MOTIVATION BEHIND INTERNATIONAL UNDERGRADUATE STUDENTS CHOOSING ENGINEERING
Bibliographic record
Abstract
This paper reports on a pilot study that investigated what motivated a group of first-year international students in the Vantage College program at the University of British Columbia (UBC) to pursue a degree in engineering. The study also sought to examine whether students report changes in their motivation as a result of completing their first year in our program. Data were collected through an open-ended survey provided to our cohort of 69 students, from which we received 66 responses. The results were analyzed qualitatively based on an expectancy value theoretical framework (focused on interest, utility, cost, and attainment.) The findings showed strong agreement with interest and utility as motivating factors, little agreement with attainment and cost as relevant factors, and the presence of additional motivators not present in our initial framework. The strongest among the latter group was family influence, with ability also appearing, yet to a lower degree. Our results suggest that interest and utility are the strongest motivators (over one third of students), with family influence (about one quarter) and ability (about one eight) being less important. We found few instances of cost (about one tenth) and no significant instances of attainment; this may be because engineering identity is developed as a student progresses through the undergraduate program.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.000 | 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".