Experiences of Female Undergraduate Engineering Students in Academia and Industry: A Literature Review
Bibliographic record
Abstract
Higher educational settings have a slow rise of women in engineering programs. This literature review synthesizes existing research and has intentions to gain further understanding of female undergraduate engineering students—such as perceptions and experiences—on their respective university campus and internship programs through answering the below the main research: What is the nature of experience for female undergraduate engineering students on university campus and internship programs? This literature review provides important insights of needing support programs at early stages for students in their undergraduate education. Early interventions such as informal mentor relationships provide female students with stronger engineering identities for female engineering students. Ten research articles are examined in this literature review. The selection criteria emphasize on primary, peer reviewed articles that had to be in English with recent publications (earliest publication date of 2007) in the engineering or STEM field, the article revolved in higher education settings with students, faculty or staff as participants, article research objectives involve with gendered issues or mentorship programs and lastly, the articles involve with internship or academic support. Although the literature across were all in English, this literature review contains research from other countries—though mainly are from United States—Australia, England, Brazil, Spain and Kazakhstan. The variety of countries included provided consistencies that contextual support and supportive programs are crucial for fostering greater self-efficacy, persistency and resiliency for female undergraduate engineering 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".