICAL STRATEGIES FOR ENTREPRENEURSHIP EDUCATION IN TERTIARY INSTITUTIONS IN DELTA STATE
Bibliographic record
Abstract
This study examined pedagogical strategies for entrepreneurship education in tertiary institutions in Delta State. Two research questions guided the study. Descriptive survey research was adopted for the study. The study population comprised 129 Business Education lecturers and Social Studies lecturers in tertiary institutions in Delta State. There was no sampling since the population in the zone was manageable. The instrument for data collection was a 21-item questionnaire. The data collected were analyzed using mean and standard deviation. The study established that pedagogical strategies for entrepreneurship education include utilizing school business premises. This will motivate students to learn how to improve customer patronage, as customers are essential for business survival. The study further established that a partnership with industry/business enables students to gain real-life experience in businesses, thereby enhancing their understanding during training. The study concluded that entrepreneurship education should be made practical to achieve the course's objective. The paper also recommends that educational institutions utilize business premises, especially those within the school environment, to make entrepreneurship education more practical. Additionally, it suggests that lecturers and other educational stakeholders collaborate with industries and business organizations to make entrepreneurship education more viable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".