GRADUATE ATTRIBUTE MANAGEMENT: MAPPING UNIVERSITY GENERIC ATTRIBUTES TO THOSE OF THE CANADIAN ENGINEERING ACCREDITATION BOARD
Bibliographic record
Abstract
After 2009, accredited Canadian engineering schools began to develop processes to map the Canadian Engineering Accreditation Board-Graduate Attributes(CEAB-GAs) to their curriculum and integrate ways to measure them. At around the same time, several Canadian universities embarked on a process to identifytheir own University-wide Graduate Attribute (UGAs). UGAs are assumed to be applicable across study disciplines; i.e. the university experience as a whole.To address the need to assess students on the basis of the CEAB GAs and the UGAs in the Faculty of Engineering and develop the basis for an integrated graduate attribute management system, an interdisciplinary team conducted a yearlong qualitative study with the purpose of exploringthe intersection of the UGAs with CEAB-GAs. The key objectives were to develop a mapping process between the two sets and to explore management strategies for assessing both sets of graduate attributes. Two independent teams performed the mapping exercise using a sequential mixed methods study design. A qualitative exploratory mapping study was followed by a quantitative aggregation of the mapping results. Integration of the qualitative and quantitative study results was completed as part of the interpretation of the results. Both forward and backward mapping took place. Results demonstrated that, although generic, UGAs may not necessarily capture specific professional program graduate attributes such as the CEAB-GAs. The study also highlighted the need for more revisions and updates of UGAs by including various stakeholders who can substantially contribute to implementation and assessment of UGAs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".