Institutional Resource Allocation, Student Engagement, and Student Satisfaction at Ontario Universities
Bibliographic record
Abstract
This study examined the relationship between institutional expenditures in student services, levels of student engagement, and measures of student satisfaction across 18 (out of 19) universities in the Province of Ontario, Canada. Information regarding these variables for each institution was assembled from four extant datasets: (a) the 2006 Common University Dataset of Ontario; (b) the 2006 COFO-UO Financial Report of Ontario Universities; (c) the 2006 National Survey of Student Engagement; and (d) the 2007 Toronto Globe and Mail University Report Card. Indices of student engagement included the benchmarks of academic challenge, active and collaborative learning, student interactions with faculty, enriching educational experiences, and supportive educational environments. The core research question pursued was whether higher per capita expenditures in student services predicted higher levels of student engagement and greater degrees of student satisfaction with various aspects of the university experience among graduates. Descriptive statistics were used to generate and compare demographic profiles of each university within a framework of institutional types. Among the universities sampled, eight were categorized as primarily undergraduate, five comprehensive, and five medical/doctoral institutions. Correlational techniques and hierarchical multiple regression analyses were applied to the data for purposes of assessing relationships between the sets of variables embedded in the research question. Correlational analyses yielded significant inverse relationships between per capita expenditures in student services and only select benchmarks of student engagement, relative to active and collaborative learning and student interactions with faculty. Strong positive correlations also were observed between multiple measures of student satisfaction and student engagement benchmarks of enriching educational experiences and supportive campus environments. Regression analyses did not sustain per capita expenditures in student services as a significant predictor of the student engagement or student satisfaction outcomes. However this study did find that select student engagement benchmarks, especially those items related to student-faculty interaction and supportive campus environments, predicted significantly measures of student satisfaction. Consideration was given to implications of these data for post-secondary policies and practices in Ontario, as well as suggestions for future research on the topic.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".