ASSESSING GRADUATE ATTRIBUTES AS DESCRIBED BY CEAB: AN EXPLORATORY STUDY IN A FIRST YEAR DESIGN COURSE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the fall of 2012, an experimental validation of the integration and assessment of six out of the twelve CEAB’s graduate attributes has been performed in the first year design course “GSC-1000 – Méthodologie de design en ingénierie”. Taking advantage of the authenticity of the learning context, and focusing on the necessity to develop just as authentic assessment tools, scoring rubrics have been extensively used in the assessment process. Automated data analysis algorithms have been embedded in the engineering faculty’s Intranet in order to facilitate the transition from the scoring rubric to a set of efficiently interpretable diagrams, supporting the assessor in its feedback delivery to learners. Results suggest that, at the beginning of the program, the study cohort presents an overall level of performance slightly below expectations in attributes 3.1.4 - Design, 3.1.6 - Individual and team work, and 3.1.9 - Impact of engineering on society and the environment, as expected for attribute 3.1.12 - Life-long learning, slightly above expectations for attribute 3.1.11 - Economics and project management, and dramatically below acceptability for attribute 3.1.7 - Communication skills.
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.
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 | 0.000 |
| 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 | 0.001 |
| 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 it