Identifying the Skills and Student Activities that Influence Career Pathways for Black vs. non- Black Engineering Graduates
Bibliographic record
Abstract
Abstract Background and Purpose The career options for engineering graduates have been increasing over the years with more and more employers from different industries wanting to hire folks with engineering degrees. Previous research has cited the rationale from an employers' perspective on why they chose to hire engineering graduates and what skills they are looking for, including technical skills/problem solving abilities. Less research has been done from the engineering graduates' perspective on what skills they have felt are most influential to their careers and how those perspectives may differ based on race. The purpose of this paper is to address the following research questions: What are the skills and activities that Black and non-Black engineering graduates have cited as being the most important to their careers? How do these skills and activities differ for Black vs non-Black engineering graduates? Design/Method A survey was developed leveraging previous survey instruments on engineering career paths, including the Pathways of Engineering Alumni Research Survey (PEARS) and the Troost ILead Career Path Survey. The survey included demographic questions as well as questions on the activities and skills that have been most influential in their career pathways. This survey was deployed to engineering graduates from various engineering programs across Canada, who had graduated at least 5 years prior. Black engineering graduates were deliberately oversampled in the survey deployment. Results Both Black and non-Black engineering graduates reported that Interpersonal and Communication skills were the most influential for their careers. When considering differences, Black graduates were more likely to state that Business/Financial Acumen and Self-Confidence were influential to their careers that non-Black graduates. When considering influential activities in undergrad, capstone/technical team projects and co-op/internships were cited most often as being influential activities to career pathways for both Black and non-Black engineering graduates. Black graduates cited co-curricular activities, mentorship, academic advising, participation and leadership in both engineering and non-engineering clubs, as well as technical team projects as being influential in statistically higher proportions than non-Black graduates. However, Black graduates cited research activities as being influential in statistically lower proportions than non-Black graduates. Contributions This research will help to support educators and administrators in determining which activities should be emphasized to support students in developing relevant skills for their careers. This can also help students determine which activities they may want to consider participating in to develop certain skills that they may want to use in their careers.
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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".