Promoting Innovation by Women through Engineering Entrepreneurship Courses: An Assessment of Entrepreneurial Self-Efficacy
Bibliographic record
Abstract
Engineering entrepreneurship education programs are increasingly exposing students to entrepreneurship and innovation. Little is known about student learning gains in these programs, particularly from a gender perspective. This study examines gender differences in students’ Entrepreneurial Self-efficacy (ESE) among students enrolled in a senior-level College of Engineering’s entrepreneurship practicum course. The ESE Scale was administered at the beginning and end of the semester to measure students’ self-efficacy for five ESE constructs – searching, planning, marshalling, implementing-people and implementing-finance. The findings reveal improvement in students searching, planning, marshalling, and implementing-finance constructs after the course. Significant gender differences were found for planning, marshalling, and implementing-finance constructs in students’ pre survey responses with female students reporting lower ESE. However, no significant gender differences were noted in students’ post survey responses. This indicates that female students were able to reach similar levels of ESE as male students as an outcome of instruction. These results demonstrate the positive impact of an entrepreneurship course on female students’ ESE and the importance of entrepreneurship programs for promoting innovation regardless of gender.
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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 | 0.002 | 0.004 |
| 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) | 0.003 | 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".