Assessing chemistry teachers’ entrepreneurial competencies for developing small chemical-based businesses in Rwanda
Bibliographic record
Abstract
Unemployment among science graduates in Rwanda remains a pressing challenge due to limited industrial opportunities and insufficient market demand for scientific skills. This study assesses chemistry teachers’ entrepreneurial competencies for developing small chemical-based businesses. This study adopted an explanatory sequential design, combining quantitative and qualitative methods. A structured questionnaire was administered to 75 chemistry teachers from secondary schools across Rwanda. Quantitative data were analyzed using descriptive and inferential statistics. Descriptive statistics were used to summarize competency levels, while Multivariate Analysis of Variance was employed to analyze the influence of gender, teaching experience, school ownership, and location on five key entrepreneurial domains: entrepreneurship skills, innovation, teaching and mentoring, communication and collaboration, and ethics and professionalism. The quantitative results revealed that teachers exhibit moderate strengths in communication and collaborative skills (44%). However, financial planning and market research emerged as the weakest areas, with over 60% of teachers uncertain about budgeting and identifying market opportunities. In addition, 47% of teachers lack confidence in adhering to business regulations. Furthermore, most teachers (61%) lack problem solving and innovation skills related to creating chemical-based business. The inferential analysis showed significant differences in entrepreneurial competencies based on gender ( p < 0.05) in favor of male teachers and teaching experience in favor experienced teachers above 6 years of working experience ( p < 0.05). On the other side, the school location and ownership did not show significant effects. ( p >0.05) These findings suggest that experience and gender-related factors influence how chemistry teachers develop and apply entrepreneurial knowledge in educational settings. Therefore, the study highlights the need for targeted professional development and curriculum integration to promote chemical entrepreneurship among chemistry teachers. In this context, project-based learning, mentorship, and stronger policy support are recommended to foster entrepreneurial mindsets among teachers and students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".