ONE-YEAR OUTCOME-BASED ASSESSMENT AT RYERSON UNIVERSITY: LESSONS AND BEST PRACTICES
Bibliographic record
Abstract
A plan for assessing CEAB graduate attributes was executed on a pilot basis during 2010-2011 at Ryerson University. Based on the assessment results, improvements to the programs were recommended. The Faculty of Engineering, Architecture and Science (FEAS) at Ryerson University has eight engineering programs and seven science programs. The development of the CEAB assessment system was overseen by the FEAS Quality Assurance Council which includes several working groups. This paper presents the lessons and best practices gained during this one-year assessment. The best practices are related to the leadership structure, assessment elements, assessment methods, assessment data and results, and future program improvements. The presented best practices should be useful to the engineering programs that are planning to start or have already started the assessment process.
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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 | 0.084 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".