Using authentic assessment in satisfying CEAB's new graduate attributes accreditation criteria
Bibliographic record
Abstract
“CEAB 2014” affects student assessment methods. There is a risk of an increased workload, as assessing for grading in a course and for the possession of attributes are “distinct matters”. Here is a rationale for assessment of learning outcomes, with an overview of methods and issues to consider when designing and using assessment of attributes in relation to CEAB’s accreditation criteria. To increase their performance, students need ‘educative assessments’, anchored in authentic tasks and with feedback usable to improve performance. At École Polytechnique de Montréal, a committee studies this issue, in both project modules and internships. The initiative aims to give students a more active role, engaging them in deep experiential learning. It faces the challenge to recommend effective and reliable assessment methods for student grading, also meeting the new requirements of CEAB with current resources. Association with other Canadian institutions is sought to continue the discussion.
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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 | 0.147 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".