Curriculum Management and CEAB Outcome Reporting: Recent Activities at the University of British Columbia
Bibliographic record
Abstract
Since 2010, the departments of Civil Engineering, and Electrical and Computer Engineering have partnered in the development of a program assessment protocol aimed at determining how well graduating students achieve independently created Program Learning Goals (PLGs). More recently, the departments are working together to prepare for CEAB Accreditation visits in 2014. This has been a fruitful partnership in part because of the very different undergraduate engineering programs offered by the two departments.This paper reports the curriculum management approach that has emerged from the collaboration between the twoengineering departments, data that has been collected to test hypothesized assessment protocols, and results from a pilot data collection process developed for CEAB outcomes and continual improvement purposes. The paper highlights the management approach, which is based on the conceptualization of the engineering programs as socio-curricular systems, the development of PLG indices, and results from the pilot CEAB outcomes reporting that involves the collection of triangulated data, i.e. the collection of data from the faculty perspective, the student perspective, and the perspective of the Professional Engineering community.
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 | 0.077 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".