Engagement, Satisfaction, and Positive Student Outcomes: The Most Prevalent Factors at Canada's Public Universities
Bibliographic record
Abstract
Research suggests that higher levels of student engagement are positively correlated with higher levels of student satisfaction. The National Survey of Student Engagement (NSSE) benchmark measures have been found to be significantly correlated with institutional outcomes related to student satisfaction, such as graduation rates and retention. Although there has been an extensive amount of research conducted on ranking HEIs, student satisfaction, and student engagement, there remained a noticeable gap in the literature: the examination of the ranking of Canadian institutions’ student satisfaction, student engagement and positive student outcome variables. As such, we offer a novel study in the context of Canadian universities (N = 49) that examines Maclean’s magazine rankings of Canadian universities based on the analyses of data obtained from student satisfaction indices (as published by Maclean’s Magazine University Rankings) and NSSE (as reported by macleans.ca) concurrently to examine (1) if NSSE engagement indicators can predict Maclean’s student satisfaction at public institutions across Canada and (2) whether there are significant differences between (a) higher versus lower ranked universities, (b) universities with good reputations versus universities with poor reputations and (c) larger enrolment versus smaller enrolment universities. Canonical correlation analyses identified significant predictors of student satisfaction, although predictors differed based on academic year (i.e., first- versus senior-year students). The significant predictors were then utilized in Mann-Whitney U Tests for comparisons between universities. Results revealed that universities based on overall rank yielded the most difference followed by the size of institution, whereas there was little difference between universities based on their reputation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".