Institutional Learning Outcome Development for Academic Planning
Bibliographic record
Abstract
This professional administrative study created Institutional Learning Outcomes (ILOs) for a publicly funded postsecondary institution located in the southern part of Ontario, Canada. The university was created in the early 2000s and was the first publicly funded university in Ontario to open in over 40 years. The university shares a campus location with a college and was created with a special mission to provide career-oriented university programs and to provide opportunities for college graduates to complete a university degree. The university has been on a trajectory of immense growth. The focus to this point has been building from the ground up. As a result, the university has just begun to think about ways to better integrate strategic planning as the university enters the next phase in its development. This study was conducted with a focus on post-new public management using a public value approach. It examined and defined the strategic focus of the university in the next phase of development through the creation of ILOs. The ILOs were developed through qualitative interviews conducted with staff and faculty. To develop the ILOs, the participants were asked their opinions on the skills, values, and attributes they would like the institution to be known for as they prepare graduates for success in today’s world and the world of tomorrow. The data were analyzed using content analysis. This resulted in the creation of 4 ILOs delivered to the client organization in a governance report. The study’s findings may be used by the university administration to drive positive social change for students and the community by adopting the ILOs.
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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 | 0.049 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".