RECONCILING GRADUATE ATTRIBUTE ASSESSMENT WITH EXISTING OUTCOME-BASED ASSESSMENT
Bibliographic record
Abstract
Historically, accreditation of engineering programs has relied on the use of input-based assessment of a program by framing major categories and identifying accreditation unit totals for each category. Beginning in 2014, compliance with an outcomes-based assessment of program quality and implementation of a program improvement process is also required.The introduction of graduate attributes assessment at BCIT prompted faculty members to question the relationship between existing learning outcomes and indicators of graduate attributes. Since both outcomes and indicators are written to describe competencies, faculty hypothesized that correlation exists between them.Upon further investigation, faculty, staff, and administrators at BCIT came to understand that there is a relationship between learning outcomes and indicators of graduate attributes, but they are not synonymous. Indicators are required to build a normalizing bridge between outcomes and attributes. They provide a rational relationship between a curriculum’s individual course learning outcomes and the twelve graduate attributes mandated by the Canadian Engineering Accreditation Board.. This is especially important for subjective expectations of learning where there is not an obvious one-to-one relationship between learning outcomes and attributes
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.228 | 0.452 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.002 | 0.008 |
| 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".