ASSESSING AND TRACKING THE FACTORS INFLUENCING STUDENT WELLBEING IN FIRST-YEAR ENGINEERING
Bibliographic record
Abstract
In recent years, there has been increasing awareness surrounding student wellbeing. The first year of university can be difficult due to the change in expectations and responsibilities associated with this transition. As many of the resources offered to support students are accessible on a voluntary basis, their effectiveness largely depends on their level of usage. Within the first year of the UBC Engineering program, we implemented four interventions looking at identifying potential challenges faced by students, and the resources they see as available. We based our work on a model that represents wellbeing as the balance between challenges and resources. We found that academics are a significant point of focus for students, with grades, second-year placement, and workload making 40% of the stressors throughout the academic year. In addition, discrepancies in academic background and the importance of having a routine were additional themes mentioned by students. COVID-19 was found to have a small impact on student wellbeing, most likely driven by the rapid transition away from student residences and uncertainty caused by the changes in the university’s grading policy. Students had difficulty finding specific resources that could support them throughout the year. Having more opportunities for studying with peers, and having access to what of upper-year students were requested by first-year students.
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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 | 0.001 |
| 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".