Attracting And Retaining Females In Engineering Programs: Using An Stse Approach
Bibliographic record
Abstract
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Introduction There are great strides to be made in the recruitment of women to engineering programs and careers. While women typically make up more than 50% of individuals studying life science at most universities and colleges across North America, we see far fewer women in engineering programs than we do males. There is a considerable body of research that suggests ways to improve this, including mentorship programs, a change in the nature of the engineering workplace to accommodate family needs, and creating a more collaborative and less competitive atmosphere in both the academic and industry sides of engineering. Much of the literature on gender studies in science, technology and engineering suggests females enjoy and connect with these fields when they are placed within a human, social or environmental context. This paper demonstrates the why and how of this relationship, drawing ideas from gender roles and gender socialization. This paper looks at how moral development may impact a woman’s choice to pursue a career in the physical sciences, technology, engineering or math. In particular, the paper draws from Gilligan’s theories on females and the care-orientation of moral development, and how her theories demonstrate a need for a STSE (Science, Technology, Society and the Environment) orientation in high school, college and university curriculum. The extensive literature review in this paper is supplemented by qualitative data from 10 semi-structured interviews with female engineering students and recent female engineering graduates from a large engineering school in Canada. The subjects were interviewed individually, and came from a diverse set of academic and cultural backgrounds, engineering disciplines, interests and aspirations. The interviews were conducted in-person or via telephone, and were 30-45 minutes in duration. The interviews were structured around the following list of questions, however, the individuals interviewed were encouraged to share any thoughts on their experience, and some themes developed, and were encouraged, on an individual basis. • Why did you decide to pursue engineering? Do you feel that females have different reasons than males for pursuing engineering? • What were your first experiences with science and engineering as a youth? Which sciences were you most exposed to? • What were your most positive experiences in science and engineering prior to starting university? • Did you have any hesitation about pursuing engineering as a female? • What have been your most positive experiences, academically, as an engineering student? • What do you plan to do with your engineering degree? Do you think females have different goals than males? • Female numbers in engineering remain relatively low, and have recently been on the decline in Canada. Why do you think this is the case? How can we attract more women to the field of engineering? • Do you think there are stereotypes about engineering, or about women, that detract women from pursuing engineering? • Do you think the experience as a student is different for males and females? • If you could change something about your education as an engineer, what would it be?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".