ASSESSING GRADUATE ATTRIBUTES AS DESCRIBED BY CEAB: AN EXPLORATORY STUDY IN A FIRST YEAR DESIGN COURSE
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.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".