Wellness in Engineering Education: An Investigation into the Impact of Degree Plan Length and Its Association with Student Wellness
Bibliographic record
Abstract
Undergraduate engineering programs are associated with high stress and heavy workloads that impact the wellness of students. One university offers a unique undergraduate engineering education program structure offering two degree plan lengths, four or five years, with both involving the same number of courses. These options offer an opportunity for students to select the degree plan length they prefer. The purpose of this research is to explore the motivations for selecting degree plan length and how plan length may be associated with student wellness. An ethics-approved survey of 189 undergraduate students was conducted. Participants responded to a variety of questions that asked about their motivations for selecting their degree plan length and their state of wellness. Mann–Whitney U tests, thematic analyses, and chi-squared tests were used to analyze quantitative and qualitative responses. Results highlighted that there were statistically significant differences (p ≤ 0.001) between the motivators (4 of 8 factors) and perceived wellness (3 of 5 indicators) of students on the four- vs. five-year plans. Overall, it appears that each degree plan length may serve different student needs. Additionally, results suggest that the five-year plan offers the opportunity to reduce the workload and correlates with a better state of perceived wellness.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".