Approaches for evaluating learning outcomes in competency-based educational programs: a protocol for a scoping review
Bibliographic record
Abstract
Core competencies such as health knowledge, issues affecting humans, animals, plants, and the environment, principles of research, and program/policy evaluation are recommended for One Health programs in North America (Togami et al., 2018). Identifying appropriate methods to evaluate learning outcomes will indicate whether students are able to demonstrate the One Health core competencies that will equip them with the necessary skills to tackle complex challenges in Canada, such as climate change and infectious diseases. A scoping review of available methodologies for evaluating learning outcomes in competency-based educational programs will enable researchers at the University of Guelph to determine if a new framework should be drafted for evaluating One Health learning outcomes within the university, or whether existing frameworks can be tailored to best suit evaluation of the One Health learning outcomes at the University of Guelph, and ultimately in One Health programs at universities across Canada. \nObjectives: The objective of this protocol is to define the methods for a scoping review that will characterize available literature on methods used to evaluate learning outcomes in competency-based educational programs at the post-secondary level.
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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 | 0.215 | 0.207 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.063 | 0.018 |
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".