Assessing the enterprising tendencies of Arab female undergraduate engineering students in the Sultanate of Oman
Bibliographic record
Abstract
This study assesses the enterprising characteristics of first-year undergraduate Omani female chemical engineering students in Muscat, Oman. Pre and post surveys were conducted with 27 respondents from an entrepreneurship boot camp module mandated by the Oman Ministry of Higher Education. The variables, measured on a 10-point Likert scale, included need for achievement, need for autonomy, creativity, risk-taking, and locus of control. Statistical analysis was performed on the integrated data to measure the impact of student learning using a t-test approach and comparing mean averages. This was followed by qualitative semi-structured interviews that were examined using thematic analysis. The comparison of students’ enterprise tendencies before and after the module indicates minor to moderate improvements in their entrepreneurial abilities and their understanding of entrepreneurial behavior. The most noticeable impact was on students’ risk-taking abilities, followed by their creativity, need for achievement, need for control, and, lastly, their need for autonomy. The findings illustrate that students perceived entrepreneurship positively but were concerned about the scheduling of the module and its integration into their core program of study. Students may have benefited further from a module of extended duration as opposed to the block delivery “boot camp” mode of learning. Due to the limited number of participants and the focus on female students only, the results of the study cannot be generalized. However, the article presents an initial exploration of and offers insights into enterprising characteristics among an empirically underexplored demographic and nonbusiness group.
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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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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".