Content. While we go bigger and beyonder, who holds down the fort for us? A perspective on modeling curricula to promote maintenance and strategic enhancement.
Bibliographic record
Abstract
As reflected by a growing consensus within the education community, content can only take our students so far (Deller et al., 2015). We now aim for discipline-specific as well discipline-independent higher order and transferable outcomes that promise to serve our students and scientific community bigger and better. While we renovate our courses and curricula to achieve these goals, how do we maintain curricular infrastructural integrity? How to implement these improvements in a manner that sustains curricular quality assurance, accountability, accessibility, and strategic spending?This session shares and reflects on a curricular modeling perspective that can hold down our curricular fort while we aim bigger and beyonder. It emphasizes international effort promoting the development of program-level learning outcomes (PLLOs) at the post secondary education level (Goff et al., 2015). It also extends the PLLO model to embrace discipline-specific and –independent higher order and transferable outcomes so that curricula can evolve nationally and internationally in a calculated and grounded manner.\nDeller, F., Brumwell , S., and MacFarlane, A. (2015). The Language of Learning Outcomes: Definitions and Assessments (Higher Education Quality Council of Ontario).\nGoff, L., Potter, M.K., Pierre, E., Carey, T., Gullage, A., Kustra, E., Lee, R., Lopes, V., Marshall, L., Martin, L., et al. (2015). Learning Outcomes Assessment: A practitioner's Handbook (Higher Education Quality Council of Ontario (HEQCO)).
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".