STEM Experiences and Student Interest in Pursuing a Career in Engineering
Bibliographic record
Abstract
Of the Science, Technology, Engineering, and Mathematic (STEM) fields, women remain underrepresented in many engineering disciplines. Obstacles persist for entering the field via enrollment in an engineering program of study, but also for entering and remaining in an engineering profession. The push for more diversity and inclusion in STEM fields has led to many STEM enrichment programs, within and outside the school environment. These career-relevant learning experiences, both prior to and during university, influence students’ perceptions and attitudes towards engineering. According to Social Cognitive Career Theory, individuals choose to pursue a career based on their skills and their expectations of the career, which can be influenced by external factors, such as participation in career-relevant learning experiences. To this end, I study the effect of pre-university engineering experiences and university extracurricular experiences on undergraduate students’ perceptions of engineering and, ultimately, their decision to remain in the field. One hundred and ninety-four engineering students from Concordia University in Montreal, Canada responded to a survey about their pre-university and during-university career-relevant experiences, and their attitudes about the engineering profession. Results suggest that pre- and during-university engineering experiences are beneficial to engineering students, improving their self-efficacy and perceptions of the field. Engineering companies and educators may want to invest in these experiences if they want to support the next generation of future engineers and benefit from a more diverse workforce.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".