Learning Outcomes, Academic Credit and Student Mobility
Bibliographic record
Abstract
There is increasing interest in the use of learning outcomes in postsecondary education, and deliberations have surfaced with regard to their potential to serve as a tool for advancing credit transfer. Learning Outcomes, Academic Credit, and Student Mobility assesses the conceptual foundations, assumptions, and implications of using learning outcomes for the purposes of postsecondary credit transfer and student mobility. Through a critical review of current approaches to the use of learning outcomes across national and international jurisdictions, scholars and practitioners in postsecondary education provide a multivalent examination of their potential impacts in the unique context of Ontario and recommend future directions for the system. The collected works are the culmination of a multi-year study entitled Learning Outcomes for Transfer, funded by the Ontario Council on Articulation and Transfer. Contributions are authored by prominent international scholars across countries with significant outcomes-based experience and education reforms (South Africa, the United States, Australia, Europe, and the United Kingdom) and an Ontario research consortium comprising college and university experts working to advance student pathways.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".