Student Motivations for Choosing the Re-Engineered First-Year Program at the University of Saskatchewan
Bibliographic record
Abstract
As part of program evaluation and continuous improvement, the University of Saskatchewan deployed a survey across the common Re-Engineered First-Year (REFY) program in September 2022. Student answers to open-ended questions about their motivation to choose the REFY program were analyzed qualitatively using expectancy-value theory. This framework establishes four subjective task values that motivate behaviour (intrinsic, attainment, utility, and cost). Statistical analysis using logistic regression was then used to examine correlations between these motivators and student demographics. Results indicate costs specifically related to lack of final exams (24%) and costs unrelated to final exams (26%) were the most common motivators for choosing the REFY program, followed by intrinsic (17%) and utility (16%), with attainment (4%) being the lowest. The program features that students found most influential in their decision to join the REFY program included the opportunity to learn about different engineering disciplines, a lack of final exams, and competency-based assessment. Students also valued the program being designed to support them and to help them succeed; this program aspect is important to maintain because it strengthens motivation.
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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 | 0.005 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".