Outcomes Assessment and Curriculum Improvement Through the Cyclical Review of Results - A Model to Satisfy CEAB-2009 Accreditation Requirements
Bibliographic record
Abstract
In Canada, engineering programs will soon have to
 show that: i. graduates possess specific attributes,
 ii. outcomes are assessed in their context, iii. results
 are used to improve the program. This paper presents
 a model to meet all three criteria, and provides
 curriculum control options to fulfil CEAB conditions.
 
 It defines proficiency levels and information flow,
 from stakeholders’ surveys to the fusion of data.
 Control modules, pre-graduation work experience and
 post-graduation reviews gather internal and external
 observations. Coverage of the CEAB attributes by the
 CDIO Syllabus is summarised. Its utility for meeting
 objectives about the 12 attributes is clarified. An XML
 tool upholds coherence between learning objectives
 and targeted proficiency levels. More than merely
 instructive, this model displays the characteristics of a
 convincing demonstration for the CEAB.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".